Control tower and enterprise management platform for value chain networks

Abstract

A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

Description

CROSS-REFERNCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to the following U.S. Provisional Patent Applications: Serial No. 62 / 931,193, filed November 5, 2019, entitled “METHODS AND SYSTEMS OF VALUE CHAIN NETWORK MANAGEMENT PLATFORM;” Serial No. 62 / 969,153 filed February 3, 2020, entitled “METHODS AND SYSTEMS OF VALUE CHAIN NETWORK MANAGEMENT PLATFORM;” Serial No. 63 / 016,976 filed April 28, 2020, entitled “DIGITAL TWIN SYSTEMS AND METHODS FOR FACILITATING VALUE CHAIN NETWORKS AND LOGISTICS;” Serial No. 63 / 054,606 filed July 21, 2020, entitled “DIGITAL TWIN SYSTEMS AND METHODS FOR FACILITATING VALUE CHAIN NETWORKS AND LOGISTICS;” Serial No. 63 / 069,533, filed August 24, 2020, entitled “INFORMATION TECHNOLOGY SYSTEMS AND METHODS FOR VALUE CHAIN ARTIFICIAL INTELLIGENCE LEVERAGING DIGITAL TWINS;” and Serial No. 63 / 087,292, filed October 4, 2020, entitled “EXECUTIVE CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM FOR VALUE CHAIN NETWORK.” Each of the above applications is hereby incorporated by reference in its entirety as if fully set forth herein. FIELD

[0002] The present disclosure relates to information technology methods and systems for management of value chain network entities, including supply chain and demand management entities. The present disclosure also relates to the field of enterprise management platforms, more particularly involving data management, artificial intelligence, network connectivity and digital twins BACKGROUND

[0003] Historically, many of the various categories of goods purchased and used by household consumers, by businesses and by other customers were been supplied mainly through a relatively linear fashion, in which manufacturers and other suppliers of finished goods, components, and other items handed off items to shipping companies, freight forwarders and the like, who delivered them to warehouses for temporary storage, to retailers, where customers purchased them, or directly to customer locations. Manufacturers and retailers undertook various sales and marketing activities to encourage and meet demand by customers, including designing products, positioning them on shelves and in advertising, setting prices, and the like.

[0004] Orders for products were fulfilled by manufacturers through a supply chain, such as depicted in Fig. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order. The product 130 was passed through the supply chain, being conveyed and stored via various hauling facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112 and delivery systems 114, such as trucks and other vehicles, trains, and the like. In many cases, maritime facilities and infrastructure, such as ships, barges, docks and ports provided transport over waterways between the points of origin 102 and one or more destinations 104.

[0005] Organizations have access to an almost unlimited amount of data. With the advent of smart connected devices, wearable technologies, the Internet of Things (loT), and the like, the amount of data available to an organization that is planning, overseeing, managing and operating a value chain network has increased dramatically and will likely to continue to do so. For example, in a manufacturing facility, warehouse, campus, or other operating environment, there may be hundreds to thousands of loT sensors that provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors that can track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and many others. Additionally, as wearable technologies become more prevalent, wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers. Furthermore, as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets.

[0006] The presence of more data and data of new types offers many opportunities for organizations to achieve competitive advantages; however, it also presents problems, such as of complexity and volume, such that users can be overwhelmed, missing opportunities for insight. A need exists for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations. SUMMARY

[0007] According to some embodiments of the present disclosure, methods and systems are provided herein for an information technology system that may include a cloud-based management platform with a micro-services architecture; a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities; and a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use.

[0008] In embodiments, provided herein are methods, systems, components and other elements for an information technology system that may include a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces for accessing and configuring features of the platform; a set of network connectivity facilities for enabling a set of value chain network entities to connect to the platform; a set of adaptive intelligence facilities for automating a set of capabilities of the platform; a set of data storage facilities for storing data collected and handled by the platform; and a set of monitoring facilities for monitoring the value chain network entities; wherein the platform hosts a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

[0009] In embodiments, an information technology system, includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform; a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform; a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform; a set of data storage facilities that are configured to store data collected and handled by the platform, wherein the data is related to at least one of the value chain network entities and the features of the platform; and a set of monitoring facilities that are configured to monitor the value chain network entities; wherein the platform is configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

[0010] In embodiments, the set of interfaces includes at least one of a demand management interface and a supply chain management interface. In embodiments, the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence facilities includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of at least one value chain network entity of the value chain network entities controlled by the enterprise. In embodiments, the set of adaptive intelligence includes a smart contract system that is configured to automate a set of interactions among the value chain network entities.

[0011] In embodiments, the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses a graph database representing a set of hierarchical relationships of the value chain network entities. In embodiments, the set of monitoring facilities includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by the enterprise. In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications, and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.

[0012] In embodiments, the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones / robots / AVs, waterways, and port infrastructure facilities. In embodiments, the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

[0013] In embodiments, the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

[0014] In embodiments, the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

[0015] In embodiments, the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

[0016] In embodiments, the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0017] In embodiments, an information technology system, includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform.

[0018] In embodiments, the unified set of robotic process automation systems automate a process selected from the group consisting of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a set of products for compatibility, configuration of a request for proposals, ordering of equipment for a warehouse, ordering of equipment for a fulfillment center, classification of a product defect in an image, inspection of a product in an image, inspection of product quality data from a set of sensors, inspection of data from a set of onboard diagnostics on a. product, inspection of diagnostic data from an Internet of Things system, review of sensor data from environmental sensors in a set of supply chain environments, selection of inputs for a digital twin, selection of outputs from a digital twin, selection of visual elements for presentation in a digital twin, diagnosis of sources of delay in a supply chain, diagnosis of sources of scarcity in a supply chain, diagnosis of sources of congestion in a supply chain, diagnosis of sources of cost overruns in a supply chain, diagnosis of sources of product defects in a supply chain, and prediction of maintenance requirements in supply chain infrastructure.

[0019] In embodiments, one of the processes automated by the robotic process automation system involves selection of a quantity of product for an order. In embodiments, one of the processes automated by the robotic process automation system involves selection of a carrier for a shipment. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a vendor for a component. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a vendor for a finished goods order. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a variation of a product for marketing. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of an assortment of goods for a shelf. In embodiments, wherein one of the processes automated by the robotic process automation system involves determination of a price for a finished good.

[0020] In embodiments, one of the processes automated by the robotic process automation system involves configuration of a service offer related to a product. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product bundle. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product kit. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product package. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product display. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product image. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product description. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a website navigation path related to a product.

[0021] In embodiments, one of the processes automated by the robotic process automation system involves determination of an inventory level for a product. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a logistics type. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a schedule for product delivery. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a logistics schedule. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a set of inputs for machine learning. In embodiments, one of the processes automated by the robotic process automation system involves preparation of product documentation. In embodiments, one of the processes automated by the robotic process automation system involves preparation of disclosures about a product. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a product for a set of local requirements. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a set of products for compatibility. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a request for proposals. In embodiments, one of the processes automated by the robotic process automation system involves ordering of equipment for a warehouse. In embodiments, one of the processes automated by the robotic process automation system involves ordering of equipment for a fulfillment center. In embodiments, one of the processes automated by the robotic process automation system involves classification of a product defect in an image. In embodiments, one of the processes automated by the robotic process automation system involves inspection of a product in an image. In embodiments, one of the processes automated by the robotic process automation system involves inspection of product quality data from a set of sensors. In embodiments, one of the processes automated by the robotic process automation system involves inspection of data from a set of onboard diagnostics on a. product.

[0022] In embodiments, one of the processes automated by the robotic process automation system involves inspection of diagnostic data from an Internet of Things system. In embodiments, one of the processes automated by the robotic process automation system involves review of sensor data from environmental sensors in a set of supply chain environments. In embodiments, one of the processes automated by the robotic process automation system involves selection of inputs for a digital twin. In embodiments, one of the processes automated by the robotic process automation system involves selection of outputs from a digital twin. In embodiments, one of the processes automated by the robotic process automation system involves selection of visual elements for presentation in a digital twin. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of delay in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of scarcity in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of congestion in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of cost overruns in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of product defects in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves prediction of maintenance requirements in supply chain infrastructure.

[0023] In embodiments, the set of demand management applications, supply chain applications, intelligent product applications and enterprise resource management applications are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0024] In embodiments, the set of interfaces includes at least one of a demand management interface and a supply chain management interface. In embodiments, the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence facilities includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a smart contract system that is configured to automate a set of interactions among a set of value chain network entities.

[0025] In embodiments, the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses graph database representing a set of hierarchical relationships of the value chain network entities. In embodiments, the set of monitoring facilities includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise. In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.

[0026] In embodiments, the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones / robots / AVs, waterways, and port infrastructure facilities.

[0027] In embodiments, wherein the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

[0028] In embodiments, the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

[0029] In embodiments, the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

[0030] In embodiments, the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

[0031] In embodiments, the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0032] In embodiments, an information technology system, includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application, wherein the microservices layers include a data collection layer that collects information from a set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities related to the value chain network entities of the platform.

[0033] In embodiments, the set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities collects information from entities selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones / robots / AVs, waterways, and port infrastructure facilities.

[0034] In embodiments, the set of Internet of Things resources is selected from the group consisting of camera systems, lighting systems, motion sensing systems, weighing systems, inspection systems, machine vision systems, environmental sensor systems, onboard sensor systems, onboard diagnostic systems, environmental control systems, sensor-enabled network switching and routing systems, RF sensing systems, magnetic sensing systems, pressure monitoring systems, vibration monitoring systems, temperature monitoring systems, heat flow monitoring systems, biological measurement systems, chemical measurement systems, ultrasonic monitoring systems, radiography systems, LIDAR-based monitoring systems, access control systems, penetrating wave sensing systems, SONAR-based monitoring systems, radar-based monitoring systems, computed tomography systems, magnetic resonance imaging systems, and network monitoring systems.

[0035] In embodiments, the set of Internet of Things resources includes a set of camera systems. In embodiments, the set of Internet of Things resources includes a set of lighting systems. In embodiments, the set of Internet of Things resources includes a set of machine vision systems. In embodiments, the set of Internet of Things resources includes a set of motion sensing systems.

[0036] In embodiments, the set of Internet of Things resources includes a set of weighing systems. In embodiments, the set of Internet of Things resources includes a set of inspection systems. In embodiments, the set of Internet of Things resources includes a set of environmental sensor systems. In embodiments, the set of Internet of Things resources includes a set of onboard sensor systems. In embodiments, the set of Internet of Things resources includes a set of onboard diagnostic systems. In embodiments, the set of Internet of Things resources includes a set of environmental control systems. In embodiments, the set of Internet of Things resources includes a set of sensor-enabled network switching and routing systems. In embodiments, the set of Internet of Things resources includes a set of RF sensing systems.

[0037] In embodiments, the set of Internet of Things resources includes a set of magnetic sensing systems. In embodiments, the set of Internet of Things resources includes a set of pressure monitoring systems. In embodiments, the set of Internet of Things resources includes a set of vibration monitoring systems. In embodiments, the set of Internet of Things resources includes a set of temperature monitoring systems. In embodiments, the set of Internet of Things resources includes a set of heat flow monitoring systems. In embodiments, the set of Internet of Things resources includes a set of biological measurement systems. In embodiments, the set of Internet of Things resources includes a set of chemical measurement systems. In embodiments, the set of Internet of Things resources includes a set of ultrasonic monitoring systems. In embodiments, the set of Internet of Things resources includes a set of radiography systems. In embodiments, the set of Internet of Things resources includes a set of LIDAR-based monitoring systems. In embodiments, the set of Internet of Things resources includes a set of access control systems. In embodiments, the set of Internet of Things resources includes a set of penetrating wave sensing systems. In embodiments, the set of Internet of Things resources includes a set of SONAR-based monitoring systems. In embodiments, the set of Internet of Things resources includes a set of radarbased monitoring systems. In embodiments, the set of Internet of Things resources includes a set of computed tomography systems. In embodiments, the set of Internet of Things resources includes a set of magnetic resonance imaging systems. In embodiments, the set of Internet of Things resources includes a set of network monitoring systems. In embodiments, the set of interfaces includes at least one of a demand management interface and a supply chain management interface.

[0038] In embodiments, the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0039] In embodiments, the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a smart contract system that is configured to automate a set of interactions among a set of value chain network entities. In embodiments, the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities. In embodiments, the set of monitoring includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise. In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.

[0040] In embodiments, the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones / robots / AVs, waterways, and port infrastructure facilities.

[0041] In embodiments, the platform manages a set of demand factors, a set of supply factors and a set of supply chain infrastructure facilities.

[0042] In embodiments, the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

[0043] In embodiments, the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

[0044] In embodiments, the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

[0045] In embodiments, the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0046] In embodiments, an information technology system, includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application, wherein the microservices layers include a robotic process automation layer that uses information collected by a data collection layer and a set of outcomes and activities involving the applications of the application layer to automate a set of actions for at least a subset of the applications with respect to the value chain network entities of the platform.

[0047] In embodiments, the robotic process automation layer automates a process selected from the group consisting of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a set of products for compatibility, configuration of a request for proposals, ordering of equipment for a warehouse, ordering of equipment for a fulfillment center, classification of a product defect in an image, inspection of a product in an image, inspection of product quality data from a set of sensors, inspection of data from a set of onboard diagnostics on a. product, inspection of diagnostic data from an Internet of Things system, review of sensor data from environmental sensors in a set of supply chain environments, selection of inputs for a digital twin, selection of outputs from a digital twin, selection of visual elements for presentation in a digital twin, diagnosis of sources of delay in a supply chain, diagnosis of sources of scarcity in a supply chain, diagnosis of sources of congestion in a supply chain, diagnosis of sources of cost overruns in a supply chain, diagnosis of sources of product defects in a supply chain, and prediction of maintenance requirements in supply chain infrastructure.

[0048] In embodiments, one of the actions automated by the robotic process automation layer involves selection of a quantity of product for an order. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a carrier for a shipment. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a vendor for a component. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a vendor for a finished goods order. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a variation of a product for marketing. In embodiments, one of the actions automated by the robotic process automation layer involves selection of an assortment of goods for a shelf. In embodiments, one of the actions automated by the robotic process automation layer involves determination of a price for a finished good. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a service offer related to a product. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of product bundle. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product kit. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product package. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product display. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product image. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product description. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a website navigation path related to a product. In embodiments, one of the actions automated by the robotic process automation layer involves determination of an inventory level for a product. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a logistics type. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a schedule for product delivery. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a logistics schedule. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a set of inputs for machine learning. In embodiments, one of the actions automated by the robotic process automation layer involves preparation of product documentation. In embodiments, one of the actions automated by the robotic process automation layer involves preparation of disclosures about a product. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product for a set of local requirements. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a set of products for compatibility. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a request for proposals. In embodiments, one of the actions automated by the robotic process automation layer involves ordering of equipment for a warehouse. In embodiments, one of the actions automated by the robotic process automation layer involves ordering of equipment for a fulfillment center. In embodiments, one of the actions automated by the robotic process automation layer involves classification of a product defect in an image. In embodiments, one of the actions automated by the robotic process automation layer involves inspection of a product in an image. In embodiments, one of the actions automated by the robotic process automation layer involves inspection of product quality data from a set of sensors. In embodiments, one of the actions automated by the robotic process automation layer involves inspection of data from a set of onboard diagnostics on a. product. In embodiments, one of the actions automated by the robotic process automation layer involves inspection of diagnostic data from an Internet of Things system. In embodiments, one of the actions automated by the robotic process automation layer involves review of sensor data from environmental sensors in a set of supply chain environments. In embodiments, one of the actions automated by the robotic process automation layer involves selection of inputs for a digital twin. In embodiments, one of the actions automated by the robotic process automation layer involves selection of outputs from a digital twin. In embodiments, one of the actions automated by the robotic process automation layer involves selection of visual elements for presentation in a digital twin. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of delay in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of scarcity in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of congestion in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of cost overruns in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of product defects in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves prediction of maintenance requirements in supply chain infrastructure.

[0049] In embodiments, the set of interfaces includes at least one of a demand management interface and a supply chain management interface.

[0050] In embodiments, the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, selfconfiguration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0051] In embodiments, the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence facilities includes a selfconfiguring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a smart contract system for automating a set of interactions among a set of value chain network entities. In embodiments, the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities. In embodiments, the set of monitoring facilities includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.

[0052] In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.

[0053] In embodiments, the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones / robots / AVs, waterways, and port infrastructure facilities.

[0054] In embodiments, the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

[0055] In embodiments, the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

[0056] In embodiments, the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

[0057] In embodiments, the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

[0058] In embodiments, the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0059] In embodiments, an information technology system, includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a machine leaming / artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and / or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.

[0060] In embodiments, the set of interfaces includes at least one of a demand management interface and a supply chain management interface. In embodiments, the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0061] In embodiments, the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence facilities includes a selfconfiguring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a smart contract system for automating a set of interactions among a set of value chain network entities. In embodiments, the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities. In embodiments, the set of monitoring facilities includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise. In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.

[0062] In embodiments, the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones / robots / AVs, waterways, and port infrastructure facilities.

[0063] In embodiments, the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

[0064] In embodiments, the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

[0065] In embodiments, the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

[0066] In embodiments, the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

[0067] In embodiments, the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

[0068] In embodiments, a value chain system that provides container fleet management decisions includes a machine learning system that trains a machine-learned model that outputs a container fleet management decision given a respective set of input features relating to a specific shipping event, wherein the machine learning system trains the machine-learned model based on training data sets that define features of previous shipping events and outcomes of the shipping events; an artificial intelligence system that receives a request for container fleet management and determines a container fleet management decision based on the machine-learned model and the request; and a digital twin system that generates an environment digital twin of an environment of a container fleet and one or more container digital twins of respective containers in the container fleet, wherein the digital twin system executes a container fleet simulation based on the environment digital twin and the one or more container digital twins, issues a container fleet management request from the artificial intelligence system based on a state of the container fleet simulation; and adjusts the state of the container fleet simulation based on the container fleet management decision output by the artificial intelligence system in response to the container fleet management request.

[0069] In embodiments, the digital twin system outputs a simulation outcome to the machinelearning system, and the machine learning system reinforces the machine-learned model used to determine the container fleet management decision based on the simulation outcome. In embodiments, the artificial intelligence system receives the container fleet management request from the digital twin system and determines the container fleet management decision based on simulation features defined in the container fleet management request, wherein the simulation features are indicative of the state of the container fleet simulation. In embodiments, the request for container fleet management includes one or more properties of a simulated shipping event. In embodiments, the artificial intelligence system determines the container fleet management decision based on the one or more properties of the simulated shipping event and the machine-learned model. In embodiments, the one or more properties include a type of good being shipped. In embodiments, the one or more properties include a source and a destination of a container. In embodiments, the digital twin system provides outcome data to the machine-learning system, wherein the outcome data defines a simulation outcome resulting from the container fleet management decision.

[0070] In embodiments, a value chain system that provides recommendations for designing a logistics system includes a machine learning system that trains a machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the machine-learned model based on training data sets that define features of logistics systems and outcomes of the logistics systems; an artificial intelligence system that receives a request for logistics system design and determines a logistics system design recommendation based on the machine-learned model and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation and one or more physical asset digital twins of physical assets, wherein the digital twin system: executes a logistics simulation based on the logistics environment digital twin and the one or more physical asset digital twins, issues a logistics system design request from the artificial intelligence system based on a state of the logistics simulation; and adjusts the state of the logistics simulation based on the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request.

[0071] In embodiments, the digital twin system outputs a graphical representation of the environment digital twin to a display, whereby a user views the simulation via the display. In embodiments, the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the logistics system design recommendation based on the simulation outcome. In embodiments, the artificial intelligence system receives the request from a logistics design system that designs logistics systems, wherein the request includes one or more logistics factors corresponding to a proposed logistics solution of an organization. In embodiments, the logistics factors include one or more of: a type of product corresponding to the proposed logistics solution, one or more features of the type of product, a location of a manufacturing site, a location of a distribution facility, a location of a warehouse, a location of a customer base, proposed expansion areas of the organization, and supply chain features. In embodiments, the logistics design system provides outcome data relating to the logistics system design recommendation to the machine learning system, and the machine learning system reinforces the machine-learned model that are used to determine the logistics system design recommendation based on the outcome data. In embodiments, the artificial intelligence system determines the logistics system design recommendation to minimize delay times. In embodiments, the artificial intelligence system determines the logistics system design recommendation to comply with regulatory requirements.

[0072] In embodiments, a value chain system that designs packaging includes a machine learning system that trains a machine-learned model that outputs a packaging design recommendation given a respective set of input features relating to a specific respective packaging design, wherein the machine learning system trains the machine-learned model based on training data sets that define features of packaging designs and outcomes of the packaging designs; an artificial intelligence system that receives a request for packaging design and determines a packaging design recommendation based on the machine-learned model and the request; and a digital twin system that generates a package digital twin of a package that incorporates the packaging design recommendation, wherein the digital twin system: executes a packaging simulation based on the package digital twin; issues a packaging design request from the artificial intelligence system based on a state of the logistics simulation; and adjusts the state of the logistics simulation based on the packaging design recommendation output by the artificial intelligence system in response to the packaging design request.

[0073] In embodiments, the digital twin system outputs a graphical representation of the package digital twin to a display, whereby a user views the simulation via the display. In embodiments, the digital twin system outputs a graphical representation of the package digital twin in a graphical user interface, whereby a user edits the packaging design via the graphical user interface. In embodiments, the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the packaging design recommendation based on the simulation outcome. In embodiments, the artificial intelligence system receives the request from a packaging design system that designs packaging for physical objects, wherein the request includes one or more packaging factors corresponding to a proposed packaging design for the physical objects. In embodiments, the packaging factors include one or more of: a type of the physical objects, dimensions of the physical objects, masses of the physical objects, and shipping methods of the physical objects. In embodiments, the packaging design system provides outcome data relating to the packaging design recommendation to the machine learning system, and the machine learning system reinforces the machine-learned model that are used to determine the packaging design recommendation based on the outcome data. In embodiments, the artificial intelligence system determines the packaging design recommendation to minimize damage. In embodiments, the artificial intelligence system determines the packaging design recommendation to minimize costs. In embodiments, the artificial intelligence system determines the packaging design recommendation to mitigate environmental impact.

[0074] In embodiments, an information technology system for leveraging digital twins in a value chain having a plurality of value chain entities, the information technology system includes a plurality of sensors positioned at least one of in, on, and near a set of value chain entities of the value chain entities and configured to collect sensor data related to the set of value chain entities, the sensor data being substantially real-time sensor data; and an adaptive intelligence system connected to the plurality of sensors and configured to receive the sensor data from the plurality of sensors, the adaptive intelligence system including: an artificial intelligence system configured to input the sensor data into a machine learning model such that the sensor data is used as training data for the machine learning model, and the machine learning model is configured to transform the sensor data into simulation data; and a digital twin system configured to create a digital replica of the set of value chain entities based on the simulation data, wherein the digital replica of the value chain entities is configured to be used to provide a substantially real-time representation of the value chain entities and provide a simulation of a possible future state of the value chain entities via the simulation data.

[0075] In embodiments, the machine learning model is configured to learn which types of sensor data are relevant to dynamics of each value chain entity of the value chain entities and simulation thereof. In embodiments, the machine learning model is configured to make suggestions to a user of the information technology system via an interface regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system. In embodiments, the machine learning model is configured to prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof.

[0076] In embodiments, a value chain network management platform, includes a machine learning system that trains one or more machine-learned models to output one or more e-commerce recommendations to a value chain network customer via an interface using training data that includes product features and outcomes; and an artificial intelligence system that receives a request for e-commerce from an e-commerce system, wherein the artificial intelligence is configured to determine and generate an e-commerce recommendation based on the one or more machine-learned models and the request, and the artificial intelligence is configured to leverage one or more product digital twins and one or more customer digital twins to execute a simulation based on the one or more customer digital twins, the one or more product digital twins, and the e-commerce recommendation.

[0077] In embodiments, the machine learning system integrates with a model interpretability system, and wherein the model interpretability system is configured to implement Testing with Concept Activation Vectors (TCAV) functionality, whereby the model interpretability facilitates learning of human-interpretable concepts by the machine-learned model. In embodiments, the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more customer profile digital twins.

[0078] In embodiments, a value chain network management platform includes a machine learning system that trains one or more machine-learned models to output one or more risk management decisions using training data that includes component features and outcomes; and an artificial intelligence system that receives a request for risk management from a risk management system, wherein the artificial intelligence system is configured to determine and generate a risk management decision based on the one or more machine-learned models and the request, and the artificial intelligence system is configured to leverage one or more component digital twins and one or more environment digital twins to execute a simulation based on the one or more component digital twins, the one or more environment digital twins, and the risk management decision.

[0079] In embodiments, the risk management decision relates to a condition of a component. In embodiments, the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more components.

[0080] In embodiments, an information technology system includes a value chain network management platform having an asset management application associated with maritime assets, wherein the platform comprises a data handling layer including data sources containing information used to populate a training set based on a set of maritime activities of one or more of the maritime assets and at least one of design outcomes, parameters, and data associated with the one or more of the maritime assets; an artificial intelligence system that is configured to leam on the training set collected from the data sources, wherein the artificial intelligence system is configured to simulate one or more attributes of the one or more of the maritime assets, and the artificial intelligence system is configured to generate one or more sets of recommendations for a change in the one or more attributes based on the training set collected from the data sources; a digital twin system that is configured to provide for visualization of a digital twin of the one or more of the maritime assets including detail generated by the artificial intelligence system of the one or more attributes in combination with the one or more generated sets of recommendations.

[0081] In embodiments, the maritime assets include one or more container ships, and wherein the digital twin system further provides for visualization of the digital twin of the one or more container ships including the one or more attributes in combination with one or more of the sets of recommendations associated with the container ships. In embodiments, the maritime assets include one or more barges, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the barges including the one or more attributes in combination with one or more of the sets of recommendations associated with the barges. In embodiments, the maritime assets include one or more components of a port infrastructure installed on or adjacent to land, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of port infrastructure including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of port infrastructure. In embodiments, the maritime assets also include a container ship moored to a component of the port infrastructure. In embodiments, the maritime assets include one or more moored navigation units deployed on water. In embodiments, the maritime assets include one or more ships each connected to a barge. In embodiments, the maritime assets are associated with a real-world maritime port, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of the real-world maritime port including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of the real-world maritime port. In embodiments, the maritime assets are associated with a real-world shipyard, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of the real-world shipyard including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of the real-world shipyard.

[0082] In embodiments, the digital twin of one or more of the maritime assets is a floating asset twin associated with a ship. In embodiments, the floating asset twin is configured to provide for visualization of a navigation course of the ship relative to a planned course of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the navigation course of the ship. In embodiments, the floating asset twin is configured to provide for visualization of an engine performance of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the engine performance of the ship. In embodiments, the visualization of the engine performance includes an emissions profile of the ship. In embodiments, the floating asset twin is configured to provide for visualization of a hull integrity of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in maintenance of the hull of the ship. In embodiments, the floating asset twin is configured to provide for visualization of in-situ hydrodynamic changes to a portion of a hull disposed below a water line of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in a hydrodynamic surface to change performance of the ship. In embodiments, the floating asset twin is configured to determine a schedule for the change to the hydrodynamic surface of the hull disposed below the waterline of the ship to improve fuel efficiency based on known routes of travel and weather patterns.

[0083] In embodiments, the floating asset twin is configured to provide visualizations of in-situ aerodynamic changes to a portion of a hull disposed above a water line of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in an aerodynamic surface to change performance of the ship. In embodiments, the floating asset twin is configured to determine a schedule for the change to the aerodynamic surface disposed above the waterline of the ship to improve fuel efficiency using known routes of travel and historical weather patterns. In embodiments, the floating asset twin is configured to provide visualizations of extendable buoyant members from a hull of the ship to improve stability during certain maneuvers of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the extendable buoyant members to change performance of the ship. In embodiments, the floating asset twin is configured to provide visualizations of a plurality of inspection points on the ship and maintenance histories associated with those inspection points. In embodiments, the floating asset twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change in maintenance of the plurality of inspection points. In embodiments, the floating asset twin is further configured to provide for visualizations of the plurality of inspection points on the ship affected by travel within a geofenced area and maintenance histories associated with those inspection points. In embodiments, the floating asset twin is further configured to provide details of a ledger of activity associated with the visualization of the plurality of inspection points on the ship affected by travel within a geofenced area and maintenance histories associated with those inspection points. In embodiments, the floating asset twin is configured to provide for visualization for a first user of one of a navigation course of the ship and an engine performance of the ship within a first geofenced area and for visualization for a second user of one of the navigation course of the ship and the engine performance of the ship within a second different geofenced area and where transit between the first and second geofenced areas motivates a handoff of the floating asset twin of the ship between the first user and the second user.

[0084] In embodiments, the digital twin is configured to at least partially represent one or more of the maritime assets associated with an event investigation and to at least partially detail a timeline of the event investigation and the associated maritime assets. In embodiments, the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets based on the event investigation and the timeline. In embodiments, the digital twin is configured to at least partially represent one or more of the maritime assets associated with a legal proceeding and to at least partially detail at least a portion of a timeline pertinent to the legal proceeding and the associated maritime assets. In embodiments, the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets based on the legal proceeding and the timeline. In embodiments, the digital twin is configured to at least partially represent one or more of the maritime assets associated with at least one of a casualty forecast and a casualty report, and to at least partially detail at least a portion of a timeline pertinent to the at least one of the casualty forecast, the casualty report, and the associated maritime assets. In embodiments, the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets to reduce exposure relative to a set of previous casualty forecasts based on at least one of the casualty forecast and the casualty report, and the timeline. In embodiments, the maritime assets include a port infrastructure facility, wherein the data collected by a value chain network management platform facilitates identifying theft at or misuse of the port infrastructure facility by correlating data between a set of data collectors for one or more physical items in the port infrastructure facility and the digital twin detailing the one or more physical items of the port infrastructure facility for the at least one of the port infrastructure facility and a set of operators. In embodiments, the digital twin details the one or more physical items of the port infrastructure facility for at least one operator that includes a view of expected states of at least a portion of the one or more physical items. In embodiments, the maritime assets include a shipyard, wherein the data collected by a value chain network management platform facilitates identifying theft at or misuse of one or more physical items in the shipyard by correlating data between a set of data collectors for the one or more physical items and the digital twin detailing the one or more physical items of the shipyard for the at least one of the shipyard and a set of operators. In embodiments, the digital twin details the one or more physical items of the shipyard for at least one operator that includes a view of expected states of at least a portion of the one or more physical items. In embodiments, the artificial intelligence system determines a set of geofence parameters, and wherein the digital twin provides further visualization of at least one geofence that integrates representation of a set of the maritime assets with a representation of a maritime environment adjacent to the geofence. In embodiments, the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the set of maritime assets based on the visualization of the at least one geofence. In embodiments, the maritime assets are ships capable of carrying cargo, wherein the artificial intelligence system determines a set of geofence parameters, and wherein the digital twin provides further visualization of at least one geofence that integrates representation of the ships capable of carrying cargo with a representation of a maritime environment. In embodiments, the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the ships capable of carrying cargo based on the visualization of the at least one geofence.

[0085] In embodiments, an information technology system having a management platform includes a user interface that provides a set of adaptive intelligence systems that provide coordinated artificial intelligence for a set of demand management applications and a set of supply chain applications for a category of goods by determining relationships among demand management and supply chain applications based on inputs used by the applications and results produced by the applications; and a set of artificial intelligence systems as part of the set of adaptive intelligence systems that provide coordinated intelligence for the set of demand management applications and the set of supply chain applications for the category of goods by determining a temporal prioritization of demand management application outputs that impact control of supply chain applications so as to meet a temporal demand for at least one of the goods in the category of goods.

[0086] In embodiments, the adaptive intelligence system facilitates coordinated artificial intelligence for the set of demand management applications or the set of supply chain applications, or both for a category of goods by processing data that is available in any of a plurality of data sources including processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System. In embodiments, the set of adaptive intelligence systems provide user access to coordinated artificial intelligence capabilities for use with the sets of applications. In embodiments, the user interface presents a set of coordinated artificial intelligence capabilities responsive to the category of goods. In embodiments, the user interface facilitates configuring the set of adaptive intelligence systems with at least one artificial intelligence system. In embodiments, the at least one artificial intelligence system is a hybrid artificial intelligence system. In embodiments, the at least one artificial intelligence system comprises a hybrid neural network. In embodiments, the set of adaptive intelligence systems that provide coordinated artificial intelligence operates on or responsive to data collected by or produced by other systems of an adaptive intelligence systems layer. In embodiments, the set of adaptive intelligence systems that provide coordinated artificial intelligence provides coordinated intelligence for a specific operator and / or enterprise that participates in the supply chain for the category of goods. In embodiments, the set of adaptive intelligence systems that provide coordinated artificial intelligence employs a neural network that processes at least one of demand management application outputs and supply chain application outputs to provide the coordinated intelligence.

[0087] In embodiments, the set of adaptive intelligence systems that provide coordinated artificial intelligence is configured through the user interface for at least two demand management applications selected from the list consisting of a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or servicetargeting application, a collaborative filtering application, a recommendation engine for a product or service

[0088] In embodiments, the set of adaptive intelligence systems that provide coordinated artificial intelligence is configured through the user interface for at least two supply chain applications selected from the list consisting of a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, and an order for components management application. In embodiments, the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management. In embodiments, an artificial intelligence system of the adaptive intelligence systems layer operates on or responsive to data collected by or produced by other systems of the adaptive intelligence systems layer. In embodiments, a set of artificial intelligence systems may provide coordinated intelligence for a specific operator and / or enterprise that participates in the supply chain for the category of goods. In embodiments, the coordinated intelligence includes a portion of a set of artificial intelligence systems that employs a neural network that processes at least one of demand management application outputs and supply chain application outputs to provide the coordinated intelligence.

[0089] In embodiments, the demand management applications include at least two of a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service.

[0090] In embodiments, the supply chain applications include at least two of a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, and an order for components management application.

[0091] In embodiments, an artificial intelligence system facilitates coordinated intelligence for the sets of applications by processing data that is available in any of a plurality of data sources including processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System.

[0092] In embodiments, the set of adaptive intelligence systems are configured in a topology that facilitates shared adaptation capabilities among at least two adaptive intelligence systems in the set of adaptive intelligence systems. In embodiments, the set of adaptive intelligence systems employ artificial intelligence to provision available network resources for both the set of demand management applications and for the set of supply chain applications. In embodiments, the set of demand management applications comprises a demand planning application. In embodiments, the set of adaptive intelligence systems employ artificial intelligence to improve at least one of the list of outputs consisting of a process output, an application output, a process outcome and an application outcome.

[0093] One path to distilling information is digital twin technology, which can present large amounts of data in a digestible format that represents salient characteristics of an item, often updated in real time or near real time as the twin is updated to reflect the current state based on a pipeline of data about a represented item. While this is helpful, current digital twin technology has its limitations due to the fact that different roles within an organization may require different information to draw their insights. For example, a CEO of an industrial facility makes decisions based on a “10,000 foot view” of the company. The CEO may review profit and loss (P&L) data, industry trends, and employee trends (e.g., employee satisfaction or employee retention rates) to make overall decisions on behalf of the organization but does not necessarily need to see the granular data points to make decisions. In contrast, a different user, such as a CFO, may require more granular information, such as sales figures by region, marketing costs, maintenance costs, depreciation information, human capital costs, and costs of third-party vendors to draw her conclusions, but may not be as concerned with employee or industry trends. Similarly, a CTO may have no need for P&L data but may require an in-depth visualization of the processes within different manufacturing facilities to gain a better understanding of opportunities to improve process outcomes or to diagnose issues within processes, equipment or systems. Thus, a need exists for digital twins and other interfaces that are configured for particular roles.

[0094] As a further challenge, a given role may have varying needs based on context. For example, while the CEO might focus on higher-level data for many activities, such as strategic decision making or board communications, the same CEO may find more granular, micro-scale data useful for other activities, such as when an issue is escalated from a subdivision of the organization for input. Thus, a need exists for context-adaptive digital twins for each role, including ones that provide relevant displays and information of the right type at the right time for various situations and activities undertaken by the role.

[0095] More generally, ubiquitous connectivity and the proliferation of larger and larger data sets offers enterprise leaders opportunities for an unprecedented degree of awareness and control over enterprise assets and activities. A need and opportunity exist for an enterprise control tower by which executive leaders can, through various interfaces, including executive digital twins, dashboards, and similar systems, obtain timely information that is curated to invoke relevant awareness, support effective decisions and enable operational control.

[0096] According to some embodiments of the present disclosure, an enterprise management platform is disclosed. In some embodiments, the enterprise management platform integrates a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including Al-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an enterprise, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.

[0097] Further provided herein are methods and systems for enterprise control towers by which executive leaders can, through various interfaces, including executive digital twins, dashboards, and similar systems, obtain timely information (often in real-time or near real-time) that is curated to invoke relevant awareness, support effective decisions and enable operational control. The present disclosure further relates to an executive control tower and enterprise management platform that is configured to provide and use a converged technology stack that includes intelligent sensing and data collection, curation and handling of data through various stages of a distributed storage, networking and connectivity pipeline (from a set of local operational environments through information technology networks to various distributed on-premises and cloud computing environments), and deployment of various application-specific and general artificial intelligence capabilities in order to enable executive control towers, including rolespecific executive digital twins, that are used by executives in management of the value chain network operations of an enterprise.

[0098] In embodiments of the present disclosure, a method is provided for configuring role-based digital twins, comprising: receiving, by a processing system having one or more processors, an organizational definition of an enterprise, wherein the organizational definition defines a set of roles within the enterprise; generating, by the processing system, an organizational digital twin of the enterprise based on the organizational definition, wherein the organizational digital twin is a digital representation of an organizational structure of the enterprise; determining, by the processing system, a set of relationships between different roles within the set of roles based on the organizational definition; determining, by the processing system, a set of settings for a role from the set of roles based on the determined set of relationships; linking an identity of a respective individual to the role; determining, by the processing system, a configuration of a presentation layer of a role-based digital twin corresponding to the role based on the settings of the role that is linked to the identity, wherein the configuration of the presentation layer defines a set of states that is depicted in the role-based digital twin associated with the role; determining, by the processing system, a set of data sources that provide data corresponding to the set of states, wherein each data source provides one or more respective types of data; and configuring one or more data structures that is received from the one or more data sources, wherein the one or more data structures are configured to provide data used to populate one or more of the set of states in the role-based digital twin.

[0099] In embodiments, an organizational definition may further identify a set of physical assets of the enterprise.

[0100] In embodiments, determining a set of relationships may include parsing the organizational definition to identify a reporting structure and one or more business units of the enterprise.

[0101] In embodiments, a set of relationships may be inferred from a reporting structure and a business unit.

[0102] In embodiments, a set of identities may be linked to a set of roles, wherein each identity corresponds to a respective role from the set of roles.

[0103] In embodiments, a role-based digital twin may integrate with an enterprise resource planning system that operates on the organizational digital twin that represents a set of roles in the enterprise, such that changes in an enterprise resource planning system are automatically reflected in the organizational digital twin.

[0104] In embodiments, an organizational structure may include hierarchical components, which may be embodied in a graph data structure.

[0105] In embodiments, a set of settings for the set of roles may include role-based permission settings.

[0106] In embodiments, a role-based permission setting may be based on hierarchical components defined in the organizational definition.

[0107] In embodiments, a set of settings for a set of roles may include role-based preference settings.

[0108] In embodiments, a role-based preference setting may be configured based on a set of rolespecific templates.

[0109] In embodiments, a set of templates may include at least one of a CEO template, a COO template, a CFO template, a counsel template, a board member template, a CTO template, a chief marketing officer template, an information technology manager template, a chief information officer template, a chief data officer template, an investor template, a customer template, a vendor template, a supplier template, an engineering manager template, a project manager template, an operations manager template, a sales manager template, a salesperson template, a service manager template, a maintenance operator template, and a business development template.

[0110] In embodiments, a set of settings for the set of roles may include role-based taxonomy settings.

[0111] In embodiments, a taxonomy setting may identify a taxonomy that is used to characterize data that is presented in a role-based digital twin, such that the data is presented in a taxonomy that is linked to the role corresponding to the role-based digital twin.

[0112] In embodiments, a set of taxonomies includes at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, and a business development taxonomy.

[0113] In embodiments, at least one role of the set of roles may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, a human resources manager role, an investor role, an engineering manager role, an accountant role, an auditor role, a resource planning role, a public relations manager role, a project manager role, an operations manager role, a research and development role, an engineer role, including but not limited to mechanical engineer, electrical engineer, semiconductor engineer, chemical engineer, computer science engineer, data science engineer, network engineer, or some other type of engineer, and a business development role.

[0114] In embodiments, at least one role may be selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

[0115] In embodiments, at least one role may be selected from among a market maker role, a market analyst role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

[0116] In embodiments, at least one role may be selected from among a chief marketing officer role, a product development role, a supply chain manager role, a product design role, a marketing analyst role, a product manager role, a competitive analyst role, a customer service representative role, a procurement operator, an inbound logistics operator, an outbound logistics operator, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

[0117] In embodiments of the present disclosure, a method is provided for training an expert agent, comprising; receiving digital twin data from a set of data sources, the digital twin data including: sensor data that is received from a set of sensors that monitor a set of monitored physical entities associated with the enterprise, the sensor data transported by a set of network entities; enterprise data streams generated by a set of enterprise assets, wherein the enterprise assets include at least one of physical entities associated with the enterprise and digital entities associated with the enterprise; structuring the digital twin data into a set of digital twin data structures that are configured to serve a plurality of different role-based digital twins; receiving a request for a rolebased digital twin from a client application, wherein the role-based digital twin is configured with respect to a defined role within the enterprise; determining a subset of the structured digital twin data to corresponds to a set of states that are depicted in the role-based digital twin; providing the subset of the structured digital twin data to the client application; receiving expert agent training data sets from the client application, each expert agent training data set indicating a respective action taken by a user using the client application and one or more features that correspond to the respective action; and training an expert agent on behalf of the user based on the expert agent training data sets, wherein the expert agent is configured to determine actions to be performed on behalf of the user, wherein the determined actions are either recommended to the user or automatically performed on behalf of the user.

[0118] In embodiments, a defined role may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

[0119] In embodiments, a defined role may be selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

[0120] In embodiments, a defined role may be selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

[0121] In embodiments, a defined role may be selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

[0122] In embodiments, an expert agent training data may include interactions training data that indicates a set of interactions with a set of experts by the user during performance of the role.

[0123] In embodiments, a set of interactions used to train the expert agent may include interactions of the user with the physical entities, interactions of the user with the role-based digital twin, interactions of the user with the sensor data as depicted in the role-based digital twin, interactions of the experts with the data streams generated by the physical entities, interactions of the experts with one or more computational entities, interactions of the user with one or more network entities, or some other type of interaction.

[0124] In embodiments, an expert agent may be trained to determine an action selected from the group comprising: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, selection of an object, selection of a workflow, triggering of a workflow, ordering of a process, ordering of a workflow, cessation of a workflow, selection of a data set, selection of a design choice, creation of a set of design choices, identification of a failure mode, identification of a fault, identification of an operating mode, identification of a problem, selection of a human resource, selection of a workforce resource, providing an instruction to a human resource, and providing an instruction to a workforce resource.

[0125] In embodiments, an executive may be trained on a training set of outcomes resulting from the actions taken by the executive.

[0126] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0127] In embodiments, an expert agent may be trained to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource, physically securing equipment, physically securing hardware, cyber-securing a system, cyber-securing a device, cybersecuring a process, cyber-securing a network, cyber-securing a computational resource, cybersecuring equipment, cyber-securing hardware, detecting a threat, detecting a fault, tuning a system, tuning a device, tuning a process, tuning a network, tuning a computational resource, tuning equipment, tuning hardware, optimizing a system, optimizing a device, optimizing a process, optimizing a network, optimizing a computational resource, optimizing equipment, optimizing hardware, monitoring a system, monitoring a device, monitoring a process, monitoring a network, monitoring a computational resource, monitoring equipment, monitoring hardware, configuring a system, configuring a device, configuring a process, configuring a network, configuring a computational resource, configuring equipment, and configuring hardware.

[0128] In embodiments, an expert agent is at least one of trained and configured via feedback from at least one expert in the defined role regarding a set of outputs of expert agent.

[0129] In embodiments, a set of outputs of the expert agent upon which the expert provides feedback may include at least one of a recommendation, a classification, a prediction, a control instruction, an input selection, a protocol selection, a communication, an alert, a target selection for a communication, a data storage selection, a computational selection, a configuration, an event detection, and a forecast.

[0130] In embodiments, feedback of the at least one expert may be solicited to train the expert agent to replicate the expertise of the expert in the role.

[0131] In embodiments, a feedback of the at least one expert may be used to modify the set of inputs to the expert agent and / or used to identify and characterize at least one error by the expert agent.

[0132] In embodiments, a report on a set of errors may be provided to a user of the expert agent to enable reconfiguring of the expert agent based on the feedback from the expert.

[0133] In embodiments, reconfiguring the artificial intelligence system may include at least one of removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and augmenting the set of inputs to the artificial intelligence system.

[0134] In embodiments, an expert agent may be trained leam upon a training set of outcomes and to provide at least one of training and guidance to an individual who is responsible for performing the defined role.

[0135] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0136] In embodiments of the present disclosure, a method is provided taking an information technology architecture that supports a digital twin of a set of physical and digital entities, the architecture including: a set of sensors that provide sensor data about the set of physical entities; a set of data streams generated by at least a subset of the set of physical and digital entities; a set of computational entities for processing data and a set of network entities for transporting data that is derived from the set of sensors and the set of data streams; a set of data processing systems for extracting, transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin; and integrating an artificial intelligence system with the information technology architecture, wherein the artificial intelligence system is configured to operate as a double of an expert worker for a defined role of the enterprise.

[0137] In embodiments, an artificial intelligence system may be trained upon a training set of data that includes a set of interactions by a specific expert worker during performance of the defined role.

[0138] In embodiments, a set of interactions may be used to train the artificial intelligence system may include interactions of the expert with the physical entities, wherein the set of interactions used to train the artificial intelligence system includes interactions of the expert with the digital twin.

[0139] In embodiments, a set of interactions used to train the artificial intelligence system may include interactions of the expert with the sensor data, wherein the set of interactions used to train the artificial intelligence system includes interactions of the expert with the data streams generated by the physical entities.

[0140] In embodiments, a set of interactions used to train the artificial intelligence system may include interactions of the expert with the computational entities, wherein the set of interactions used to train the artificial intelligence system may include interactions of the expert with the network entities.

[0141] In embodiments, a set of interactions may be parsed to identify a chain of reasoning of the expert worker upon a set of information and the chain of reasoning is embodied in the configuration of the artificial intelligence system.

[0142] In embodiments, an artificial intelligence system may be trained based on the set interactions to determine an action selected from: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, selection of an object, selection of a workflow, triggering of a workflow, ordering of a process, ordering of a workflow, cessation of a workflow, selection of a data set, selection of a design choice, creation of a set of design choices, identification of a failure mode, identification of a fault, identification of an operating mode, identification of a problem, selection of a human resource, selection of a workforce resource, providing an instruction to a human resource, and providing an instruction to a workforce resource.

[0143] In embodiments, a chain of reasoning may be parsed to identify a type of reasoning of the expert worker and the type of reasoning is used as a basis for configuration of the artificial intelligence system.

[0144] In embodiments, a chain of reasoning may be a deductive chain of reasoning from a set of data.

[0145] In embodiments, a chain of reasoning may be an inductive chain of reasoning, a classification chain of reasoning, a predictive chain of reasoning, an iterative chain of reasoning, a trial-and-error chain of reasoning, a Bayesian chain of reasoning, a scientific method chain of reasoning, or some other reasoning method or system.

[0146] In embodiments, an artificial intelligence system may be trained on a training set to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource, physically securing equipment, physically securing hardware, cybersecuring a system, cyber-securing a device, cyber-securing a process, cyber-securing a network, cyber-securing a computational resource, cyber-securing equipment, cyber-securing hardware, detecting a threat, detecting a fault, tuning a system, tuning a device, tuning a process, tuning a network, tuning a computational resource, tuning equipment, tuning hardware, optimizing a system, optimizing a device, optimizing a process, optimizing a network, optimizing a computational resource, optimizing equipment, optimizing hardware, monitoring a system, monitoring a device, monitoring a process, monitoring a network, monitoring a computational resource, monitoring equipment, monitoring hardware, configuring a system, configuring a device, configuring a process, configuring a network, configuring a computational resource, configuring equipment, and configuring hardware.

[0147] In embodiments, a training set of interactions may be parsed to identify a type of processing of the expert worker upon a set of information and the type of processing is embodied in the configuration of the artificial intelligence system.

[0148] In embodiments, a type of processing may use visual processing of the expert worker and the artificial intelligence system is configured to operate on image or video information.

[0149] In embodiments, a type of processing may use audio processing of the expert worker and the artificial intelligence system may be configured to operate on audio information.

[0150] In embodiments, a type of processing may use touch processing of the expert worker and the artificial intelligence system may be configured to operate on physical sensor information.

[0151] In embodiments, a type of processing may use olfactory processing of the expert worker and the artificial intelligence system may be configured to operate on chemical sensing information.

[0152] In embodiments, a type of processing may use textual information processing of the expert worker and the artificial intelligence system may be configured to operate on text information.

[0153] In embodiments, a type of processing may use motion processing of the expert worker and the artificial intelligence system may be configured to operate on motion information.

[0154] In embodiments, a type of processing may use taste processing of the expert worker and the artificial intelligence system may be configured to operate on chemical information.

[0155] In embodiments, a type of processing may use mathematical processing of the expert worker and the artificial intelligence system may be configured to operate mathematically on available data.

[0156] In embodiments, a type of processing may use executive manager processing of the expert worker and the artificial intelligence system may be configured to provide executive decision support.

[0157] In embodiments, a type of processing may use creative processing of the expert worker and the artificial intelligence system may be configured to provide a set of alternative options.

[0158] In embodiments, a type of processing may use analytic processing of the expert worker to select among a set of available choices and the artificial intelligence system may be configured to provide a recommendation among a set of choices.

[0159] In embodiments, an artificial intelligence system may be trained on a training set of outcomes.

[0160] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0161] In embodiments, an artificial intelligence system may be at least one of trained and configured via feedback from the specific expert worker regarding a set of outputs of the artificial intelligence system.

[0162] In embodiments, a set of outputs of the artificial intelligence system upon which the expert provides feedback may include at least one of a recommendation, a classification, a prediction, a control instruction, an input selection, a protocol selection, a communication, an alert, a target selection for a communication, a data storage selection, a computational selection, a configuration, an event detection, and a forecast.

[0163] In embodiments, a feedback of the expert may be solicited to train the artificial intelligence system to replicate the expertise of the expert in the role, used to modify the set of inputs to the artificial intelligence system, and or used to identify and characterize at least one error by the artificial intelligence system.

[0164] In embodiments, a report on a set of errors may be provided to a manager associated with the artificial intelligence system to enable reconfiguring of the artificial intelligence system based on the feedback from the expert.

[0165] In embodiments, reconfiguring the artificial intelligence system may include at least one of removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and augmenting the set of inputs to the artificial intelligence system.

[0166] In embodiments, an artificial intelligence system may be configured to provide at least one of training and guidance to another worker to enable the other worker to perform the defined role.

[0167] In embodiments, an artificial intelligence system may leam on a training set of outcomes to enhance the training and guidance.

[0168] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0169] In embodiments, an artificial intelligence system may be configured to provide at least one of training and guidance to another worker to enable the other worker to perform the defined role.

[0170] In embodiments, an artificial intelligence system may leam on a training set of outcomes to enhance the training and guidance.

[0171] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0172] In embodiments, an artificial intelligence system may be configured to provide at least one of training and guidance to the expert worker to enable the expert worker to perform the defined role.

[0173] In embodiments, an artificial intelligence system may leam on a training set of outcomes to enhance the training and guidance.

[0174] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0175] In embodiments, outcomes may be compared between a set of actions of the expert worker and a set of outputs of the artificial intelligence system.

[0176] In embodiments, a comparison may be used to train the expert worker.

[0177] In embodiments, a comparison may be used to improve the artificial intelligence system.

[0178] In embodiments, a defined role of the expert worker may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, a chief marketing officer role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, a customer role, a vendor role, a supplier role, an engineering manager role, a project manager role, an operations manager role, a sales manager role, a salesperson role, a service manager role, a maintenance operator role, and a business development role.

[0179] In embodiments, computational entities and the network entities may be integrated as a converged computational and network entity.

[0180] In embodiments of the present disclosure, a method is provided for maintaining an information technology architecture that supports a digital twin of a set of physical entities, the architecture including: a set of sensors that provide sensor data about the set of physical entities; a set of data streams generated by at least a subset of the set of physical entities; a set of computational entities for processing data and a set of network entities for transporting data that is derived from the set of sensors and the set of data streams; a set of data processing systems for extracting, transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin; and integrating an artificial intelligence system with the information technology architecture, wherein the artificial intelligence system is configured to operate as a double of an expert worker for a defined role of the enterprise and wherein an electronic account associated with the expert worker is awarded with a benefit for training the artificial intelligence system.

[0181] In embodiments, a benefit may be a reward based on the outcomes of the use of the artificial intelligence system, a reward based on the productivity of the artificial intelligence system and / or a reward based on a measure of the expertise of the artificial intelligence system.

[0182] In embodiments, a benefit may be a share of revenue or profit generated by the work of the artificial intelligence system and / or a reward that is tracked via a distributed ledger on a blockchain that captures information associated with a set of actions and events involving the artificial intelligence system.

[0183] In embodiments, a reward may be administered via a smart contract operating on the blockchain.

[0184] In embodiments, an artificial intelligence system may be trained upon a training set of data that includes a set of interactions by a specific expert worker during performance of the defined role.

[0185] In embodiments, a set of interactions may be used to train the artificial intelligence system includes interactions of the expert with the physical entities, used to train the artificial intelligence system includes interactions of the expert with the digital twin and / or used to train the artificial intelligence system includes interactions of the expert with the sensor data.

[0186] In embodiments, a set of interactions used to train the artificial intelligence system may include interactions of the expert with the data streams generated by the physical entities, interactions of the expert with the computational entities, and / or interactions of the expert with the network entities.

[0187] In embodiments, an artificial intelligence system may be trained based on the interactions to determine an action selected from: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, selection of an object, selection of a workflow, triggering of a workflow, ordering of a process, ordering of a workflow, cessation of a workflow, selection of a data set, selection of a design choice, creation of a set of design choices, identification of a failure mode, identification of a fault, identification of an operating mode, identification of a problem, selection of a human resource, selection of a workforce resource, providing an instruction to a human resource, and providing an instruction to a workforce resource.

[0188] In embodiments, a training set of interactions may be parsed to identify a chain of reasoning of the expert worker upon a set of information and the chain of reasoning is embodied in the configuration of the artificial intelligence system.

[0189] In embodiments, a chain of reasoning may be parsed to identify a type of reasoning of the expert worker and the type of reasoning is used as a basis for configuration of the artificial intelligence system.

[0190] In embodiments, a chain of reasoning may be a deductive chain of reasoning from a set of data.

[0191] In embodiments, an artificial intelligence system may be trained to perform an action selected from: determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource, physically securing equipment, physically securing hardware, cyber-securing a system, cyber-securing a device, cyber-securing a process, cyber-securing a network, cyber-securing a computational resource, cyber-securing equipment, cyber-securing hardware, detecting a threat, detecting a fault, tuning a system, tuning a device, tuning a process, tuning a network, tuning a computational resource, tuning equipment, tuning hardware, optimizing a system, optimizing a device, optimizing a process, optimizing a network, optimizing a computational resource, optimizing equipment, optimizing hardware, monitoring a system, monitoring a device, monitoring a process, monitoring a network, monitoring a computational resource, monitoring equipment, monitoring hardware, configuring a system, configuring a device, configuring a process, configuring a network, configuring a computational resource, configuring equipment, and configuring hardware.

[0192] In embodiments of the present disclosure, a method is provided for taking an information technology architecture that supports a digital twin of a set of physical entities, the architecture including: a set of sensors that provide sensor data about the set of physical entities; a set of data streams generated by at least a subset of the set of physical entities; a set of computational entities for processing data and a set of network entities for transporting data that is derived from the set of sensors and the set of data streams; a set of data processing systems for extracting, transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin; and integrating an artificial intelligence system with the information technology architecture, wherein the artificial intelligence system is configured to operate as a double of a defined workforce involving a defined set of roles of the enterprise.

[0193] In embodiments, an artificial intelligence system may be trained upon a training set of data that includes a set of interactions by members of the defined workforce during performance of the defined set of roles.

[0194] In embodiments, a set of interactions used to train the artificial intelligence system may include interactions of the workforce with the physical entities, interactions of the workforce with the digital twin, interactions of the workforce with the sensor data, interactions of the workforce with the data streams generated by the physical entities, interactions of the workforce with the computational entities, and / or interactions of the workforce with the network entities.

[0195] In embodiments, a training set of interactions may be parsed to identify a chain of operations of the workforce upon a set of information and the chain of reasoning may be embodied in the configuration of the artificial intelligence system.

[0196] In embodiments, a training set of interactions may be parsed to identify a type of processing of the workforce upon a set of information and the type of processing may be embodied in the configuration of the artificial intelligence system.

[0197] In embodiments, an artificial intelligence system may be trained based on the interactions to determine an action selected from: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, selection of an object, selection of a workflow, triggering of a workflow, ordering of a process, ordering of a workflow, cessation of a workflow, selection of a data set, selection of a design choice, creation of a set of design choices, identification of a failure mode, identification of a fault, identification of an operating mode, identification of a problem, selection of a human resource, selection of a workforce resource, providing an instruction to a human resource, and providing an instruction to a workforce resource.

[0198] In embodiments, an artificial intelligence system may be trained on a training set of outcomes.

[0199] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0200] In embodiments, an artificial intelligence system may be at least one of trained and configured via feedback from members of the workforce regarding a set of outputs of the artificial intelligence system.

[0201] In embodiments, a set of outputs of the artificial intelligence system upon which the workforce members provide feedback may include at least one of a recommendation, a classification, a prediction, a control instruction, an input selection, a protocol selection, a communication, an alert, a target selection for a communication, a data storage selection, a computational selection, a configuration, an event detection, and a forecast.

[0202] In embodiments, a feedback of the workforce members may be solicited to train the artificial intelligence system to replicate the operation of the workforce in the defined set of roles.

[0203] In embodiments, a feedback of the workforce members may be used to modify the set of inputs to the artificial intelligence system.

[0204] In embodiments, a feedback of the workforce members may be used to identify and characterize at least one error by the artificial intelligence system.

[0205] In embodiments, a report on a set of errors may be provided to a manager of the artificial intelligence system to enable reconfiguring of the artificial intelligence system based on the feedback.

[0206] In embodiments, reconfiguring the artificial intelligence system may include at least one of removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and augmenting the set of inputs to the artificial intelligence system.

[0207] In embodiments, an artificial intelligence system may be configured to provide at least one of training and guidance to enable the other worker to perform a role within the defined set of roles of the workforce.

[0208] In embodiments, an artificial intelligence system may learn on a training set of outcomes to enhance the training and guidance.

[0209] In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

[0210] In embodiments, an artificial intelligence system may be trained to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource, physically securing equipment, physically securing hardware, cybersecuring a system, cyber-securing a device, cyber-securing a process, cyber-securing a network, cyber-securing a computational resource, cyber-securing equipment, cyber-securing hardware, detecting a threat, detecting a fault, tuning a system, tuning a device, tuning a process, tuning a network, tuning a computational resource, tuning equipment, tuning hardware, optimizing a system, optimizing a device, optimizing a process, optimizing a network, optimizing a computational resource, optimizing equipment, optimizing hardware, monitoring a system, monitoring a device, monitoring a process, monitoring a network, monitoring a computational resource, monitoring equipment, monitoring hardware, configuring a system, configuring a device, configuring a process, configuring a network, configuring a computational resource, configuring equipment, and configuring hardware.

[0211] In embodiments, an artificial intelligence system may be configured to provide at least one of training and guidance to the workforce to enable the workforce to perform the defined role.

[0212] In embodiments, an artificial intelligence system may leam on a training set of outcomes to enhance the training and guidance.

[0213] In embodiments, a training set of outcomes may include, data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome

[0214] In embodiments, outcomes may be compared between a set of actions of the workforce and a set of outputs of the artificial intelligence system, wherein the comparison is used to train the workforce and / or is used to improve the artificial intelligence system.

[0215] In embodiments, at least one role within the set of roles of the workforce may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

[0216] In embodiments, a workforce may be a factory operations workforce, a plant operations workforce, a resource extraction operations workforce, a network operations workforce responsible for operating a network for an industrial production environment, a supply chain management workforce, a demand planning workforce, a logistics planning workforce, a vendor management workforce, or some other kind of workforce.

[0217] In embodiments, a workforce may be a brokering workforce for a marketplace, a trading workforce for a marketplace, a trade reconciliation workforce for a marketplace, a transactional execution workforce for a marketplace, or some other kind of workforce.

[0218] In embodiments, computational entities and the network entities may be integrated as a converged computational and network entity.

[0219] In embodiments of the present disclosure, a method is provided for configuring a digital twin of a workforce, comprising: representing an enterprise organizational structure in a digital twin of an enterprise; parsing the structure to infer relationships among a set of roles within the organizational structure, the relationships and the roles defining a workforce of the enterprise; and configuring the presentation layer of a digital twin to represent the enterprise as a set of workforces having a set of attributes and relationships.

[0220] In embodiments, a digital twin may integrate with an enterprise resource planning system that operates on a data structure representing a set of roles in the enterprise, such that changes in the enterprise resource planning system are automatically reflected in the digital twin.

[0221] In embodiments, an organizational structure may include hierarchical components.

[0222] In embodiments, hierarchical components may be embodied in a graph data structure.

[0223] In embodiments, a workforce may be a factory operations workforce, a plant operations workforce, a resource extraction operations workforce, or some other type of workforce.

[0224] In embodiments, a workforce may be a network operations workforce responsible for operating a network for an industrial production environment, wherein the workforce is a supply chain management workforce, a demand planning workforce, a logistics planning workforce, a vendor management workforce, a brokering workforce for a marketplace, a trading workforce for a marketplace, a trade reconciliation workforce for a marketplace, a transactional execution workforce for a marketplace, or some other type of workforce.

[0225] In embodiments, at least one workforce role may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

[0226] In embodiments, at least one workforce role may be selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

[0227] In embodiments, at least one workforce role may be selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

[0228] In embodiments, at least one workforce role may be selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

[0229] In embodiments, a digital twin may represent a recommendation for training for the workforce, a recommendation for augmentation of the workforce, a recommendation for configuration of a set of operations involving the workforce, a recommendation for configuration of the workforce, or some other kind of recommendation.

[0230] In embodiments of the present disclosure, a method is provided for providing a digital twin of a workforce, comprising: maintaining an information technology architecture that supports a digital twin of a set of physical and digital entities, the architecture including: a set of sensors that provide sensor data about the set of physical entities; a set of data streams generated by at least a subset of the set of physical and digital entities; a set of computational entities for processing data and a set of network entities for transporting data that is derived from the set of sensors and the set of data streams; a set of data processing systems for extracting, transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin; representing an enterprise organizational structure in a digital twin of an enterprise; parsing the structure to infer relationships among a set of roles within the organizational structure, the relationships and the roles defining a workforce of the enterprise; integrating an artificial intelligence system with the information technology architecture, wherein the artificial intelligence system is configured to operate as a double of a set of workers for a set of defined roles of the enterprise and configuring the presentation layer of a digital twin to represent the enterprise as a set of workforces having a set of attributes and relationships, wherein the attributes and relationships include human worker attributes and relationships and artificial intelligence double attributes and relationships.

[0231] In embodiments, a digital twin may integrate with an enterprise resource planning system that operates on a data structure representing a set of roles in the enterprise, such that changes in the enterprise resource planning system are automatically reflected in the digital twin.

[0232] In embodiments, an organizational structure may include hierarchical components.

[0233] In embodiments, hierarchical components may be embodied in a graph data structure.

[0234] In embodiments, a workforce may be a factory operations workforce, a plant operations workforce, a resource extraction operations workforce, a network operations workforce responsible for operating a network for an industrial production environment, a supply chain management workforce, a demand planning workforce, a logistics planning workforce, a vendor management workforce, a brokering workforce, a trading workforce, a trade reconciliation workforce, a transactional execution workforce, or some other type of workforce.

[0235] In embodiments, at least one workforce role may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

[0236] In embodiments, at least one workforce role may be selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

[0237] In embodiments, at least one workforce role may be selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

[0238] In embodiments, at least one workforce role may be selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

[0239] In embodiments, a digital twin may represent a recommendation for training for the workforce, a recommendation for augmentation of the workforce, a recommendation for configuration of a set of operations involving the workforce, a recommendation for configuration of the workforce, a set of capacities and competencies of a set of workers and a set of doubles, and / or a set of mixed workgroups of human workers and artificial intelligence doubles.

[0240] In embodiments of the present disclosure, a method is provided for serving digital twins comprising: receiving, by a processing system of a digital twin system, a request for a digital twin from a user device of a user associated with an enterprise, the enterprise deploying a sensor system to monitor one or more facilities of the enterprise; determining, by the processing system, a workforce role of the user with respect to the enterprise; generating, by the processing system, a role-based digital twin corresponding to the workforce role of the user based on a perspective view corresponding to the workforce role of the user, wherein the role-based digital twin depicts one or more states and / or entities that are related to the enterprise; providing, by the processing system, the role-based digital twin to the user device, wherein providing the role-based digital twin: identifying, by the processing system, a set of data types that are used to populate the at least one of the states and / or entities of the role-based digital twin, wherein the set of data types include one or more sensor data feeds that are received from the sensor system deployed by the enterprise; and connecting, by the processing system, the one or more sensor data streams to the role-based digital twin.

[0241] In embodiments, generating a role-based digital twin may include determining the perspective view corresponding to the workforce role of the user based on the workforce role of the user and a set of data types that are relevant to the workforce role of the user.

[0242] In embodiments, determining the perspective view corresponding to the workforce role of the user may include determining an appropriate granularity level for each of the data types.

[0243] In embodiments, an appropriate granularity level for at least one of the data types may be defined in a default configuration corresponding to the workforce role.

[0244] In embodiments, an appropriate granularity level for at least one of the data types may be determined based on previous interactions of the user with the role-based digital twin.

[0245] In embodiments, a sensor system may include an edge device that receives sensor data from a set of sensors within the sensor system and generates the sensor data stream that is provided to the digital twin system via a network.

[0246] In embodiments, an edge device may receive sensor data from the set of sensors and selectively compresses the sensor data based on values indicated in the sensor data to obtain the sensor data stream.

[0247] In embodiments, connecting the one or more sensor streams may include: receiving the sensor data stream from the edge device; and routing the sensor data stream to the user device that is presenting the role-based digital twin to the user.

[0248] In embodiments, connecting the one or more sensor streams may include: receiving the sensor data stream from the edge device; analyzing the sensor data stream to identify one or more fault conditions corresponding to an object being monitored by the sensor system; and routing an indicator of the fault condition to the user device that is presenting the role-based digital twin to the user.

[0249] In embodiments, connecting the one or more sensor streams may include: receiving the sensor data stream from the edge device; analyzing the sensor data stream to identify a recommendation corresponding to the workforce role of the user; and routing an indicator of the recommendation to the user device that is presenting the role-based digital twin to the user.

[0250] In embodiments, connecting the one or more sensor streams may include: receiving the sensor data stream from the edge device; analyzing the sensor data stream to identify a recommendation corresponding to the workforce role of the user; and routing an indicator of the recommendation to the user device that is presenting the role-based digital twin to the user.

[0251] In embodiments, a workforce may be a factory operations workforce, a plant operations workforce, a resource extraction operations workforce, a network operations workforce responsible for operating a network for an industrial production environment, a supply chain management workforce, a demand planning workforce, a logistics planning workforce, a vendor management workforce, or some other type of workforce.

[0252] In embodiments, at least one workforce role may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

[0253] In embodiments, at least one workforce role may be selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

[0254] In embodiments, at least one workforce role may be selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

[0255] In embodiments, at least one workforce role may be selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

[0256] In embodiments of the present disclosure, a method is provided for providing a digital twin of a workforce, comprising: maintaining an information technology architecture that supports a digital twin of a set of physical and digital entities, the architecture including: a set of sensors that provide sensor data about the set of physical entities; a set of data streams generated by at least a subset of the set of physical and digital entities; a set of computational entities for processing data and a set of network entities for transporting data that is derived from the set of sensors and the set of data streams; a set of data processing systems for extracting, transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin; representing an enterprise organizational structure in a digital twin of an enterprise; parsing the structure to infer relationships among a set of roles within the organizational structure, the relationships and the roles defining a workforce of the enterprise; determining a set of parameters with which the digital twin is configured based on the inferred set of relationships; and configuring the presentation layer of a digital twin based on the set of parameters.

[0257] A more complete understanding of the disclosure will be appreciated from the description and accompanying drawings and the claims, which follow. All documents referenced herein are hereby incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0258] The accompanying drawings, which are included to provide a better understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the many aspects of the disclosure. In the drawings:

[0259] Fig. 1 is a block diagram showing prior art relationships of various entities and facilities in a supply chain.

[0260] Fig. 2is a block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure.

[0261] Fig. 3 is another block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure.

[0262] Fig. 4 is a block diagram showing components and interrelationships of systems and processes of a digital products network of Figs. 2 and 3 in accordance with the present disclosure.

[0263] Fig. 5 is a block diagram showing components and interrelationships of systems and processes of a value chain network technology stack in accordance with the present disclosure.

[0264] Fig. 6 is a block diagram showing a platform and relationships for orchestrating controls of various entities in a value chain network in accordance with the present disclosure.

[0265] Fig. 7 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0266] Fig. 8 is a block diagram showing components and relationships of value chain entities managed by embodiments of a value chain network management platform in accordance with the present disclosure.

[0267] Fig. 9 is a block diagram showing network relationships of entities in a value chain network in accordance with the present disclosure.

[0268] Fig. 10 is a block diagram showing a set of applications supported by unified data handling layers in a value chain network management platform in accordance with the present disclosure.

[0269] Fig. 11 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0270] Fig. 12is a block diagram showing components and relationships of a data storage layer in embodiments of a value chain network management platform in accordance with the present disclosure.

[0271] Fig. 13 is a block diagram showing components and relationships of an adaptive intelligent systems layer in embodiments of a value chain network management platform in accordance with the present disclosure.

[0272] Fig. 14 is a block diagram that depicts providing adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0273] Fig. 15 is a block diagram that depicts providing hybrid adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications or a category of goods in accordance with the present disclosure.

[0274] Fig. 16 is a block diagram that depicts providing adaptive intelligence systems for predictive intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0275] Fig. 17 is a block diagram that depicts providing adaptive intelligence systems for classification intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0276] Fig. 18 is a block diagram that depicts providing adaptive intelligence systems to produce automated control signals for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0277] Fig. 19 is a block diagram that depicts training artificial intelligence / machine learning systems to produce information routing recommendations for a selected value chain network in accordance with the present disclosure.

[0278] Fig. 20 is a block diagram that depicts a semi-sentient problem recognition system for recognition of pain points / problem states in a value chain network in accordance with the present disclosure.

[0279] Fig. 21 is a block diagram that depicts a set of artificial intelligence systems operating on value chain information to enable automated coordination of value chain activities for an enterprise in accordance with the present disclosure.

[0280] Fig. 22 is a block diagram showing components and relationships involved in integrating a set of digital twins in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0281] Fig. 23 is a block diagram showing a set of digital twins involved in embodiments of a value chain network management platform in accordance with the present disclosure.

[0282] Fig. 24 is a block diagram showing components and relationships of entity discovery and management systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0283] Fig. 25 is a block diagram showing components and relationships of a robotic process automation system in embodiments of a value chain network management platform in accordance with the present disclosure.

[0284] Fig. 26 is a block diagram showing components and relationships of a set of opportunity miners in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0285] Fig. 27 is a block diagram showing components and relationships of a set of edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0286] Fig. 28 is a block diagram showing components and relationships in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0287] Fig. 29 is a block diagram showing additional details of components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0288] Fig. 30 is a block diagram showing components and relationships in an embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities in accordance with the present disclosure.

[0289] Fig. 31 is a block diagram showing components and relationships of a unified database in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0290] Fig. 32 is a block diagram showing components and relationships of a set of unified data collection systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0291] Fig. 33 is a block diagram showing components and relationships of a set of Internet of Things monitoring systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0292] Fig. 34 is a block diagram showing components and relationships of a machine vision system and a digital twin in embodiments of a value chain network management platform in accordance with the present disclosure.

[0293] Fig. 35 is a block diagram showing components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0294] Fig. 36 is a block diagram showing additional details of components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0295] Fig. 37 is a block diagram showing components and relationships of a set of unified adaptive intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0296] Fig. 38 is a schematic of a system configured to train an artificial system that is leveraged by a value chain system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0297] Fig. 39 is a schematic of a system configured to train an artificial system that is leveraged by a container fleet management system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0298] Fig. 40 is a schematic of a system configured to train an artificial system that is leveraged by a logistics design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0299] Fig. 41 is a schematic of a system configured to train an artificial system that is leveraged by a packaging design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0300] Fig. 42 is a schematic of a system configured to train an artificial system that is leveraged by a waste mitigation system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0301] Fig. 43 is a schematic illustrating an example of a portion of an information technology system for value chain artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.

[0302] Fig. 44 is a block diagram showing components and relationships of a set of intelligent project management facilities in embodiments of a value chain network management platform in accordance with the present disclosure.

[0303] Fig. 45 is a block diagram showing components and relationships of an intelligent task recommendation system in embodiments of a value chain network management platform in accordance with the present disclosure.

[0304] Fig. 46 is a block diagram showing components and relationships of a routing system among nodes of a value chain network in embodiments of a value chain network management platform in accordance with the present disclosure.

[0305] Fig. 47 is a block diagram showing components and relationships of a dashboard for managing a set of digital twins in embodiments of a value chain network management platform.

[0306] Fig. 48 is a block diagram showing components and relationships in embodiments of a value chain network management platform that uses a microservices architecture.

[0307] Fig. 49 is a block diagram showing components and relationships of an Internet of Things data collection architecture and sensor recommendation system in embodiments of a value chain network management platform.

[0308] Fig. 50 is a block diagram showing components and relationships of a social data collection architecture in embodiments of a value chain network management platform.

[0309] Fig. 51 is a block diagram showing components and relationships of a crowdsourcing data collection architecture in embodiments of a value chain network management platform.

[0310] Fig. 52 is a diagrammatic view that depicts embodiments of a set of value chain network digital twins representing virtual models of a set of value chain network entities in accordance with the present disclosure.

[0311] Fig. 53 is a diagrammatic view that depicts embodiments of a warehouse digital twin kit system in accordance with the present disclosure.

[0312] Fig. 54 is a diagrammatic view that depicts embodiments of a stress test performed on a value chain network in accordance with the present disclosure.

[0313] Fig. 55 is a diagrammatic view that depicts embodiments of methods used by a machine for detecting faults and predicting any future failures of the machine in accordance with the present disclosure.

[0314] Fig. 56 is a diagrammatic view that depicts embodiments of deployment of machine twins to perform predictive maintenance on a set of machines in accordance with the present disclosure.

[0315] Fig. 57 is a schematic illustrating an example of a portion of a system for value chain customer digital twins and customer profile digital twins according to some embodiments of the present disclosure.

[0316] Fig. 58 is a schematic illustrating an example of an advertising application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.

[0317] Fig. 59 is a schematic illustrating an example of an e-commerce application integrated with the adaptive intelligent systems layer in accordance with the present disclosure.

[0318] Fig. 60 is a schematic illustrating an example of a demand management application integrated with the adaptive intelligent systems layer in accordance with the present disclosure.

[0319] Fig. 61 is a schematic illustrating an example of a portion of a system for value chain smart supply component digital twins according to some embodiments of the present disclosure.

[0320] Fig. 62 is a schematic illustrating an example of a risk management application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.

[0321] Fig. 63 is a diagrammatic view of maritime assets associated with a value chain network management platform including components of a port infrastructure in accordance with the present disclosure.

[0322] Figs. 64 and 65 are diagrammatic views of maritime assets associated with a value chain network management platform including components of a ship in accordance with the present disclosure.

[0323] Fig. 66 is a diagrammatic view of maritime assets associated with a value chain network management platform including components of a barge in accordance with the present disclosure.

[0324] Fig. 67 is a diagrammatic view of maritime assets associated with a value chain network management platform including those involved in maritime events, legal proceedings and making use of geofenced parameters in accordance with the present disclosure.

[0325] Fig. 68 is a schematic illustrating an example environment of the enterprise and executive control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.

[0326] Fig. 69 is a schematic illustrating an example set of components of the enterprise control tower and management platform according to some embodiments of the present disclosure.

[0327] Fig. 70 is a schematic illustrating and example of an enterprise data model according to some embodiments of the disclosure.

[0328] Fig. 71 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of the enterprise digital twin framework according to some embodiments of the present disclosure.

[0329] Fig. 72 is a schematic illustrating an example implementation of the enterprise and executive control tower and management platform according to some embodiments of the present disclosure.

[0330] Fig. 73 is a flow chart illustrating an example set of operations for configuring and serving an enterprise digital twin.

[0331] Fig. 74 illustrates an example set of operations of a method for configuring an organizational digital twin.

[0332] Fig. 75 illustrates an example set of operations of a method for generating an executive digital twin.

[0333] Fig. 76 through Fig. 103 are schematic diagrams of embodiments of neural net systems that may connect to, be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure. DETAILED DESCRIPTION

[0334] Over time, companies have increasingly used technology solutions to improve outcomes related to a traditional supply chain like the one depicted in Fig. 1, such as software systems for predicting and managing customer demand, RFID and asset tracking systems for tracking goods as they move through the supply chain, navigation and routing systems to improve the efficiency of route selection, and the like. However, some large trends have placed manufacturers, retailers and other businesses under increasing pressure to improve supply chain performance. First, online and ecommerce operators, in particular Amazon™ have become the largest retail channels for many categories of goods and have introduced distribution and fulfillment centers 112 throughout some geographies like the United States that house hundreds of thousands, and sometimes more, product categories (SKUs), so that customers can receive items the day after they are ordered, and in some cases on the same day (and in some cases delivered to the door by a drone, robot, and / or autonomous vehicle. For retailers that do not have extensive geographic distribution of fulfillment centers or warehouses, customer expectations for speed of delivery place increased pressure on supply chain efficiency and optimization. Accordingly, a need still exists for improved supply chain methods and systems.

[0335] Second, agile manufacturing capabilities (such as using 3D printing and robotic assembly techniques, among others), customer profiling technologies, and online ratings and reviews have led to increased customer expectations for customization and personalization of products. Accordingly, in order to compete, manufacturers and retailers need improved methods and systems for understanding, predicting, and satisfying customer demand.

[0336] Historically, supply chain management and demand planning and management have been largely separate activities, unified primarily when demand is converted to an order, which is passed to the supply side for fulfillment in a supply chain. As expectations for speed and personalization increase, a need exists for methods and systems that can provide unified orchestration of supply and demand.

[0337] In parallel with these other large trends has been the emergence of the Internet of Things, in which some categories of products, particularly smart home products like thermostats, lighting systems, and speakers, are increasingly enabled with onboard network connectivity and processing capability, often including a voice controlled intelligent agent like Alexa™ or Siri™ that allows device control and triggering of certain application features, such as playing music, or even ordering a product. In some cases, smart products 650 even initiate orders, such as printers that order refill cartridges. Intelligent products 650 are in some cases involved in a coordinated system, such as where an Amazon™ Echo™ product controls a television, or where a sensor-enabled thermostat or security camera connects to a mobile device, but most intelligent products are still involved in sets of largely isolated, application-specific interactions. As artificial intelligence capabilities increase, and as more and more computing and networking power is moved to network-enabled edge devices and systems that reside in supply environments 670, in demand environments 672, and in all of the locations, systems, and facilities that populate the path of a product 650 from the loading dock of a manufacturer to the point of destination 612 of a customer 662 or retailers 664, a need and opportunity exists for dramatically improved intelligence, control, and automation of all of the factors involved in demand and supply. Value Chain Networks

[0338] Referring to Fig. 2, a block diagram is presented at 200 showing components and interrelationships of systems and processes of a value chain network. In example embodiments, “value chain network,” as used herein, refers to elements and interconnections of historically segregated demand management systems and processes and supply chain management systems and processes, enabled by the development and convergence of numerous diverse technologies. In example embodiments a value chain control tower 260 (e.g., referred to herein in some cases as a “value chain network management platform”, a “VCNP”, or simply as “the system”, or “the platform”) may be connected to, in communication with, or otherwise operatively coupled with data processing facilities including, but not limited to, big data centers (e.g., big data processing 230) and related processing functionalities that receive data flow, data pools, data streams and / or other data configurations and transmission modalities received from, for example, digital product networks 252, directly from customers (e.g., direct connected customer 250), or some other third party 220. Communications related to market orchestration activities and communications 210, analytics 232, or some other type of input may also be utilized by the value chain control tower for demand enhancement 262, synchronized planning 234, intelligent procurement 238, dynamic fulfillment 240 or some other smart operation informed by coordinated and adaptive intelligence, as described herein.

[0339] Referring to Fig. 3, another block diagram is presented showing components and interrelationships of systems and processes of a value chain network and related uses cases, data handling, and associated entities. In example embodiments, the value chain control tower 360 may coordinate market orchestration activities 310 including, but not limited to, demand curve management 352, synchronization of an ecosystem 348, intelligent procurement 344, dynamic fulfillment 350, value chain analytics 340, and / or smart supply chain operations 342. In example embodiments, the value chain control tower 360 may be connected to, in communication with, or otherwise operatively coupled with adaptive data pipelines 302 and processing facilities that may be further connected to, in communication with, or otherwise operationally coupled with external data sources 320 and a data handling stack 330 (e.g., value chain network technology) that may include intelligent, user-adaptive interfaces, adaptive intelligence and control 332, and / or adaptive data monitoring and storage 334, as described herein. The value chain control tower 302 may also be further connected to, in communication with, or otherwise operatively coupled with additional value chain entities including, but not limited to, digital product networks 360, customers (e.g., directed connected customers 362), and / or other connected operations 364 and entities of a value chain network. Digital Product Networks (“DPN”)

[0340] Referring to Fig. 4, a block diagram is presented showing components and interrelationships of systems and processes of the digital products networks at 400. In example embodiments, products (including goods and services) may create and transmit data, such as product level data, to a communication layer within the value chain network technology stack and / or to an edge data processing facility. This data may produce enhanced product level data and may be combined with third party data for further processing, modeling or other adaptive or coordinated intelligence activity, as described herein. This may include, but is not limited to, producing and / or simulating product and value chain use cases, the data for which may be utilized by products, product development processes, product design, and the like. Stack View Examples

[0341] Referring to Fig. 5, a block diagram is presented at 500 showing components and interrelationships of systems and processes of a value chain network technology stack, which may include, but is not limited to a presentation layer, an intelligence layer, and serverless functionalities such as platforms (e.g., development and hosting platforms), data facilities (e.g., relating to data with loT and Big Data), and data aggregation facilities. In example embodiments, the presentation layer may include, but is not limited to, a user interface, and modules for investigation and discovery and tracking users’ experience and engagements. In example embodiments, the intelligence layer may include, but is not limited to, a statistical and computation methods, semantic models, an analytics library, a development environment for analytics, algorithms, logic and rules, and machine learning. In example embodiments, the platforms or the value chain network technology stack may include a development environment, APIs for connectivity, cloud and / or hosting applications, and device discovery. In example embodiments, the data aggregation facilities or layer may include, but is not limited to, modules for data normalization for common transmission and heterogeneous data collection from disparate devices. In example embodiments, the data facilities or layer may include, but is not limited to, loT and big data access, control, and collection and alternatives. In example embodiments, the value chain network technology stack may be further associated with additional data sources and / or technology enablers. Value Chain Orchestration from a Command Platform

[0342] Fig. 6 illustrates a connected value chain network 668 in which a value chain network management platform 604 (referred to herein in some cases as a “value chain control tower,” the “VCNP,” or simply as “the system,” or “the platform”) orchestrates a variety of factors involved in planning, monitoring, controlling, and optimizing various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, logistics and distribution factors, and the like. By virtue of a unified platform 604 for monitoring and managing supply factors and demand factors as well as status information (e.g., quality and status, plan, order and confirm, and / or track and trace) can be shared about and between various entities (e.g., including customers / consumers, suppliers, distribution such as distributors, suppliers, and production such as producers or production facilities) as demand factors are understood and accounted for, as orders are generated and fulfilled, and as products are created and moved through a supply chain. The value chain network 668 may include not only an intelligent product 650, but all of the equipment, infrastructure, personnel and other entities involved in planning and satisfying demand for it. Value Chain Network and Value Chain Network Management Platform

[0343] Referring to Fig. 7, the value chain network 668 managed by a value chain management platform 604 may include a set of value chain network entities 652, such as, without limitation: a product 650, which may be an intelligent product 650; a set of production facilities 674 involved in producing finished goods, components, systems, sub-systems, materials used in goods, or the like; various entities, activities and other supply factors 648 involved in supply environments 670, such as suppliers 642, points of origin 610, and the like; various entities, activities and other demand factors 644 involved in demand environments 672, such as customers 662 (including consumers, businesses, and intermediate customers such as value added resellers and distributors), retailers 664 (including online retailers, mobile retailers, conventional bricks and mortar retailers, pop-up shops and the like) and the like located and / or operating at various destinations 612; various distribution environments 678 and distribution facilities 658, such as warehousing facilities 654, fulfillment facilities 628, and delivery systems 632, and the like, as well as maritime facilities 622, such as port infrastructure facilities 660, floating assets 620, and shipyards 638, among others. In embodiments, the value chain network management platform 604 monitors, controls, and otherwise enables management (and in some cases autonomous or semi-autonomous behavior) of a wide range of value chain network 668 processes, workflows, activities, events and applications 630 (collectively referred to in some cases simply as “applications 630”).

[0344] Referring still to Fig. 7, a high-level schematic of the value chain network management platform 604 is illustrated. The value chain network management platform 604 may include a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact or the like within, or own, operate, support or enable, one or more value chain network processes, workflows, activities, events and / or applications 630 or that may otherwise be part of, integrated with, linked to, or operated on by the VCNP 604 in connection with a product 650 (which may be any category of product, such as a finished good, software product, hardware product, component product, material, item of equipment, item of consumer packaged goods, consumer product, food product, beverage product, home product, business supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product and / or set of related services, and which may, in embodiments, encompass an intelligent product 650 that is enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, selforganization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and / or various Internet of Things capabilities, among others.

[0345] In embodiments, the management platform 604 may include a set of data handling layers 624 each of which is configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation, machine learning, applications of artificial intelligence, intelligent transactions, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In embodiments, the data handling layers 624 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by a value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof. In embodiments, the data handling layers 624 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by a value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as a data storage layer 624 or storage layer 624. As shown in Fig. 7, the data handling layers 624 may also include an adaptive intelligent systems layer 614. The adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewhere throughout this disclosure. The data processing, artificial intelligence and computational systems 634 may relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, modelbased systems, and the like). Specifically, the data processing, artificial intelligence and computational systems 634 may relate to various examples, in some embodiments, such as use of a recurrent network as adaptive intelligence system operating on a blockchain of transactions in a supply chain to determine a pattern, use with biological systems, opportunity mining (e.g., where artificial intelligence system may be used to monitor for new data sources as opportunities for automatically deploying intelligence), robotic process automation (e.g., automation of intelligent agents for various workflows), edge and network intelligence (e.g., implicated on monitoring systems such as adaptively using available RF spectrum, adaptively using available fixed network spectrum, adaptively storing data based on available storage conditions, adaptively sensing based on a kind of contextual sensing), and the like.

[0346] In embodiments, the data handling layers 624 may be depicted in vertical stacks or ribbons in the figures and may represent many functionalities available to the platform 604 including storage, monitoring, and processing applications and resources and combinations thereof. In embodiments, the set of capabilities of the data handling layers 624 may include a shared microservices architecture. By way of these examples, the set of capabilities may be deployed to provide multiple distinct services or applications, which can be configured as one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within or be resident to certain applications or processes. In some examples, the set of capabilities can include one or more activities marshaled for the benefit of the platform. In some examples, the set of capabilities may include one or more events organized for the benefit of the platform. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture such as common architecture that supports a common data schema. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support a common storage. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support common monitoring systems. In embodiments, one or more sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support one or more common processing frameworks. In embodiments, the set of capabilities of the data handling layers 624 can include examples where the storage functionality supports scalable processing capabilities, scalable monitoring systems, digital twin systems, payments interface systems, and the like. By way of these examples, one or more software development kits can be provided by the platform along with deployment interfaces to facilitate connections and use of the capabilities of the data handling layers 624. In further examples, adaptive intelligence systems may analyze, leam, configure, and reconfigure one or more of the capabilities of the data handling layers 624. In embodiments, the platform 604 may, for example, include a common data storage schema serving a shipyard entity related service and a warehousing entity service. There are many other applicable examples and combinations applicable to the foregoing example including the many value chain entities disclosed herein. By way of these examples, the platform 604 may be shown to create connectivity (e.g., supply of capabilities and information) across many value chain entities. In many examples, there are pairings (doubles, triples, quadruplets, etc.) of similar kinds of value chain entities using one or more smaller sets of capabilities of the data handling layers 624 to deploy (interact with, rely on, etc.) a common data schema, a common architecture, a common interface, and the like. While services and capabilities can be provided to single value chain entities, the platform can be shown to provide myriad benefits to value chains and consumers by supporting connectivity across value chain entities and applications used by the entities. Value Chain Network Entities Managed by the Platform

[0347] Referring to Fig. 8, the value chain network management platform 604 is illustrated in connection with a set of value chain entities 652 that may be subject to management by the platform 604, may integrate with or into the platform 604, and / or may supply inputs to and / or take outputs from the platform 604, such as ones involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 (collectively “applications 630” or simply “activities”)). Connections with the value chain entities 652 may be facilitated by a set of connectivity facilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in greater detail below. This may include connectivity and interface capabilities for individual services of the platform, for the data handling layers, for the platform as a whole, and / or among value chain entities 652, among others.

[0348] These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as, without limitation: machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loading machines, cranes, lifts, haulers, trucks, loading machines, unloading machines, packing machines, picking machines, and many others, including robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”), drones, autonomous vehicles, software bots and many others); products 650 (which may be any category of products, such as a finished goods, software products, hardware products, component products, material, items of equipment, items of consumer packaged goods, consumer products, food products, beverage products, home products, business supply products, consumable products, pharmaceutical products, medical device products, technology products, entertainment products, or any other type of products and / or set of related services); value chain processes 722 (such as shipping processes, hauling processes, maritime processes, inspection processes, hauling processes, loading / unloading processes, packing / unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, and others), border control processes, port-related processes, software processes (including applications, programs, services, and others), packing and loading processes, financial processes (e.g., insurance processes, reporting processes, transactional processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others); wearable and portable devices 720 (such as mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-wom devices, clothing-integrated devices, arm bands, bracelets, neck-worn devices, AR / VR devices, headphones, and many others); workers 718 (such as delivery workers, shipping workers, barge workers, port workers, dock workers, train workers, ship workers, distribution of fulfillment center workers, warehouse workers, vehicle drivers, business managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, cargo handling workers, inspectors, delivery personnel, environmental control managers, financial asset managers, process supervisors and workers (for any of the processes mentioned herein), security personnel, safety personnel and many others); suppliers 642 (such as suppliers of goods and related services of all types, component suppliers, ingredient suppliers, materials suppliers, manufacturers, and many others); customers 662 (including consumers, licensees, businesses, enterprises, value added and other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use a category of goods and / or related services); a wide range of operating facilities 712 (such as loading and unloading docks, storage and warehousing facilities 654, vaults, distribution facilities 658 and fulfillment centers 628, air travel facilities 740 (including aircraft, airports, hangars, runways, refueling depots, and the like), maritime facilities 622 (such as port infrastructure facilities 622 (such as docks, yards, cranes, roll-on / roll-off facilities, ramps, containers, container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (such as ships, barges, boats and others), facilities and other items at points of origin 610 and / or points of destination 628, hauling facilities 710 (such as container ships, barges, and other floating assets 620, as well as land-based vehicles and other delivery systems 632 used for conveying goods, such as trucks, trains, and the like); items or elements factoring in demand (i.e., demand factors 644) (including market factors, events, and many others); items or elements factoring in supply (i.e., supply factors 648)(including market factors, weather, availability of components and materials, and many others); logistics factors 750 (such as availability of travel routes, weather, fuel prices, regulatory factors, availability of space (such as on a vehicle, in a container, in a package, in a warehouse, in a fulfillment center, on a shelf, or the like), and many others); retailers 664 (including online retailers 730 and others such as in the form of eCommerce sites 730); pathways for conveyance (such as waterways 732, roadways 734, air travel routes, railways 738 and the like); robotic systems 744 (including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, and others); drones 748 (including for package delivery, site mapping, monitoring or inspection, and the like); autonomous vehicles 742 (such as for package delivery); software platforms 752 (such as enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, platform as a service platforms, infrastructure as a service platforms, software-based data storage platforms, analytic platforms, artificial intelligence platforms, and others); and many others. In some example embodiments, the product 650 may be encompassed as an intelligent product 650 or the VCNP 604 may include the intelligent product 650. The intelligent product 650 may be enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and / or various Internet of Things capabilities, among others. The intelligent product 650 may include a form of information technology. The intelligent product 650 may have a processor, computer random access memory, and a communication module. The intelligent product 650 may be a passive intelligent product that is similar to a RFID type of data structure where the intelligent product may be pinged or read. The product 650 may be considered a value chain network entity (e.g., under control of platform) and may be rendered intelligent by surrounding infrastructure and adding an RFID such that data may be read from the intelligent product 650. The intelligent product 650 may fit in a value chain network in a connected way such that connectivity was built around the intelligent product 650 through a sensor, an loT device, a tag, or another component.

[0349] In embodiments, the monitoring systems layer 614 may monitor any or all of the value chain entities 652 in a value chain network 668, may exchange data with the value chain entities 652, may provide control instructions to or take instructions from any of the value chain entities 652, or the like, such as through the various capabilities of the data handling layers 624 described throughout this disclosure. Network Characteristics of the Value Chain Network Entities

[0350] Referring to Fig. 9, orchestration of a set of deeply interconnected value chain network entities 652 in a value chain network 668 by the value chain network management platform 604 is illustrated. Each of the value chain network entities 652 may have a connection to the VCNP 604, to a set of other value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a connection via a cloud, or other connection), and / or through the VCNP 604 to other value chain network entities 652. The value chain network management platform 604 may manage the connections, configure or provision resources to enable connectivity, and / or manage applications 630 that take advantage of the connections, such as by using information from one set of entities 652 to inform applications 630 involving another set of entities 652, by coordinating activities of a set of entities 652, by providing input to an artificial intelligence system of the VCNP 604 or of or about a set of entities 652, by interacting with edge computation systems deployed on or in entities 652 and their environments, and the like.

[0351] The entities 652 may be external such that the VCNP 604 may interact with these entities 652. When the VCNP 604 functions as the control tower to establish monitoring (e.g., establish monitoring such as common monitoring across several entities 652). In one unified platform, there may be an interface where a user may view various items such as user’s destinations, ports, air and rail assets, as well as orders, etc. Then, the next step may be to establish a common data schema that enables services that work on or in any one of these applications. This may involve taking any of the data that is flowing through or about any of these entities 652 and pull the data into a framework where other applications across supply and demand may interact with the entities 652. This may be a shared data pipeline coming from an loT system and other external data sources, feeding into the monitoring layer, being stored in a common data schema in the storage layer, and then various intelligence may be trained to identify implications across these entities 652. In an example embodiment, a supplier may be bankrupt, or a determination is made that the supplier is bankrupt, and then the VCNP 604 may automatically trigger a substitute smart contract to be sent to a secondary supplier with altered terms. There may be management of different aspects of the supply chain. For example, changing pricing instantly and automatically on the demand side in response to one more supplier’s being identified as bankrupt (e.g., from bankruptcy announcement). Other similar examples may be used based on what occurs in that automation layer which may be enabled by the VCNP 604. Then, at the interface layer of this VCNP 604, a digital twin may be used by user to view all these entities 652 that are not typically shown together and monitor what is going on with each of these entities 652 including identification of problem states. For example, after viewing three quarters of bad financial reports on a supplier, a report may be flagged to watch it closely for potential future bankruptcy, etc.

[0352] For example, an loT system deployed in a fulfillment center 628 may coordinate with an intelligent product 650 that takes customer feedback about the product 650, and an application 630 for the fulfillment center 628 may, upon receiving customer feedback via a connection path to the intelligent product 650 about a problem with the product 650, initiate a workflow to perform corrective actions on similar products 650 before the products 650 are sent out from the fulfillment center 628. Similarly, a port infrastructure facility 660, such as a yard for holding shipping containers, may inform a fleet of floating assets 620 via connections to the floating assets 620 (such as ships, barges, or the like) that the port is near capacity, thereby kicking off a negotiation process (which may include an automated negotiation based on a set of rules and governed by a smart contract) for the remaining capacity and enabling some assets 620 to be redirected to alternative ports or holding facilities. These and many other connections among value chain network entities 652, whether one-to-one connections, one-to-many connections, many-to-many connections, or connections among defined groups of entities 652 (such as ones controlled by the same owner or operator), are encompassed herein as applications 630 managed by the VCNP 604. Value Chain Network Activities and Applications Managed by the Platform

[0353] Referring to Fig. 10, the set of applications 630 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may include, without limitation, one or more of any of a wide range of types of applications, such as: a supply chain management application 812 (such as, without limitation, for management of timing, quantities, logistics, shipping, delivery, and other details of orders for goods, components, and other items); an asset management application 814 (such as, without limitation, for managing value chain assets, such as floating assets (such as ships, boats, barges, and floating platforms), real property (such as used for location of warehouses, ports, shipyards, distribution centers and other buildings), equipment, machines and fixtures (such as used for handling containers, cargo, packages, goods, and other items), vehicles (such as forklifts, delivery trucks, autonomous vehicles, and other systems used to move items), human resources (such as workers), software, information technology resources, data processing resources, data storage resources, power generation and / or storage resources, computational resources and other assets); a finance application 822 (such as, without limitation, for handling finance matters relating to value chain entities and assets, such as involving payments, security, collateral, bonds, customs, duties, imposts, taxes and others); a risk management application 818 (such as, without limitation, for managing risk or liability with respect to a shipment, goods, a product, an asset, a person, a floating asset, a vehicle, an item of equipment, a component, an information technology system, a security system, a security event, a cybersecurity system, an item of property, a health condition, mortality, fire, flood, weather, disability, negligence, business interruption, injury, damage to property, damage to a business, breach of a contract, and others); a demand management application 824 (such as, without limitation, an application for analyzing, planning, or promoting interest by customers of a category of goods that can be supplied by or with facilities of a value chain product or service, such as a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and others, including ones that use or are enabled by one or more features of an intelligent product 650 or that are executed using intelligence capabilities on an intelligent product 650); a trading application 858 (such as, without limitation, a buying application, a selling application, a bidding application, an auction application, a reverse auction application, a bid / ask matching application, an analytic application for analyzing value chain performance, yield, return on investment, or other metrics, or others); a tax application 850 (such as, without limitation, for managing, calculating, reporting, optimizing, or otherwise handling data, events, workflows, or other factors relating to a tax, a tariff, an impost, a levy, a tariff, a duty, a credit, a fee or other government-imposed charge, such as, without limitation, customs duties, value added tax, sales tax, income tax, property tax, municipal fees, pollution tax, renewal energy credit, pollution abatement credit, import duties, export duties, and others); an identity management application 830 (such as for managing one or more identities of entities 652 involved in a value chain, such as, without limitation, one or more of an identity verification application, a biometric identify validation application, a pattern-based identity verification application, a location-based identity verification application, a user behavior-based application, a fraud detection application, a network address-based fraud detection application, a black list application, a white list application, a content inspection-based fraud detection application, or other fraud detection application; an inventory management application 820 (such as, without limitation, for managing inventory in a fulfillment center, distribution center, warehouse, storage facility, store, port, ship or other floating asset, or other location); a security application, solution or service 834 (referred to herein as a security application, such as, without limitation, any of the identity management applications 830 noted above, as well as a physical security system (such as for an access control system (such as using biometric access controls, fingerprinting, retinal scanning, passwords, and other access controls), a safe, a vault, a cage, a safe room, a secure storage facility, or the like), a monitoring system (such as using cameras, motion sensors, infrared sensors and other sensors), a perimeter security system, a floating security system for a floating asset, a cyber security system (such as for virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyber-attack detection and remediation, packet inspection, traffic inspection, DNS attack remediation and detection, and others) or other security application); a safety application 840 (such as, without limitation, for improving safety of workers, for reducing the likelihood of damage to property, for reducing accident risk, for reducing the likelihood of damage to goods (such as cargo), for risk management with respected to insured items, collateral for loans, or the like, including any application for detecting, characterizing or predicting the likelihood and / or scope of an accident or other damaging event, including safety management based on any of the data sources, events or entities noted throughout this disclosure or the documents incorporated herein by reference); a blockchain application 844 (such as, without limitation, a distributed ledger capturing a series of transactions, such as debits or credits, purchases or sales, exchanges of in kind consideration, smart contract events, or the like, or other blockchainbased application); a facility management application 850 (such as, without limitation, for managing infrastructure, buildings, systems, real property, personal property, and other property involved in supporting a value chain, such as a shipyard, a port, a distribution center, a warehouse, a dock, a store, a fulfillment center, a storage facility, or others, as well as for design, management or control of systems and facilities in or around a property, such as an information technology system, a robotic / autonomous vehicle system, a packaging system, a packing system, a picking system, an inventory tracking system, an inspection system, a routing system for mobile robots, a workflow system for human assets, or the like); a regulatory application 852 (such as, without limitation, an application for regulating any of the applications, services, transactions, activities, workflows, events, entities, or other items noted herein and in the documents incorporated by reference herein, such as regulation of permitted routes, permitted cargo and goods, permitted parties to transactions, required disclosures, privacy, pricing, marketing, offering of goods and services, use of data (including data privacy regulations, regulations relating to storage of data and others), banking, marketing, sales, financial planning, and many others); a commerce application, solution or service 854 (such as, without limitation an e-commerce site marketplace, an online site, an auction site or marketplace, a physical goods marketplace, an advertising marketplace, a reverse-auction marketplace, an advertising network, or other marketplace); a vendor management application 832 (such as, without limitation, an application for managing a set of vendors or prospective vendors and / or for managing procurement of a set of goods, components or materials that may be supplied in a value chain, such as involving features such as vendor qualification, vendor rating, requests for proposal, requests for information, bonds or other assurances of performance, contract management, and others); an analytics application 838 (such as, without limitation, an analytic application with respect to any of the data types, applications, events, workflows, or entities mentioned throughout this disclosure or the documents incorporated by reference herein, such as a big data application, a user behavior application, a prediction application, a classification application, a dashboard, a pattern recognition application, an econometric application, a financial yield application, a return on investment application, a scenario planning application, a decision support application, a demand prediction application, a demand planning application, a route planning application, a weather prediction application, and many others); a pricing application 842 (such as, without limitation, for pricing of goods, services (including any mentioned throughout this disclosure and the documents incorporated by reference herein; and a smart contract application, solution, or service (referred to collectively herein as a smart contract application 848, such as, without limitation, any of the smart contract types referred to in this disclosure or in the documents incorporated herein by reference, such as a smart contract for sale of goods, a smart contract for an order for goods, a smart contract for a shipping resource, a smart contract for a worker, a smart contract for delivery of goods, a smart contract for installation of goods, a smart contract using a token or cryptocurrency for consideration, a smart contract that vests a right, an option, a future, or an interest based on a future condition, a smart contract for a security, commodity, future, option, derivative, or the like, a smart contract for current or future resources, a smart contract that is configured to account for or accommodate a tax, regulatory or compliance parameter, a smart contract that is configured to execute an arbitrage transaction, or many others). Thus, the value chain management platform 604 may host an enable interaction among a wide range of disparate applications 630 (such term including the above-referenced and other value chain applications, services, solutions, and the like), such that by virtue of shared microservices, shared data infrastructure, and shared intelligence, any pair or larger combination or permutation of such services may be improved relative to an isolated application of the same type.

[0354] Referring still to Fig. 10, the set of applications 630 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a payments application 860 (such as for calculating payments (including based on situational factors such as applicable taxes, duties and the like for the geography of an entity 652), transferring funds, resolving payments to parties, and the like, for any of the applications 630 noted herein); a process management application 862 (such as for managing any of the processes or workflows described throughout this disclosure, including supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, ordering processes, navigation processes, and many others); a compatibility testing application 864, such as for assessing compatibility among value chain network entities 652 or activities involved in any of the processes, workflows, activities, or other applications 630 described herein (such as for determining compatibility of a container or package with a product 650, the compatibility of a product 650 with a set of customer requirements, the compatibility of a product 650 with another product 650 (such as where one is a refill, resupply, replacement part, or the like for the other), the compatibility of a infrastructure and equipment entities 652 (such as between a container ship or barge and a port or waterway, between a container and a storage facility, between a truck and a roadway, between a drone or robot and a package, between a drone, AV or robot and a delivery destination, and many others); an infrastructure testing application 802 (such as for testing the capabilities of infrastructure elements to support a product 650 or an application 630 (such as, without limitation, storage capabilities, lifting capabilities, moving capabilities, storage capacity, network capabilities, environmental control capabilities, software capabilities, security capabilities, and many others)); and / or an incident management application 910 (such as for managing events, accidents, and other incidents that may occur in one or more environments involving value chain network entities 652, such as, without limitation, vehicle accidents, worker injuries, shutdown incidents, property damage incidents, product damage incidents, product liability incidents, regulatory non-compliance incidents, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, and others, as well as combinations among them), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, unauthorized use incidents, and many others).

[0355] Referring still to Fig. 10, the set of applications 630 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others); a logistics application 912 (such as for managing logistics for pickups, deliveries, transfer of goods onto hauling facilities, loading, unloading, packing, picking, shipping, driving, and other activities involving in the scheduling and management of the movement of products 650 and other items between points of origin and points of destination through various intermediate locations; a reverse logistic application 914 (such as for handling logistics for returned products 650, waste products, damaged goods, or other items that can be transferred on a return logistics path); a waste reduction application 920 (such as for reducing packaging waste, solid waste, waste of energy, liquid waste, pollution, contaminants, waste of computing resources, waste of human resources, or other waste involving a value chain network entity 652 or activity); an augmented reality, mixed reality and / or virtual reality application 930 (such as for visualizing one or more value chain network entities 652 or activities involved in one or more of the applications 630, such as, without limitation, movement of a product 650, the interior of a facility, the status or condition of an item of goods, one or more environmental conditions, a weather condition, a packing configuration for a container or a set of containers, or many others); a demand prediction application 940 (such as for predicting demand for a product 650, a category of products, a potential product, and / or a factor involved in demand, such as a market factor, a wealth factor, a demographic factor, a weather factor, an economic factor, or the like); a demand aggregation application 942 (such as for aggregating information, orders and / or commitments (optionally embodied in one or more contracts, which may be smart contracts) for one or more products 650, categories, or the like, including current demand for existing products and future demand for products that are not yet available); a customer profiling application 944 (such as for profiling one or more demographic, psychographic, behavioral, economic, geographic, or other attributes of a set of customers, including based on historical purchasing data, loyalty program data, behavioral tracking data (including data captured in interactions by a customer with a smart product 650), online clickstream data, interactions with intelligent agents, and other data sources); and / or a component supply application 948 (such as for managing a supply chain of components for a set of products 650).

[0356] Referring still to Fig. 10, the set of applications 630 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a policy management application 868 (such as for deploying one or more policies, rules, or the like for governance of one or more value chain network entities 652 or applications 630, such as to govern execution of one or more workflows (which may involve configuring polices in the platform 604 on a per-workflow basis), to govern compliance with regulations (including maritime, food & drug, medical, environmental, health, safety, tax, financial reporting, commercial, and other regulations as described throughout this disclosure or as would be understood in the art), to govern provisioning of resources (such as connectivity, computing, human, energy, and other resources), to govern compliance with corporate policies, to govern compliance with contracts (including smart contracts, wherein the platform 604 may automatically deploy governance features to relevant entities 652 and applications 630, such as via connectivity facilities 642), to govern interactions with other entities (such as involving policies for sharing of information and access to resources), to govern data access (including privacy data, operational data, status data, and many other data types), to govern security access to infrastructure, products, equipment, locations, or the like, and many others; a product configuration application 870 (such as for allowing a product manager and / or automated product configuration process (optionally using robotic process automation) to determine a configuration for a product 650, including configuration on-the-fly, such as during agile manufacturing, or involving configuration or customization in route (such as by 3D printing one or more features or elements), or involving configuration or customization remotely, such as by downloading firmware, configuring field programmable gate arrays, installing software, or the like; a warehousing and fulfillment application 872 (such as for managing a warehouse, distribution center, fulfillment center, or the like, such as involving selection of products, configuring storage locations for products, determining routes by which personnel, mobile robots, and the like move products around a facility, determining picking and packing schedules, routes and workflows, managing operations of robots, drones, conveyors, and other facilities, determining schedules for moving products out to loading docks or the like, and many other functions); a kit configuration and deployment application 874 (such as for enabling a user of the VCNP to configure a kit, box, or otherwise pre-integrated, pre-provisioned, and / or pre-configured system to allow a customer or worker to rapidly deploy a subset of capabilities of the VCNP 604 for a specific value chain network entity 652 and / or application 630); and / or a product testing application 878 for testing a product 650 (including testing for performance, activation of capabilities and features, safety, compliance with policy or regulations, quality, quality of service, likelihood of failure, and many other factors).

[0357] Referring still to Fig. 10, the set of applications 630 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation a maritime fleet management application 880 (for managing a set of maritime assets, such as container ships, barges, boats, and the like, as well as related infrastructure facilities such as docks, cranes, ports, and others, such as to determine optimal routes for fleet assets based on weather, market, traffic, and other conditions, to ensure compliance with policies and regulations, to ensure safety, to improve environmental factors, to improve financial metrics, and many others); a shipping management application 882 (such as for managing a set of shipping assets, such as trucks, trains, airplanes, and the like, such as to optimize financial yield, to improve safety, to reduce energy consumption, to reduce delays, to mitigate environmental impact, and for many other purposes); an opportunity matching application 884 (such as for matching one or more demand factors with one or more supply factors, for matching needs and capabilities of value chain network entities 652, for identifying reverse logistics opportunities, for identifying opportunities for inputs to enrich analytics, artificial intelligence and / or automation, for identifying cost-saving opportunities, for identifying profit and / or arbitrage opportunities, and many others); a workforce management application 888 (such as for managing workers in various work forces, including work forces in, on or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise management locations, retail stores, online / ecommerce site management facilities, ports, ships, boats, barges, trains, depots, and other facilities mentioned throughout this disclosure); a distribution and delivery application 890 (such as for planning, scheduling, routing, and otherwise managing distribution and delivery of products 650 and other items); and / or an enterprise resource planning (ERP) application 892 (such as for planning utilization of enterprise resources, including workforce resources, financial resources, energy resources, physical assets, digital assets, and other resources). Core Capabilities and Interactions of the Data Handling Layers (Adaptive Intelligence, Monitoring, Data Storage and Applications) Referring to Fig. 11, a high-level schematic of an embodiment of the value chain network management platform 604 is illustrated, including a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent management of sets of the value chain entities 652 that may occur, operate, transact or the like within, or own, operate, support or enable, one or more value chain network processes, workflows, activities, events and / or applications 630 or that may otherwise be part of, integrated with, linked to, or operated on by the platform 604 in connection with a product 650 (which may be a finished good, software product, hardware product, component product, material, item of equipment, consumer packaged good, consumer product, food product, beverage product, home product, business supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product or related service, which may, in embodiments, encompass an intelligent product that is enabled with processing, networking, sensing, computation, and / or other Internet of Things capabilities). Value chain entities 652, such as involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference.

[0358] In embodiments, the value chain network management platform 604 may include the set of data handling layers 624, each of which is configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation, machine learning, applications of artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In embodiments, the data handling layers 624 may include a value chain network monitoring systems layer 614, a value chain network entity-oriented data storage systems layer 624 (referred to in some cases herein for convenience simply as a data storage layer 624), an adaptive intelligent systems layer 614 and a value chain network management platform layer 604. The value chain network management platform 604 may include the data handling layers 624 such that the value chain network management platform layer 604 may provide management of the value chain network management platform 604 and / or management of the other layers such as the value chain network monitoring systems layer 614, the value chain network entity-oriented data storage systems layer 624 (e.g., data storage layer 624), and the adaptive intelligent systems layer 614. Each of the data handling layers 624 may include a variety of services, programs, applications, workflows, systems, components and modules, as further described herein and in the documents incorporated herein by reference. In embodiments, each of the data handling layers 624 (and optionally the platform 604 as a whole) is configured such that one or more of its elements can be accessed as a service by other layers 624 or by other systems (e.g., being configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture). For example, the platform 604 may have (or may configure and / or provision), and a data handling layer 608 may use, a set of connectivity facilities 642, such as network connections (including various configurations, types and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, micro-services, SaaS interfaces, PaaS interfaces, laaS interfaces, cloud capabilities, or the like by which data or information may be exchanged between a data handling layer 608 and other layers, systems or sub-systems of the platform 604, as well as with other systems, such as value chain entities 652 or external systems, such as cloud-based or on-premises enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems and many others). Each of the data handling layers 624 may include a set of services (e.g., microservices), for data handling, including facilities for data extraction, transformation and loading; data cleansing and deduplication facilities; data normalization facilities; data synchronization facilities; data security facilities; computational facilities (e.g., for performing pre-defined calculation operations on data streams and providing an output stream); compression and de-compression facilities; analytic facilities (such as providing automated production of data visualizations) and others.

[0359] In embodiments, each data handling layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each of the other data handling layers 624. These may include data integration capabilities, such as for extracting, transforming, loading, normalizing, compression, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and / or the applications 630, such as transforming data from one format or protocol to another as needed in order for one layer to consume output from another. In embodiments, the data handling layers 624 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof. For example, a stream of physiological data from a wearable device worn by a worker undertaking a task or a consumer engaged in an activity can be distributed via the monitoring systems layer 614 to multiple distinct applications in the value chain management platform layer 604, such as one that facilitates monitoring the physiological, psychological, performance level, attention, or other state of a worker and another that facilitates operational efficiency and / or effectiveness. In embodiments, the monitoring systems layer 614 facilitates alignment, such as time-synchronization, normalization, or the like of data that is collected with respect to one or more value chain network entities 652. For example, one or more video streams or other sensor data collected of or with respect to a worker 718 or other entity in a value chain network facility or environment, such as from a set of camera-enabled loT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos, such as machine learning systems that operate on images in the videos, on changes between images in different frames of the video, or the like. In such an example, the monitoring systems layer 614 may further align a set of videos, camera images, sensor data, or the like, with other data, such as a stream of data from wearable devices, a stream of data produced by value chain network systems (such as ships, lifts, vehicles, containers, cargo handling systems, packing systems, delivery systems, drones / robots, and the like), a stream of data collected by mobile data collectors, and the like. Configuration of the monitoring systems layer 614 as a common platform, or set of microservices, that are accessed across many applications, may dramatically reduce the number of interconnections required by an owner or other operator within a value chain network in order to have a growing set of applications monitoring a growing set of loT devices and other systems and devices that are under its control.

[0360] In embodiments, the data handling layers 624 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by the value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as the data storage layer 624 or storage layer 624. For example, various data collected about the value chain entities 652, as well as data produced by the other data handling layers 624, may be stored in the data storage layer 624, such that any of the services, applications, programs, or the like of the various data handling layers 624 can access a common data source (which may comprise a single logical data source that is distributed across disparate physical and / or virtual storage locations). This may facilitate a dramatic reduction in the amount of data storage required to handle the enormous amount of data produced by or about value chain network entities 652 as applications 630 and uses of value chain networks grow and proliferate. For example, a supply chain or inventory management application in the value chain management platform layer 604, such as one for ordering replacement parts for a machine or item of equipment, may access the same data set about what parts have been replaced for a set of machines as a predictive maintenance application that is used to predict whether a component of a ship, or facility of a port is likely to require replacement parts. Similarly, prediction may be used with respect to the resupply of items.

[0361] In embodiments, value chain network data objects 1004 may be provided according to an object-oriented data model that defines classes, objects, attributes, parameters and other features of the set of data objects (such as associated with value chain network entities 652 and applications 630) that are handled by the platform 604.

[0362] In embodiments, the data storage systems layer 624 may provide an extremely rich environment for collection of data that can be used for extraction of features or inputs for intelligence systems, such as expert systems, analytic systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems as disclosed throughout this disclosure and the documents incorporated herein by reference. As a result, each application 630 in the platform 604 and each adaptive intelligent system in the adaptive intelligent systems layer 614 can benefit from the data collected or produced by or for each of the others. In embodiments, the data storage systems layer 624 may facilitate collection of data that can be used for extraction of features or inputs for intelligence systems such as a development framework from artificial intelligence. In examples, the collections of data may pull in and / or house event logs (naturally stored or ad-hoc, as needed), perform periodic checks on onboard diagnostic data, or the like. In examples, pre calculation of features may be deployed using AWS Lambda, for example, or various other cloudbased on-demand compute capabilities, such as pre-calculations, multiplexing signals. In many examples, there are pairings (doubles, triples, quadruplets, etc.) of similar kinds of value chain entities that may use one or more sets of capabilities of the data handling layers 624 to deploy connectivity and services across value chain entities and across applications used by the entities even when amassing hundreds and hundreds of data types from relatively disparate entities. In these examples, various pairings of similar types of value chain entities using, at least in part, the connectivity and services across value chain entities and applications, may direct the information from the pairings of connected data to artificial intelligence services including the various neural networks disclosed herein and hybrid combinations thereof. In these examples, genetic programming techniques may be deployed to prune some of the input features in the information from the pairings of connected data. In these examples, genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairings. These genetic programming techniques may be shown to increase the efficacy of the determinations established by the artificial intelligence services. In these examples, the information from the pairings of connected data may be migrated to other layers on the platform including to support or deploy robotic process automation, prediction, forecasting, and other resources such that the shared data schema may facilitate as capabilities and resources for the platform 604.

[0363] A wide range of data types may be stored in the storage layer 624 using various storage media and data storage types, data architectures 1002, and formats, including, without limitation: asset and facility data 1030, state data 1140 (such as indicating a state, condition status, or other indicator with respect to any of the value chain network entities 652, any of the applications 630 or components or workflows thereof, or any of the components or elements of the platform 604, among others), worker data 1032 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data and many other types); event data 1034 ((such as with respect to any of a wide range of events, including operational data, transactional data, workflow data, maintenance data, and many other types of data that includes or relates to events that occur within a value chain network 668 or with respect to one or more applications 630, including process events, financial events, transaction events, output events, input events, state-change events, operating events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, recharging events, shipping events, warehousing events, transfers of goods, crossing of borders, moving of cargo, inspection events, supply events, and many others); claims data 664 (such as relating to insurance claims, such as for business interruption insurance, product liability insurance, insurance on goods, facilities, or equipment, flood insurance, insurance for contract-related risks, and many others, as well as claims data relating to product liability, general liability, workers compensation, injury and other liability claims and claims data relating to contracts, such as supply contract performance claims, product delivery requirements, warranty claims, indemnification claims, delivery requirements, timing requirements, milestones, key performance indicators and others); accounting data 730 (such as data relating to completion of contract requirements, satisfaction of bonds, payment of duties and tariffs, and others); and risk management data 732 (such as relating to items supplied, amounts, pricing, delivery, sources, routes, customs information and many others), among many other datatypes associated with value chain network entities 652 and applications 630.

[0364] In embodiments, the data handling layers 624 are configured in a topology that facilitates shared adaptation capabilities, which may be provided, managed, mediated and the like by one or more of a set of services, components, programs, systems, or capabilities of the adaptive intelligent systems layer 614, referred to in some cases herein for convenience as the adaptive intelligence layer 614. The adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewhere throughout this disclosure. Thus, use of various resources, such as computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for single devices or peered networks), and available cloud infrastructure, among others), data storage resources (including local storage on devices, storage resources in or on value chain entities or environments (including on-device storage, storage on asset tags, local area network storage and the like), network storage resources, cloud-based storage resources, database resources and others), networking resources (including cellular network spectrum, wireless network resources, fixed network resources and others), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others) and others may be optimized in a coordinated or shared way on behalf of an operator, enterprise, or the like, such as for the benefit of multiple applications, programs, workflows, or the like. For example, the adaptive intelligence layer 614 may manage and provision available network resources for both a supply chain management application and for a demand planning application (among many other possibilities), such that low latency resources are used for supply chain management application (where rapid decisions may be important) and longer latency resources are used for the demand planning application. As described in more detail throughout this disclosure and the documents incorporated herein by reference, a wide variety of adaptations may be provided on behalf of the various services and capabilities across the various layers 624, including ones based on application requirements, quality of service, on-time delivery, service objectives, budgets, costs, pricing, risk factors, operational objectives, efficiency objectives, optimization parameters, returns on investment, profitability, uptime / downtime, worker utilization, and many others.

[0365] The value chain management platform layer 604, referred to in some cases herein for convenience as the platform layer 604, may include, integrate with, and enable the various value chain network processes, workflows, activities, events and applications 630 described throughout this disclosure that enable an operator to manage more than one aspect of a value chain network environment or entity 652 in a common application environment (e.g., shared, pooled, similarly licenses whether shared data for one person, multiple people, or anonymized), such as one that takes advantage of common data storage in the data storage layer 624, common data collection or monitoring in the monitoring systems layer 614 and / or common adaptive intelligence of the adaptive intelligence layer 614. Outputs from the applications 630 in the platform layer 604 may be provided to the other data handing layers 624. These may include, without limitation, state and status information for various objects, entities, processes, flows and the like; object information, such as identity, attribute and parameter information for various classes of objects of various data types; event and change information, such as for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information, such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and others) among others. Outputs from each application 630 can be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614. The cross-application nature of the platform layer 604 thus facilitates convenient organization of all of the necessary infrastructure elements for adding intelligence to any given application, such as by supplying machine learning on outcomes across applications, providing enrichment of automation of a given application via machine learning based on outcomes from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604. In examples, there may be systems, components, services and other capabilities that optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or ones that may generally improve any of process and application outputs and outcomes 1040 pursued by use of the platform 604. In some examples, outputs and outcomes 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated. Some Data Storage Layer Details - Alternative Data Architectures

[0366] Referring to Fig. 12, additional details, components, sub-systems, and other elements of an optional embodiment of the data storage layer 624 of the platform 604 are illustrated. Various data architectures may be used, including conventional relational and object-oriented data architectures, blockchain architectures 1180, asset tag data storage architectures 1178, local storage architectures 1190, network storage architectures 1174, multi-tenant architectures 1132, distributed data architectures 1002, value chain network (VCN) data object architectures 1004, cluster-based architectures 1128, event data-based architectures 1034, state data-based architectures 1140, graph database architectures 1124, self-organizing architectures 1134, and other data architectures 1002.

[0367] The adaptive intelligent systems layer 614 of the platform 604 may include one or more protocol adaptors 1110 for facilitating data storage, retrieval access, query management, loading, extraction, normalization, and / or transformation to enable use of the various other data storage architectures 1002, such as allowing extraction from one form of database and loading to a data system that uses a different protocol or data structure.

[0368] In embodiments, the value chain network-oriented data storage systems layer 624 may include, without limitation, physical storage systems, virtual storage systems, local storage systems (e.g., part of the local storage architectures 1190), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of the network storage architectures 1174such as using NVME, storage attached networks, and other network storage systems), and many others.

[0369] In embodiments, the storage layer 624 may store data in one or more knowledge graphs (such as a directed acyclic graph, a data map, a data hierarchy, a data cluster including links and nodes, a self-organizing map, or the like) in the graph database architectures 1124. In example embodiments, the knowledge graph may be a prevalent example of when a graph database and graph database architecture may be used. In some examples, the knowledge graph may be used to graph a workflow. For a linear workflow, a directed acyclic graph may be used. For a contingent workflow, a cyclic graph may be used. The graph database (e.g., graph database architectures vpc608) may include the knowledge graph or the knowledge graph may be an example of the graph database. In example embodiments, the knowledge graph may include ontology and connections (e.g., relationships) between the ontology of the knowledge graph. In an example, the knowledge graph may be used to capture an articulation of knowledge domains of a human expert such that there may be an identification of opportunities to design and build robotic process automation or other intelligence that may replicate this knowledge set. The platform may be used to recognize that a type of expert is using this factual knowledge base (from the knowledge graph) coupled with competencies that may be replicable by artificial intelligence that may be different depending on type of expertise involved. For example, artificial intelligence such as a convolutional neural network may be used with spatiotemporal aspects that may be used to diagnose issues or packing up a box in a warehouse. Whereas the platform may use a different type of knowledge graph for a self-organizing map of an expert whose main job is to segment customers into customer segmentation groups. In some examples, the knowledge graph may be built from various data such as job credentials, job listings, parsing output deliverables. In embodiments, the data storage layer 624 may store data in a digital thread, ledger, or the like, such as for maintaining a serial or other records of an entities 652 over time, including any of the entities described herein. In embodiments, the data storage layer 624 may use and enable an asset tag 1178, which may include a data structure that is associated with an asset and accessible and managed, such as by use of access controls, so that storage and retrieval of data is optionally linked to local processes, but also optionally open to remote retrieval and storage options. In embodiments, the storage layer 624 may include one or more blockchains 1180, such as ones that store identity data, transaction data, historical interaction data, and the like, such as with access control that may be role-based or may be based on credentials associated with a value chain entity 652, a service, or one or more applications 630. Data stored by the data storage systems 624 may include accounting and other financial data 730, access data 734, asset and facility data 1030 (such as for any of the value chain assets and facilities described herein), asset tag data 1178, worker data 1032, event data 1034, risk management data 732, pricing data 738, safety data 664 and many other types of data that may be associated with, produced by, or produced about any of the value chain entities and activities described herein and in the documents incorporated by reference. Adaptive Intelligent Systems and Monitoring Layers

[0370] Referring to Fig. 13, additional details, components, sub-systems, and other elements of an optional embodiment of the platform 604 are illustrated. The management platform 604 may, in various optional embodiments, include the set of applications 630, by which an operator or owner of a value chain network entity, or other users, may manage, monitor, control, analyze, or otherwise interact with one or more elements of a value chain network entity 652, such as any of the elements noted in connection above and throughout this disclosure.

[0371] In embodiments, the adaptive intelligent systems layer 614 may include a set of systems, components, services and other capabilities that collectively facilitate the coordinated development and deployment of intelligent systems, such as ones that can enhance one or more of the applications 630 at the application platform layer 604; ones that can improve the performance of one or more of the components, or the overall performance (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors) of the connectivity facilities 642; ones that can improve other capabilities within the adaptive intelligent systems layer 614; ones that improve the performance (e.g., speed / latency, energy utilization, storage capacity, storage efficiency, reliability, security, or the like) of one or more of the components, or the overall performance, of the value chain network-oriented data storage systems 624; ones that optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or ones that generally improve any of the process and application outputs and outcomes 1040 pursued by use of the platform 604.

[0372] These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adaptors 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction management systems 1900 (such as for setting up, provisioning, configuring and otherwise managing sets of interactions between and among sets of value chain network entities 652 in the value chain network 668), and other systems.

[0373] In embodiments, the value chain monitoring systems layer 614 and its data collection systems 640 may include a wide range of systems for the collection of data. This layer may include, without limitation, real time monitoring systems 1520 (such as onboard monitoring systems like event and status reporting systems on ships and other floating assets, on delivery vehicles, on trucks and other hauling assets, and in shipyards, ports, warehouses, distribution centers and other locations; on-board diagnostic (OBD) and telematics systems on floating assets, vehicles and equipment; systems providing diagnostic codes and events via an event bus, communication port, or other communication system; monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, person tracking systems, and ambient sensing systems located in various environments where value chain activities and other events take place), as well as removable and replaceable monitoring systems, such as portable and mobile data collectors, RFID and other tag readers, smart phones, tablets and other mobile devices that are capable of data collection and the like); software interaction observation systems 1500 (such as for logging and tracking events involved in interactions of users with software user interfaces, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigation actions, eye movements, finger movements, gestures, menu selections, and many others, as well as software interactions that occur as a result of other programs, such as over APIs, among many others); mobile data collectors 1170 (such as described extensively herein and in documents incorporated by reference), visual monitoring systems 1930 (such as using video and still imaging systems, LIDAR, IR and other systems that allow visualization of items, people, materials, components, machines, equipment, personnel, gestures, expressions, positions, locations, configurations, and other factors or parameters of entities 652, as well as inspection systems that monitor processes, activities of workers and the like); point of interaction systems 1530 (such as dashboards, user interfaces, and control systems for value chain entities); physical process observation systems 1510 (such as for tracking physical activities of operators, workers, customers, or the like, physical activities of individuals (such as shippers, delivery workers, packers, pickers, assembly personnel, customers, merchants, vendors, distributors and others), physical interactions of workers with other workers, interactions of workers with physical entities like machines and equipment, and interactions of physical entities with other physical entities, including, without limitation, by use of video and still image cameras, motion sensing systems (such as including optical sensors, LIDAR, IR and other sensor sets), robotic motion tracking systems (such as tracking movements of systems attached to a human or a physical entity) and many others; machine state monitoring systems 1940 (including onboard monitors and external monitors of conditions, states, operating parameters, or other measures of the condition of any value chain entity, such as a machine or component thereof, such as a machine, such as a client, a server, a cloud resource, a control system, a display screen, a sensor, a camera, a vehicle, a robot, or other machine); sensors and cameras 1950 and other loT data collection systems 1172 (including onboard sensors, sensors or other data collectors (including click tracking sensors) in or about a value chain environment (such as, without limitation, a point of origin, a loading or unloading dock, a vehicle or floating asset used to convey goods, a container, a port, a distribution center, a storage facility, a warehouse, a delivery vehicle, and a point of destination), cameras for monitoring an entire environment, dedicated cameras for a particular machine, process, worker, or the like, wearable cameras, portable cameras, cameras disposed on mobile robots, cameras of portable devices like smart phones and tablets, and many others, including any of the many sensor types disclosed throughout this disclosure or in the documents incorporated herein by reference); indoor location monitoring systems 1532 (including cameras, IR systems, motiondetection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectrum detection systems, time-of-flight systems, chemical noses and other chemical sensor sets, as well as other sensors); user feedback systems 1534 (including survey systems, touch pads, voice-based feedback systems, rating systems, expression monitoring systems, affect monitoring systems, gesture monitoring systems, and others); behavioral monitoring systems 1538 (such as for monitoring movements, shopping behavior, buying behavior, clicking behavior, behavior indicating fraud or deception, user interface interactions, product return behavior, behavior indicative of interest, attention, boredom or the like, mood-indicating behavior (such as fidgeting, staying still, moving closer, or changing posture) and many others); and any of a wide variety of Internet of Things (loT) data collectors 1172, such as those described throughout this disclosure and in the documents incorporated by reference herein.

[0374] In embodiments, the value chain monitoring systems layer 614 and its data collection systems 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652, such as any of the entities described throughout this disclosure. This may include components or sub-systems for searching for entities within the value chain network 668, such as by device identifier, by network location, by geolocation (such as by geofence), by indoor location (such as by proximity to known resources, such as loT-enabled devices and infrastructure, Wifi routers, switches, or the like), by cellular location (such as by proximity to cellular towers), by identity management systems (such as where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise by an identifier that is assigned by and / or managed by the platform 604), and the like. Entity discovery 1900 may initiate a handshake among a set of devices, such as to initiate interactions that serve various applications 630 or other capabilities of the platform 604.

[0375] Referring to Fig. 14, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, and the like) for a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain. The adaptive intelligence systems 614 may deliver artificial intelligence 1160 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the sets of value chain applications (e.g., demand management applications 824 and supply chain applications 812). The adaptive intelligence systems 614 may include artificial intelligence, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.

[0376] In embodiments, user interface may include interfaces for configuring an artificial intelligence system 1160 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and supply them, such as to a neural network, artificial intelligence system 1160 or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010. In embodiments, the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes relating to the value chain, the category of goods 3010 and the like.

[0377] In embodiments, providing coordinated intelligence may include providing artificial intelligence capabilities, such as artificial intelligence systems 1160 and the like. Artificial intelligence systems may facilitate coordinated intelligence for the set of demand management applications 824 or the set of supply chain applications 812 or both, such as for a category of goods, such as by processing data that is available in any of the data sources of the value chain, such as value chain processes, bills of materials, manifests, delivery schedules, weather data, traffic data, goods design specifications, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like.

[0378] In embodiments, the user interface 3020 may provide access to, among other things artificial intelligence capabilities, applications, systems and the like for coordinating intelligence for applications of the value chain and particularly for value chain applications for the category of goods 3010. The user interface 3020 may be adapted to receive information descriptive of the category of goods 3010 and configure user access to the artificial intelligence capabilities responsive thereto, so that the user, through the user interface is guided to artificial intelligence capabilities that are suitable for use with value chain applications (e.g., the set of demand management applications 824 and supply chain applications 812) that contribute to goods / services in the category of goods 3010. The user interface 3020 may facilitate providing coordinated intelligence that comprises artificial intelligence capabilities that provide coordinated intelligence for a specific operator and / or enterprise that participates in the supply chain for the category of goods.

[0379] In embodiments, the user interface 3020 may be configured to facilitate the user selecting and / or configuring multiple artificial intelligence systems 1160 for use with the value chain. The user interface may present the set of demand management applications 824 and supply chain applications 812 as connected entities that receive, process, and produce outputs each of which may be shared among the applications. Types of artificial intelligence systems 1160 may be indicated in the user interface 3020 responsive to sets of connected applications or their data elements being indicated in the user interface, such as by the user placing a pointer proximal to a connected set of applications and the like. In embodiments, the user interface 3020 may facilitate access to the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

[0380] The adaptive intelligence systems 614 may be configured with data processing, artificial intelligence and computational systems 634 that may operate cooperatively to provide coordinated intelligence, such as when an artificial intelligence system 1160 operates on or responds to data collected by or produced by other systems of the adaptive intelligence systems 614, such as a data processing system and the like. In embodiments,...

Claims

1. An information technology system, comprising:a cloud-based management platform with a micro-services architecture, the platform having:a set of interfaces that are configured to access and configure features of the platform;a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform;a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform;a set of data storage facilities that are configured to store data collected and handled by the platform, wherein the data is related to at least one of the value chain network entities and the features of the platform; anda set of monitoring facilities that are configured to monitor the value chain network entities;wherein the platform is configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

2. The system of claim 1, wherein the set of interfaces includes at least one of a demand management interface and a supply chain management interface.

3. The system of claim 1, wherein the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise.

4. The system of claim 1, wherein the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise.

5. The system of claim 1, wherein the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise.

6. The system of claim 1, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise.

7. The system of claim 1, wherein the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise.

8. The system of claim 1, wherein the set of adaptive intelligence facilities includes a robotic process automation system.

9. The system of claim 1, wherein the set of adaptive intelligence facilities includes a selfconfiguring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.

10. The system of claim 1, wherein the set of adaptive intelligence facilities includes a digital twin system representing attributes of at least one value chain network entity of the value chain network entities controlled by the enterprise.

11. The system of claim 1, wherein the set of adaptive intelligence includes a smart contract system that is configured to automate a set of interactions among the value chain network entities.

12. The system of claim 1, wherein the set of data storage facilities uses a distributed data architecture.

13. The system of claim 1, wherein the set of data storage facilities uses a blockchain.

14. The system of claim 1, wherein the set of data storage facilities uses a distributed ledger.

15. The system of claim 1, wherein the set of data storage facilities uses a graph database representing a set of hierarchical relationships of the value chain network entities.

16. The system of claim 1, wherein the set of monitoring facilities includes an Internet of Things monitoring system.

17. The system of claim 1, wherein the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by the enterprise.

18. The system of claim 1, wherein the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications, and enterprise resource management applications.

19. The system of claim 1, wherein the set of applications includes an asset management application.

20. The system of claim 1, wherein the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems,floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, dr ones / robots / AVs, waterways, and port infrastructure facilities.

21. The system of claim 1, wherein the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

22. The system of claim 21, wherein the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

23. The system of claim 21, wherein the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

24. The system of claim 21, wherein the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

25. The system of claim 1, wherein the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

26. An information technology system, comprising:a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, whereinthe interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities;a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; anda unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform.

27. The system of claim 26, wherein the unified set of robotic process automation systems automate a process selected from the group consisting of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a set of products for compatibility, configuration of a request for proposals, ordering of equipment for a warehouse, ordering of equipment for a fulfillment center, classification of a product defect in an image, inspection of a product in an image, inspection of product quality data from a set of sensors, inspection of data from a set of onboard diagnostics on a. product, inspection of diagnostic data from an Internet of Things system, review of sensor data from environmental sensors in a set of supply chain environments, selection of inputs for a digital twin, selection of outputs from a digital twin, selection of visual elements for presentation in a digital twin, diagnosis of sources of delay in a supply chain, diagnosis of sources of scarcity in a supply chain, diagnosis of sources of congestion in a supply chain, diagnosis of sources of cost overruns in a supply chain, diagnosis of sources of product defects in a supply chain, and prediction of maintenance requirements in supply chain infrastructure.

28. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of a quantity of product for an order.

29. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of a carrier for a shipment.

30. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of a vendor for a component.

31. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of a vendor for a finished goods order.

32. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of a variation of a product for marketing.

33. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of an assortment of goods for a shelf.

34. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves determination of a price for a finished good.

35. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a service offer related to a product.

36. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a product bundle.

37. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a product kit.

38. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a product package.

39. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a product display.

40. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a product image.

41. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a product description.

42. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a website navigation path related to a product.

43. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves determination of an inventory level for a product.

44. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of a logistics type.

45. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a schedule for product delivery.

46. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a logistics schedule.

47. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a set of inputs for machine learning.

48. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves preparation of product documentation.

49. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves preparation of disclosures about a product.

50. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a product for a set of local requirements.

51. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a set of products for compatibility.

52. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves configuration of a request for proposals.

53. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves ordering of equipment for a warehouse.

54. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves ordering of equipment for a fulfillment center.

55. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves classification of a product defect in an image.

56. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves inspection of a product in an image.

57. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves inspection of product quality data from a set of sensors.

58. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves inspection of data from a set of onboard diagnostics on a. product.

59. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves inspection of diagnostic data from an Internet of Things system.

60. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves review of sensor data from environmental sensors in a set of supply chain environments.

61. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of inputs for a digital twin.

62. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of outputs from a digital twin.

63. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves selection of visual elements for presentation in a digital twin.

64. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves diagnosis of sources of delay in a supply chain.

65. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves diagnosis of sources of scarcity in a supply chain.

66. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves diagnosis of sources of congestion in a supply chain.

67. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves diagnosis of sources of cost overruns in a supply chain.

68. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves diagnosis of sources of product defects in a supply chain.

69. The system of claim 27, wherein one of the processes automated by the robotic process automation system involves prediction of maintenance requirements in supply chain infrastructure.

70. The system of claim 26, wherein the set of demand management applications, supply chain applications, intelligent product applications and enterprise resource management applications are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, selforganization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleetmanagement, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

71. The system of claim 26, wherein the set of interfaces includes at least one of a demand management interface and a supply chain management interface.

72. The system of claim 26, wherein the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise.

73. The system of claim 26, wherein the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise.

74. The system of claim 26, wherein the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise.

75. The system of claim 26, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise.

76. The system of claim 26, wherein the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise.

77. The system of claim 26, wherein the set of adaptive intelligence facilities includes a robotic process automation system.

78. The system of claim 26, wherein the set of adaptive intelligence facilities includes a selfconfiguring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.

79. The system of claim 26, wherein the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise.

80. The system of claim 26, wherein the set of adaptive intelligence facilities includes a smart contract system that is configured to automate a set of interactions among a set of value chain network entities.

81. The system of claim 26, wherein the set of data storage facilities uses a distributed data architecture.

82. The system of claim 26, wherein the set of data storage facilities uses a blockchain.

83. The system of claim 26, wherein the set of data storage facilities uses a distributed ledger.

84. The system of claim 26, wherein the set of data storage facilities uses graph database representing a set of hierarchical relationships of the value chain network entities.

85. The system of claim 26, wherein the set of monitoring facilities includes an Internet of Things monitoring system.

86. The system of claim 26, wherein the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.

87. The system of claim 26, wherein the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications.

88. The system of claim 26, wherein the set of applications includes an asset management application.

89. The system of claim 26, wherein the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, dr ones / robots / AVs, waterways, and port infrastructure facilities.

90. The system of claim 26, wherein the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

91. The system of claim 90, wherein the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillmentcenter location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

92. The system of claim 90, wherein the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

93. The system of claim 90, wherein the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

94. The system of claim 26, wherein the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control,automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

95. An information technology system, comprising:a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities;a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; anda set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application, wherein the microservices layers include a data collection layer that collects information from a set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities related to the value chain network entities of the platform.

96. The system of claim 95, wherein the set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities collects information from entities selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverselogistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones / robots / AVs, waterways, and port infrastructure facilities.

97. The system of claim 95, wherein the set of Internet of Things resources is selected from the group consisting of camera systems, lighting systems, motion sensing systems, weighing systems, inspection systems, machine vision systems, environmental sensor systems, onboard sensor systems, onboard diagnostic systems, environmental control systems, sensor-enabled network switching and routing systems, RF sensing systems, magnetic sensing systems, pressure monitoring systems, vibration monitoring systems, temperature monitoring systems, heat flow monitoring systems, biological measurement systems, chemical measurement systems, ultrasonic monitoring systems, radiography systems, LIDAR-based monitoring systems, access control systems, penetrating wave sensing systems, SONAR-based monitoring systems, radar-based monitoring systems, computed tomography systems, magnetic resonance imaging systems, and network monitoring systems.

98. The system of claim 95, wherein the set of Internet of Things resources includes a set of camera systems.

99. The system of claim 95, wherein the set of Internet of Things resources includes a set of lighting systems.

100. The system of claim 95, wherein the set of Internet of Things resources includes a set of machine vision systems.

101. The system of claim 95, wherein the set of Internet of Things resources includes a set of motion sensing systems.

102. The system of claim 95, wherein the set of Internet of Things resources includes a set of weighing systems.

103. The system of claim 95, wherein the set of Internet of Things resources includes a set of inspection systems.

104. The system of claim 95, wherein the set of Internet of Things resources includes a set of environmental sensor systems.

105. The system of claim 95, wherein the set of Internet of Things resources includes a set of onboard sensor systems.

106. The system of claim 95, wherein the set of Internet of Things resources includes a set of onboard diagnostic systems.

107. The system of claim 95, wherein the set of Internet of Things resources includes a set of environmental control systems.

108. The system of claim 95, wherein the set of Internet of Things resources includes a set of sensor-enabled network switching and routing systems.

109. The system of claim 95, wherein the set of Internet of Things resources includes a set of RF sensing systems.

110. The system of claim 95, wherein the set of Internet of Things resources includes a set of magnetic sensing systems.

111. The system of claim 95, wherein the set of Internet of Things resources includes a set of pressure monitoring systems.

112. The system of claim 95, wherein the set of Internet of Things resources includes a set of vibration monitoring systems.

113. The system of claim 95, wherein the set of Internet of Things resources includes a set of temperature monitoring systems.

114. The system of claim 95, wherein the set of Internet of Things resources includes a set of heat flow monitoring systems.

115. The system of claim 95, wherein the set of Internet of Things resources includes a set of biological measurement systems.

116. The system of claim 95, wherein the set of Internet of Things resources includes a set of chemical measurement systems.

117. The system of claim 95, wherein the set of Internet of Things resources includes a set of ultrasonic monitoring systems.

118. The system of claim 95, wherein the set of Internet of Things resources includes a set of radiography systems.

119. The system of claim 95, wherein the set of Internet of Things resources includes a set of LIDAR-based monitoring systems.

120. The system of claim 95, wherein the set of Internet of Things resources includes a set of access control systems.

121. The system of claim 95, wherein the set of Internet of Things resources includes a set of penetrating wave sensing systems.

122. The system of claim 95, wherein the set of Internet of Things resources includes a set of SONAR-based monitoring systems.

123. The system of claim 95, wherein the set of Internet of Things resources includes a set of radar-based monitoring systems.

124. The system of claim 95, wherein the set of Internet of Things resources includes a set of computed tomography systems.

125. The system of claim 95, wherein the set of Internet of Things resources includes a set of magnetic resonance imaging systems.

126. The system of claim 95, wherein the set of Internet of Things resources includes a set of network monitoring systems.

127. The system of claim 95, wherein the set of interfaces includes at least one of a demand management interface and a supply chain management interface.

128. The system of claim 95, wherein the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, selfhealing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

129. The system of claim 95, wherein the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise.

130. The system of claim 95, wherein the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise.

131. The system of claim 95, wherein the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise.

132. The system of claim 95, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise.

133. The system of claim 95, wherein the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise.

134. The system of claim 95, wherein the set of adaptive intelligence facilities includes a robotic process automation system.

135. The system of claim 95, wherein the set of adaptive intelligence includes a selfconfiguring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.

136. The system of claim 95, wherein the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise.

137. The system of claim 95, wherein the set of adaptive intelligence facilities includes a smart contract system that is configured to automate a set of interactions among a set of value chain network entities.

138. The system of claim 95, wherein the set of data storage facilities uses a distributed data architecture.

139. The system of claim 95, wherein the set of data storage facilities uses a blockchain.

140. The system of claim 95, wherein the set of data storage facilities uses a distributed ledger.

141. The system of claim 95, wherein the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities.

142. The system of claim 95, wherein the set of monitoring includes an Internet of Things monitoring system.

143. The system of claim 95, wherein the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.

144. The system of claim 95, wherein the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demandmanagement applications, intelligent product applications and enterprise resource management applications.

145. The system of claim 95, wherein the set of applications includes an asset management application.

146. The system of claim 95, wherein the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, dr ones / robots / AVs, waterways, and port infrastructure facilities.

147. The system of claim 95, wherein the platform manages a set of demand factors, a set of supply factors and a set of supply chain infrastructure facilities.

148. The system of claim 147, wherein the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

149. The system of claim 147, wherein the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, groupdemand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

150. The system of claim 147, wherein the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

151. The system of claim 95, wherein the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shippingmanagement, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

152. An information technology system, comprising:a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities;a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; anda set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application, wherein the microservices layers include a robotic process automation layer that uses information collected by a data collection layer and a set of outcomes and activities involving the applications of the application layer to automate a set of actions for at least a subset of the applications with respect to the value chain network entities of the platform.

153. The system of claim 152, wherein the robotic process automation layer automates a process selected from the group consisting of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a setof products for compatibility, configuration of a request for proposals, ordering of equipment for a warehouse, ordering of equipment for a fulfillment center, classification of a product defect in an image, inspection of a product in an image, inspection of product quality data from a set of sensors, inspection of data from a set of onboard diagnostics on a. product, inspection of diagnostic data from an Internet of Things system, review of sensor data from environmental sensors in a set of supply chain environments, selection of inputs for a digital twin, selection of outputs from a digital twin, selection of visual elements for presentation in a digital twin, diagnosis of sources of delay in a supply chain, diagnosis of sources of scarcity in a supply chain, diagnosis of sources of congestion in a supply chain, diagnosis of sources of cost overruns in a supply chain, diagnosis of sources of product defects in a supply chain, and prediction of maintenance requirements in supply chain infrastructure.

154. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of a quantity of product for an order.

155. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of a carrier for a shipment.

156. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of a vendor for a component.

157. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of a vendor for a finished goods order.

158. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of a variation of a product for marketing.

159. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of an assortment of goods for a shelf.

160. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves determination of a price for a finished good.

161. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a service offer related to a product.

162. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of product bundle.

163. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a product kit.

164. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a product package.

165. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a product display.

166. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a product image.

167. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a product description.

168. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a website navigation path related to a product.

169. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves determination of an inventory level for a product.

170. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of a logistics type.

171. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a schedule for product delivery.

172. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a logistics schedule.

173. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a set of inputs for machine learning.

174. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves preparation of product documentation.

175. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves preparation of disclosures about a product.

176. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a product for a set of local requirements.

177. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a set of products for compatibility.

178. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves configuration of a request for proposals.

179. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves ordering of equipment for a warehouse.

180. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves ordering of equipment for a fulfillment center.

181. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves classification of a product defect in an image.

182. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves inspection of a product in an image.

183. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves inspection of product quality data from a set of sensors.

184. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves inspection of data from a set of onboard diagnostics on a. product.

185. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves inspection of diagnostic data from an Internet of Things system.

186. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves review of sensor data from environmental sensors in a set of supply chain environments.

187. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of inputs for a digital twin.

188. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of outputs from a digital twin.

189. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves selection of visual elements for presentation in a digital twin.

190. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of delay in a supply chain.

191. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of scarcity in a supply chain.

192. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of congestion in a supply chain.

193. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of cost overruns in a supply chain.

194. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of product defects in a supply chain.

195. The system of claim 152, wherein one of the actions automated by the robotic process automation layer involves prediction of maintenance requirements in supply chain infrastructure.

196. The system of claim 152, wherein the set of interfaces includes at least one of a demand management interface and a supply chain management interface.

197. The system of claim 152, wherein the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, andenterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, selfhealing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

198. The system of claim 152, wherein the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise.

199. The system of claim 152, wherein the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise.

200. The system of claim 152, wherein the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise.

201. The system of claim 152, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise.

202. The system of claim 152, wherein the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise.

203. The system of claim 152, wherein the set of adaptive intelligence facilities includes a robotic process automation system.

204. The system of claim 152, wherein the set of adaptive intelligence facilities includes a selfconfiguring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.

205. The system of claim 152, wherein the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise.

206. The system of claim 152, wherein the set of adaptive intelligence facilities includes a smart contract system for automating a set of interactions among a set of value chain network entities.

207. The system of claim 152, wherein the set of data storage facilities uses a distributed data architecture.

208. The system of claim 152, wherein the set of data storage facilities uses a blockchain.

209. The system of claim 152, wherein the set of data storage facilities uses a distributed ledger.

210. The system of claim 152, wherein the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities.

211. The system of claim 152, wherein the set of monitoring facilities includes an Internet of Things monitoring system.

212. The system of claim 152, wherein the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.

213. The system of claim 152, wherein the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications.

214. The system of claim 152, wherein the set of applications includes an asset management application.

215. The system of claim 152, wherein the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use,networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, dr ones / robots / AVs, waterways, and port infrastructure facilities.

216. The system of claim 152, wherein the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

217. The system of claim 216, wherein the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

218. The system of claim 216, wherein the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

219. The system of claim 216, wherein the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane,container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

220. The system of claim 152, wherein the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

221. An information technology system, comprising:a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the datastorage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities;a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; anda machine leaming / artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and / or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.

222. The system of claim 221, wherein the set of interfaces includes at least one of a demand management interface and a supply chain management interface.

223. The system of claim 221, wherein the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, selfhealing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

224. The system of claim 221, wherein the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise.

225. The system of claim 221, wherein the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise.

226. The system of claim 221, wherein the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise.

227. The system of claim 221, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise.

228. The system of claim 221, wherein the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise.

229. The system of claim 221, wherein the set of adaptive intelligence facilities includes a robotic process automation system.

230. The system of claim 221, wherein the set of adaptive intelligence facilities includes a selfconfiguring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.

231. The system of claim 221, wherein the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise.

232. The system of claim 221, wherein the set of adaptive intelligence facilities includes a smart contract system for automating a set of interactions among a set of value chain network entities.

233. The system of claim 221, wherein the set of data storage facilities uses a distributed data architecture.

234. The system of claim 221, wherein the set of data storage facilities uses a blockchain.

235. The system of claim 221, wherein the set of data storage facilities uses a distributed ledger.

236. The system of claim 221, wherein the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities.

237. The system of claim 221, wherein the set of monitoring facilities includes an Internet of Things monitoring system.

238. The system of claim 221, wherein the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.

239. The system of claim 221, wherein the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications.

240. The system of claim 221, wherein the set of applications includes an asset management application.

241. The system of claim 221, wherein the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, dr ones / robots / AVs, waterways, and port infrastructure facilities.

242. The system of claim 221, wherein the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

243. The system of claim 242, wherein the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.

244. The system of claim 242, wherein the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need forreplacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.

245. The system of claim 242, wherein the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.

246. The system of claim 221, wherein the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kitmodification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertisement, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

247. A value chain system that provides container fleet management decisions comprising:a machine learning system that trains a machine-learned model that outputs a container fleet management decision given a respective set of input features relating to a specific shipping event, wherein the machine learning system trains the machine-learned model based on training data sets that define features of previous shipping events and outcomes of the shipping events;an artificial intelligence system that receives a request for container fleet management and determines a container fleet management decision based on the machine-learned model and the request; anda digital twin system that generates an environment digital twin of an environment of a container fleet and one or more container digital twins of respective containers in the container fleet, wherein the digital twin system:executes a container fleet simulation based on the environment digital twin and the one or more container digital twins,issues a container fleet management request from the artificial intelligence system based on a state of the container fleet simulation; andadjusts the state of the container fleet simulation based on the container fleet management decision output by the artificial intelligence system in response to the container fleet management request.

248. The value chain system of claim 247, wherein the digital twin system outputs a simulation outcome to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the container fleet management decision based on the simulation outcome.

249. The value chain system of claim 247, wherein the artificial intelligence system receives the container fleet management request from the digital twin system and determines the container fleet management decision based on simulation features defined in the container fleet management request, wherein the simulation features are indicative of the state of the container fleet simulation.

250. The value chain system of claim 247, wherein the request for container fleet management includes one or more properties of a simulated shipping event.

251. The value chain system of claim 250, wherein the artificial intelligence system determines the container fleet management decision based on the one or more properties of the simulated shipping event and the machine-learned model.

252. The value chain system of claim 250, wherein the one or more properties include a type of good being shipped.

253. The value chain system of claim 250, wherein the one or more properties include a source and a destination of a container.

254. The value chain system of claim 247, wherein the digital twin system provides outcome data to the machine learning system, wherein the outcome data defines a simulation outcome resulting from the container fleet management decision.

255. A value chain system that provides recommendations for designing a logistics system comprising:a machine learning system that trains a machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the machine-learned model based on training data sets that define features of logistics systems and outcomes of the logistics systems;an artificial intelligence system that receives a request for logistics system design and determines a logistics system design recommendation based on the machine-learned model and the request; anda digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation and one or more physical asset digital twins of physical assets, wherein the digital twin system:executes a logistics simulation based on the logistics environment digital twin and the one or more physical asset digital twins,issues a logistics system design request from the artificial intelligence system based on a state of the logistics simulation; andadjusts the state of the logistics simulation based on the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request.

256. The value chain system of claim 255, wherein the digital twin system outputs a graphical representation of the environment digital twin to a display, whereby a user views the simulation via the display.

257. The value chain system of claim 255, wherein the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning systemreinforces the machine-learned model used to determine the logistics system design recommendation based on the simulation outcome.

258. The value chain system of claim 255, wherein the artificial intelligence system receives the request from a logistics design system that designs logistics systems, wherein the request includes one or more logistics factors corresponding to a proposed logistics solution of an organization.

259. The value chain system of claim 258, wherein the logistics factors include one or more of: a type of product corresponding to the proposed logistics solution, one or more features of the type of product, a location of a manufacturing site, a location of a distribution facility, a location of a warehouse, a location of a customer base, proposed expansion areas of the organization, and supply chain features.

260. The value chain system of claim 255, wherein the logistics design system provides outcome data relating to the logistics system design recommendation to the machine learning system, and the machine learning system reinforces the machine-learned model that are used to determine the logistics system design recommendation based on the outcome data.

261. The value chain system of claim 255, wherein the artificial intelligence system determines the logistics system design recommendation to minimize delay times.

262. The value chain system of claim 255, wherein the artificial intelligence system determines the logistics system design recommendation to comply with regulatory requirements.

263. A value chain system that designs packaging comprising:a machine learning system that trains a machine-learned model that outputs a packaging design recommendation given a respective set of input features relating to a specific respective packaging design, wherein the machine learning system trains the machine-learned model based on training data sets that define features of packaging designs and outcomes of the packaging designs;an artificial intelligence system that receives a request for packaging design and determines a packaging design recommendation based on the machine-learned model and the request; anda digital twin system that generates a package digital twin of a package that incorporates the packaging design recommendation, wherein the digital twin system:executes a packaging simulation based on the package digital twin;issues a packaging design request from the artificial intelligence system based on a state of the logistics simulation; andadjusts the state of the logistics simulation based on the packaging design recommendation output by the artificial intelligence system in response to the packaging design request.

264. The value chain system of claim 263, wherein the digital twin system outputs a graphical representation of the package digital twin to a display, whereby a user views the simulation via the display.

265. The value chain system of claim 263, wherein the digital twin system outputs a graphical representation of the package digital twin in a graphical user interface, whereby a user edits the packaging design via the graphical user interface.

266. The value chain system of claim 263, wherein the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the packaging design recommendation based on the simulation outcome.

267. The value chain system of claim 263, wherein the artificial intelligence system receives the request from a packaging design system that designs packaging for physical objects, wherein the request includes one or more packaging factors corresponding to a proposed packaging design for the physical objects.

268. The value chain system of claim 267, wherein the packaging factors include one or more of: a type of the physical objects, dimensions of the physical objects, masses of the physical objects, and shipping methods of the physical objects.

269. The value chain system of claim 263, wherein the packaging design system provides outcome data relating to the packaging design recommendation to the machine learning system, and the machine learning system reinforces the machine-learned model that are used to determine the packaging design recommendation based on the outcome data.

270. The value chain system of claim 263, wherein the artificial intelligence system determines the packaging design recommendation to minimize damage.

271. The value chain system of claim 263, wherein the artificial intelligence system determines the packaging design recommendation to minimize costs.

272. The value chain system of claim 263, wherein the artificial intelligence system determines the packaging design recommendation to mitigate environmental impact.

273. An information technology system for leveraging digital twins in a value chain having a plurality of value chain entities, the information technology system comprising:a plurality of sensors positioned at least one of in, on, and near a set of value chain entities of the value chain entities and configured to collect sensor data related to the set of value chain entities, the sensor data being substantially real-time sensor data; andan adaptive intelligence system connected to the plurality of sensors and configured to receive the sensor data from the plurality of sensors, the adaptive intelligence system including:an artificial intelligence system configured to input the sensor data into a machine learning model such that the sensor data is used as training data for the machine learning model, and the machine learning model is configured to transform the sensor data into simulation data; anda digital twin system configured to create a digital replica of the set of value chain entities based on the simulation data, wherein the digital replica of the value chain entities is configured to be used to provide a substantially real-time representation of the value chain entities and provide a simulation of a possible future state of the value chain entities via the simulation data.

274. The information technology system of claim 273, wherein the machine learning model is configured to leam which types of sensor data are relevant to dynamics of each value chain entity of the value chain entities and simulation thereof.

275. The information technology system of claim 273, wherein the machine learning model is configured to make suggestions to a user of the information technology system via an interface regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system.

276. The information technology system of claim 273, wherein the machine learning model is configured to prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof.

277. A value chain network management platform, comprising:a machine learning system that trains one or more machine-learned models to output one or more e-commerce recommendations to a value chain network customer via an interface using training data that includes product features and outcomes; andan artificial intelligence system that receives a request for e-commerce from an ecommerce system, wherein the artificial intelligence is configured to determine and generate an e-commerce recommendation based on the one or more machine-learned models and the request, and the artificial intelligence is configured to leverage one or more product digital twins and one or more customer digital twins to execute a simulation based on the one or more customer digital twins, the one or more product digital twins, and the e-commerce recommendation.

278. The value chain network management platform of claim 277, wherein the machine learning system integrates with a model interpretability system, and wherein the modelinterpretability system is configured to implement Testing with Concept Activation Vectors (TCAV) functionality, whereby the model interpretability facilitates learning of human-interpretable concepts by the machine-learned model.

279. The value chain network management platform of claim 277, wherein the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more customer profile digital twins.

280. A value chain network management platform comprising:a machine learning system that trains one or more machine-learned models to output one or more risk management decisions using training data that includes component features and outcomes; andan artificial intelligence system that receives a request for risk management from a risk management system, wherein the artificial intelligence system is configured to determine and generate a risk management decision based on the one or more machine-learned models and the request, and the artificial intelligence system is configured to leverage one or more component digital twins and one or more environment digital twins to execute a simulation based on the one or more component digital twins, the one or more environment digital twins, and the risk management decision.

281. The value chain network management platform of claim 280, wherein the risk management decision relates to a condition of a component.

282. The value chain network management platform of claim 280, wherein the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more components.

283. An information technology system comprising:a value chain network management platform having an asset management application associated with maritime assets, wherein the platform comprises:a data handling layer including data sources containing information used to populate a training set based on a set of maritime activities of one or more of the maritime assets and at least one of design outcomes, parameters, and data associated with the one or more of the maritime assets;an artificial intelligence system that is configured to leam on the training set collected from the data sources, wherein the artificial intelligence system is configured to simulate one or more attributes of the one or more of the maritime assets, and the artificial intelligence system is configured to generate one or more sets of recommendations for a change in the one or more attributes based on the training set collected from the data sources;a digital twin system that is configured to provide for visualization of a digital twin of the one or more of the maritime assets including detail generated by the artificial intelligence system of the one or more attributes in combination with the one or more generated sets of recommendations.

284. The system of claim 283, wherein the maritime assets include one or more container ships, and wherein the digital twin system further provides for visualization of the digital twin of the one or more container ships including the one or more attributes in combination with one or more of the sets of recommendations associated with the container ships.

285. The system of claim 283, wherein the maritime assets include one or more barges, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the barges including the one or more attributes in combination with one or more of the sets of recommendations associated with the barges.

286. The system of claim 283, wherein the maritime assets include one or more components of a port infrastructure installed on or adjacent to land, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of port infrastructure including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of port infrastructure.

287. The system of claim 286, wherein the maritime assets also include a container ship moored to a component of the port infrastructure.

288. The system of claim 283, wherein the maritime assets include one or more moored navigation units deployed on water.

289. The system of claim 283, wherein the maritime assets include one or more ships each connected to a barge.

290. The system of claim 283, wherein the maritime assets are associated with a real-world maritime port, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of the real-world maritime port including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of the real-world maritime port.

291. The system of claim 283, wherein the maritime assets are associated with a real-world shipyard, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of the real-world shipyard including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of the real-world shipyard.

292. The system of claim 283, wherein the digital twin of one or more of the maritime assets is a floating asset twin associated with a ship.

293. The system of claim 292, wherein the floating asset twin is configured to provide for visualization of a navigation course of the ship relative to a planned course of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the navigation course of the ship.

294. The system of claim 292, wherein the floating asset twin is configured to provide for visualization of an engine performance of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the engine performance of the ship.

295. The system of claim 294, wherein the visualization of the engine performance includes an emissions profile of the ship.

296. The system of claim 292, wherein the floating asset twin is configured to provide for visualization of a hull integrity of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in maintenance of the hull of the ship.

297. The system of claim 292, wherein the floating asset twin is configured to provide for visualization of in-situ hydrodynamic changes to a portion of a hull disposed below a water line of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in a hydrodynamic surface to change performance of the ship.

298. The system of claim 297, wherein the floating asset twin is configured to determine a schedule for the change to the hydrodynamic surface of the hull disposed below the waterline of the ship to improve fuel efficiency based on known routes of travel and weather patterns.

299. The system of claim 292, wherein the floating asset twin is configured to provide visualizations of in-situ aerodynamic changes to a portion of a hull disposed above a water line of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in an aerodynamic surface to change performance of the ship.

300. The system of claim 299, wherein the floating asset twin is configured to determine a schedule for the change to the aerodynamic surface disposed above the waterline of the ship to improve fuel efficiency using known routes of travel and historical weather patterns.

301. The system of claim 292, wherein the floating asset twin is configured to provide visualizations of extendable buoyant members from a hull of the ship to improve stability during certain maneuvers of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the extendable buoyant members to change performance of the ship.

302. The system of claim 292, wherein the floating asset twin is configured to provide visualizations of a plurality of inspection points on the ship and maintenance histories associated with those inspection points.

303. The system of claim 302, wherein the floating asset twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change in maintenance of the plurality of inspection points.

304. The system of claim 302, wherein the floating asset twin is further configured to provide for visualizations of the plurality of inspection points on the ship affected by travel within a geofenced area and maintenance histories associated with those inspection points.

305. The system of claim 302, wherein the floating asset twin is further configured to provide details of a ledger of activity associated with the visualization of the plurality of inspection points on the ship affected by travel within a geofenced area and maintenance histories associated with those inspection points.

306. The system of claim 292, wherein the floating asset twin is configured to provide for visualization for a first user of one of a navigation course of the ship and an engine performance of the ship within a first geofenced area and for visualization for a second user of one of the navigation course of the ship and the engine performance of the ship within a second different geofenced area and where transit between the first and second geofenced areas motivates a handoff of the floating asset twin of the ship between the first user and the second user.

307. The system of claim 283, wherein the digital twin is configured to at least partially represent one or more of the maritime assets associated with an event investigation and to at least partially detail a timeline of the event investigation and the associated maritime assets.

308. The system of claim 307, wherein the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets based on the event investigation and the timeline.

309. The system of claim 283, wherein the digital twin is configured to at least partially represent one or more of the maritime assets associated with a legal proceeding and to at least partially detail at least a portion of a timeline pertinent to the legal proceeding and the associated maritime assets.

310. The system of claim 309, wherein the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change ofone of the attributes of the associated maritime assets based on the legal proceeding and the timeline.

311. The system of claim 283, wherein the digital twin is configured to at least partially represent one or more of the maritime assets associated with at least one of a casualty forecast and a casualty report, and to at least partially detail at least a portion of a timeline pertinent to the at least one of the casualty forecast, the casualty report, and the associated maritime assets.

312. The system of claim 311, wherein the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets to reduce exposure relative to a set of previous casualty forecasts based on at least one of the casualty forecast and the casualty report, and the timeline.

313. The system of claim 283, wherein the maritime assets include a port infrastructure facility, wherein the data collected by a value chain network management platform facilitates identifying theft at or misuse of the port infrastructure facility by correlating data between a set of data collectors for one or more physical items in the port infrastructure facility and the digital twin detailing the one or more physical items of the port infrastructure facility for the at least one of the port infrastructure facility and a set of operators.

314. The system of claim 313, wherein the digital twin details the one or more physical items of the port infrastructure facility for at least one operator that includes a view of expected states of at least a portion of the one or more physical items.

315. The system of claim 283, wherein the maritime assets include a shipyard, wherein the data collected by a value chain network management platform facilitates identifying theft at or misuse of one or more physical items in the shipyard by correlating data between a set of data collectors for the one or more physical items and the digital twin detailing the one or more physical items of the shipyard for the at least one of the shipyard and a set of operators.

316. The system of claim 315, wherein the digital twin details the one or more physical items of the shipyard for at least one operator that includes a view of expected states of at least a portion of the one or more physical items.

317. The system of claim 283, wherein the artificial intelligence system determines a set of geofence parameters, and wherein the digital twin provides further visualization of at least one geofence that integrates representation of a set of the maritime assets with a representation of a maritime environment adjacent to the geofence.

318. The system of claim 317, wherein the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the set of maritime assets based on the visualization of the at least one geofence.

319. The system of claim 283, wherein the maritime assets are ships capable of carrying cargo, wherein the artificial intelligence system determines a set of geofence parameters, and wherein the digital twin provides further visualization of at least one geofence that integrates representation of the ships capable of carrying cargo with a representation of a maritime environment.

320. The system of claim 319, wherein the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the ships capable of carrying cargo based on the visualization of the at least one geofence.

321. An information technology system having a management platform comprising:a user interface that provides a set of adaptive intelligence systems that provide coordinated artificial intelligence for a set of demand management applications and a set of supply chain applications for a category of goods by determining relationships among demand management and supply chain applications based on inputs used by the applications and results produced by the applications; anda set of artificial intelligence systems as part of the set of adaptive intelligence systems that provide coordinated intelligence for the set of demand management applications and the set of supply chain applications for the category of goods by determining a temporal prioritization of demand management application outputs that impact control of supply chain applications so as to meet a temporal demand for at least one of the goods in the category of goods.

322. The system of claim 321, wherein the adaptive intelligence system facilitates coordinated artificial intelligence for the set of demand management applications or the set of supply chain applications, or both for a category of goods by processing data that is available in any of a plurality of data sources including processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System.

323. The system of claim 322, wherein the set of adaptive intelligence systems provide user access to coordinated artificial intelligence capabilities for use with the sets of applications.

324. The system of claim 322, wherein the user interface presents a set of coordinated artificial intelligence capabilities responsive to the category of goods.

325. The system of claim 322, wherein the user interface facilitates configuring the set of adaptive intelligence systems with at least one artificial intelligence system.

326. The system of claim 322, wherein the at least one artificial intelligence system is a hybrid artificial intelligence system.

327. The system of claim 322, wherein the at least one artificial intelligence system comprises a hybrid neural network.

328. The system of claim 322, wherein the set of adaptive intelligence systems that provide coordinated artificial intelligence operates on or responsive to data collected by or produced by other systems of an adaptive intelligence systems layer.

329. The system of claim 322, wherein the set of adaptive intelligence systems that provide coordinated artificial intelligence provides coordinated intelligence for a specific operator and / or enterprise that participates in the supply chain for the category of goods.

330. The system of claim 322, wherein the set of adaptive intelligence systems that provide coordinated artificial intelligence employs a neural network that processes at least one of demand management application outputs and supply chain application outputs to provide the coordinated intelligence.

331. The system of claim 322, wherein the set of adaptive intelligence systems that provide coordinated artificial intelligence is configured through the user interface for at least two demand management applications selected from the list consisting of a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an ecommerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service332. The system of claim 322, wherein the set of adaptive intelligence systems that provide coordinated artificial intelligence is configured through the user interface for at least two supply chain applications selected from the list consisting of a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, and an order for components management application.

333. The system of claim 321, wherein the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

334. The system of claim 321, wherein an artificial intelligence system of the adaptive intelligence systems layer operates on or responsive to data collected by or produced by other systems of the adaptive intelligence systems layer.

335. The system of claim 321, wherein a set of artificial intelligence systems may provide coordinated intelligence for a specific operator and / or enterprise that participates in the supply chain for the category of goods.

336. The system of claim 321, wherein the coordinated intelligence includes a portion of a set of artificial intelligence systems that employs a neural network that processes at least one of demand management application outputs and supply chain application outputs to provide the coordinated intelligence.

337. The system of claim 321, wherein the demand management applications include at least two of a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service.

338. The system of claim 321, wherein the supply chain applications include at least two of a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, and an order for components management application.

339. The system of claim 321, wherein an artificial intelligence system facilitates coordinated intelligence for the sets of applications by processing data that is available in any of a plurality of data sources including processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer ExperienceManagement (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System.

340. The system of claim 321, wherein the set of adaptive intelligence systems are configured in a topology that facilitates shared adaptation capabilities among at least two adaptive intelligence systems in the set of adaptive intelligence systems.

341. The system of claim 321, wherein the set of adaptive intelligence systems employ artificial intelligence to provision available network resources for both the set of demand management applications and for the set of supply chain applications.

342. The system of claim 321, wherein the set of demand management applications comprises a demand planning application.

343. The system of claim 321, wherein the set of adaptive intelligence systems employ artificial intelligence to improve at least one of the list of outputs consisting of a process output, an application output, a process outcome and an application outcome.

344. A method for configuring role-based digital twins, comprising:receiving, by a processing system having one or more processors, an organizational definition of an enterprise, wherein the organizational definition defines a set of roles within the enterprise;generating, by the processing system, an organizational digital twin of the enterprise based on the organizational definition, wherein the organizational digital twin is a digital representation of an organizational structure of the enterprise;determining, by the processing system, a set of relationships between different roles within the set of roles based on the organizational definition;determining, by the processing system, a set of settings for a role from the set of roles based on the determined set of relationships;linking an identity of a respective individual to the role;determining, by the processing system, a configuration of a presentation layer of a rolebased digital twin corresponding to the role based on the settings of the role that is linked to the identity, wherein the configuration of the presentation layer defines a set of states that is depicted in the role-based digital twin associated with the role;determining, by the processing system, a set of data sources that provide data corresponding to the set of states, wherein each data source provides one or more respective types of data; andconfiguring one or more data structures that receive digital twin data from the one or more data sources, wherein the one or more data structures are configured to provide data used to populate one or more of the set of states in the role-based digital twin.

345. The method of claim 344, wherein the organizational definition further identifies a set of physical assets of the enterprise.

346. The method of claim 344, wherein determining the set of relationships includes parsing the organizational definition to identify a reporting structure and one or more business units of the enterprise.

347. The method of claim 346, wherein the set of relationships are inferred from the reporting structure and the business units.

348. The method of claim 344, further comprising linking a set of identities to the set of roles, wherein each identity corresponds to a respective role from the set of roles.

349. The method of claim 344, wherein the role-based digital twin integrates with an enterprise resource planning system that operates on the organizational digital twin that represents the set of roles in the enterprise, such that changes in the enterprise resource planning system are automatically reflected in the organizational digital twin.

350. The method of claim 344, wherein the organizational structure includes hierarchical components.

351. The method of claim 350, wherein the hierarchical components are embodied in a graph data structure.

352. The method of claim 344, wherein the set of settings for the set of roles includes rolebased permission settings.

353. The method of claim 352, wherein the role-based permission settings are based on hierarchical components defined in the organizational definition.

354. The method of claim 344, wherein the set of settings for the set of roles includes rolebased preference settings.

355. The method of claim 354, wherein the role-based preference settings are configured based on a set of role-specific templates.

356. The method of claim 355, wherein the set of role-specific templates includes at least one of a CEO template, a COO template, a CFO template, a counsel template, a board member template, a CTO template, a chief marketing officer template, an information technology manager template, a chief information officer template, a chief data officer template, an investor template, a customer template, a vendor template, a supplier template, an engineering manager template, a project manager template, an operations manager template, a sales manager template, a salesperson template, a service manager template, a maintenance operator template, and a business development template.

357. The method of claim 344, wherein the set of settings for the set of roles includes rolebased taxonomy settings.

358. The method of claim 357, wherein the taxonomy settings identify a taxonomy that is used to characterize data that is presented in the role-based digital twin, such that the data is presented in a taxonomy that is linked to the role corresponding to the role-based digital twin.

359. The method of claim 358, wherein the set of taxonomies includes at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customer taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, and a business development taxonomy.

360. The method of claim 344, wherein at least one role of the set of roles is selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, a human resources manager role, an investor role, an engineering manager role, an accountant role, an auditor role, a resource planning role, a public relations manager role, a project manager role, an operations manager role, a research and development role, an engineer role, including but not limited to mechanical engineer, electrical engineer, semiconductor engineer, chemical engineer, computer science engineer, data science engineer, network engineer, or some other type of engineer, and a business development role.

361. The method of claim 344, wherein the at least one role is selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

362. The method of claim 344, wherein the at least one role is selected from among a market maker role, a market analyst role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

363. The method of claim 344, wherein the at least one role is selected from among a chief marketing officer role, a product development role, a supply chain manager role, a product design role, a marketing analyst role, a product manager role, a competitive analyst role, a customer service representative role, a procurement operator, an inbound logistics operator, an outbound logistics operator, a customer role, a supplier role, a vendor role, a demandmanagement role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

364. A method for training an expert agent, comprising:receiving digital twin data from a set of data sources, the digital twin data including: sensor data that is received from a set of sensors that monitor a set of monitored physical entities associated with the enterprise, the sensor data transported by a set of network entities; andenterprise data streams generated by a set of enterprise assets, wherein the enterprise assets include at least one of physical entities associated with the enterprise and digital entities associated with the enterprise;structuring the digital twin data into a set of digital twin data structures that are configured to serve a plurality of different role-based digital twins;receiving a request for a role-based digital twin from a client application, wherein the role-based digital twin is configured with respect to a defined role within the enterprise;determining a subset of the structured digital twin data to corresponds to a set of states that are depicted in the role-based digital twin;providing the subset of the structured digital twin data to the client application;receiving expert agent training data sets from the client application, each expert agent training data set indicating a respective action taken by a user using the client application and one or more features that correspond to the respective action; andtraining an expert agent on behalf of the user based on the expert agent training data sets, wherein the expert agent is configured to determine actions to be performed on behalf of the user, wherein the determined actions are either recommended to the user or automatically performed on behalf of the user.

365. The method of claim 364, wherein the defined role is selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

366. The method of claim 364, wherein the defined role is selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

367. The method of claim 364, wherein the defined role is selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customerrole, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

368. The method of claim 364, wherein the expert agent training data includes interactions training data that indicates a set of interactions with a set of experts by the user during performance of the role.

369. The method claim 368, wherein the set of interactions used to train the expert agent includes interactions of the user with the physical entities.

370. The method of claim 368, wherein the set of interactions used to train the expert agent includes interactions of the user with the role-based digital twin.

371. The method of claim 368, wherein the set of interactions used to train the expert agent includes interactions of the user with the sensor data as depicted in the role-based digital twin.

372. The method of claim 368, wherein the set of interactions used to train the artificial intelligence system includes interactions of the experts with the data streams generated by the physical entities.

373. The method of claim 368, wherein the set of interactions used to train the expert agent system includes interactions of the experts with one or more computational entities.

374. The method of claim 368, wherein the set of interactions used to train the expert agent includes interactions of the user with one or more network entities.

375. The method of claim 364, wherein the expert agent is trained to determine an action selected from the group comprising: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, selection of an object, selection of a workflow, triggering of a workflow, ordering of a process, ordering of a workflow, cessation of a workflow, selection of a data set, selection of a design choice, creation of a set of design choices, identification of a failure mode, identification of a fault, identification of an operating mode, identification of a problem, selection of a human resource, selection of a workforce resource, providing an instruction to a human resource, and providing an instruction to a workforce resource.

376. The method of claim 364, wherein the executive is trained on a training set of outcomes resulting from the actions taken by the executive.

377. The method of claim 376, wherein the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome,an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

378. The method of claim 364, wherein the expert agent is trained to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource, physically securing equipment, physically securing hardware, cyber-securing a system, cybersecuring a device, cyber-securing a process, cyber-securing a network, cyber-securing a computational resource, cyber-securing equipment, cyber-securing hardware, detecting a threat, detecting a fault, tuning a system, tuning a device, tuning a process, tuning a network, tuning a computational resource, tuning equipment, tuning hardware, optimizing a system, optimizing a device, optimizing a process, optimizing a network, optimizing a computational resource, optimizing equipment, optimizing hardware, monitoring a system, monitoring a device, monitoring a process, monitoring a network, monitoring a computational resource, monitoring equipment, monitoring hardware, configuring a system, configuring a device, configuring a process, configuring a network, configuring a computational resource, configuring equipment, and configuring hardware.

379. The method of claim 364, wherein the expert agent is at least one of trained and configured via feedback from at least one expert in the defined role regarding a set of outputs of the expert agent.

380. The method of claim 379, wherein the set of outputs of the expert agent upon which the expert provides feedback includes at least one of a recommendation, a classification, a prediction, a control instruction, an input selection, a protocol selection, a communication, an alert, a target selection for a communication, a data storage selection, a computational selection, a configuration, an event detection, and a forecast.

381. The method of claim 380, wherein the feedback of the at least one expert is solicited to train the expert agent to replicate the expertise of the expert in the role.

382. The method of claim 380, wherein the feedback of the at least one expert is used to modify a set of inputs to the expert agent.

383. The method of claim 380, wherein herein the feedback of the at least one expert is used to identify and characterize at least one error by the expert agent.

384. The method of claim 383, wherein a report on a set of errors is provided to a user of the expert agent to enable reconfiguring of the expert agent based on the feedback from the expert.

385. The method of claim 384, wherein reconfiguring the artificial intelligence system includes at least one of removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and augmenting the set of inputs to the artificial intelligence system.

386. The method of claim 364, wherein the expert agent is trained leam upon a training set of outcomes and to provide at least one of training and guidance to an individual who is responsible for performing the defined role.

387. The method of claim 386, wherein the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.

388. The method of claim 364, wherein the defined role is selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

389. A method for serving digital twins comprising:receiving, by a processing system of a digital twin system, a request for a digital twin from a user device of a user associated with an enterprise, the enterprise deploying a sensor system to monitor one or more facilities of the enterprise;determining, by the processing system, a workforce role of the user with respect to the enterprise;generating, by the processing system, a role-based digital twin corresponding to the workforce role of the user based on a perspective view corresponding to the workforce role of the user, wherein the role-based digital twin depicts one or more states and / or entities that are related to the enterprise; andproviding, by the processing system, the role-based digital twin to the user device, wherein providing the role-based digital twin:identifying, by the processing system, a set of data types that are used to populate the at least one of the states and / or entities of the role-based digital twin, wherein the set of data types include one or more sensor data feeds that are received from the sensor system deployed by the enterprise; andconnecting, by the processing system, the one or more sensor data streams to the rolebased digital twin.

390. The method of claim 389, wherein generating the role-based digital twin includes determining the perspective view corresponding to the workforce role of the user based on the workforce role of the user and a set of data types that are relevant to the workforce role of the user.

391. The method of claim 390, wherein determining the perspective view corresponding to the workforce role of the user includes determining an appropriate granularity level for each data type of the set of data types.

392. The method of claim 391, wherein the appropriate granularity level for at least one of the data types is defined in a default configuration corresponding to the workforce role.

393. The method of claim 391, wherein the appropriate granularity level for at least one of the data types is determined based on previous interactions of the user with the role-based digital twin.

394. The method of claim 389, wherein the sensor system includes an edge device that receives sensor data from a set of sensors within the sensor system and generates the sensor data stream that is provided to the digital twin system via a network.

395. The method of claim 394, wherein the edge device receives sensor data from the set of sensors and selectively compresses the sensor data based on values indicated in the sensor data to obtain the sensor data stream.

396. The method of claim 394, wherein connecting the one or more sensor streams includes: receiving the sensor data stream from the edge device; androuting the sensor data stream to the user device that is presenting the role-based digital twin to the user.

397. The method of claim 394, wherein connecting the one or more sensor streams includes:receiving the sensor data stream from the edge device; andanalyzing the sensor data stream to identify one or more fault conditions corresponding to an object being monitored by the sensor system; androuting an indicator of the fault condition to the user device that is presenting the rolebased digital twin to the user.

398. The method of claim 394, wherein connecting the one or more sensor streams includes: receiving the sensor data stream from the edge device;analyzing the sensor data stream to identify a recommendation corresponding to the workforce role of the user; androuting an indicator of the recommendation to the user device that is presenting the role-based digital twin to the user.

399. The method of claim 394, wherein connecting the one or more sensor streams includes: receiving the sensor data stream from the edge device;analyzing the sensor data stream to identify a recommendation corresponding to the workforce role of the user; androuting an indicator of the recommendation to the user device that is presenting the role-based digital twin to the user.

400. The method of claim 389, wherein the workforce is a factory operations workforce.

401. The method of claim 389, wherein the workforce is a plant operations workforce.

402. The method of claim 389, wherein the workforce is a resource extraction operations workforce.

403. The method of claim 389, wherein the workforce is a network operations workforce responsible for operating a network for an industrial production environment.

404. The method of claim 389, wherein the workforce is a supply chain management workforce.

405. The method of claim 389, wherein the workforce is a demand planning workforce.

406. The method of claim 389, wherein the workforce is a logistics planning workforce.

407. The method of claim 389, wherein the workforce is a vendor management workforce.

408. The method of claim 389, wherein at least one workforce role is selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

409. The method of claim 389, wherein at least one workforce role is selected from among a factory manager role, a factory operations role, a factory worker role, a power plantmanager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

410. The method of claim 389, wherein at least one workforce role is selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.

411. The method of claim 389, wherein at least one workforce role is selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

412. A method for providing a digital twin of a workforce, comprising:maintaining an information technology architecture that supports a set of enterprise digital twins of a set of physical and digital entities of an enterprise, the architecture including:a set of sensors that provide sensor data about the set of physical entities;a set of data streams generated by at least a subset of the set of physical and digital entities;a set of computational entities that process data and a set of network entities that transport data that is derived from the set of sensors and the set of data streams;a set of data processing systems for extracting, transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin;representing an enterprise organizational structure in an organizational digital twin of an enterprise;parsing the structure to infer relationships among a set of roles within the organizational structure, wherein the relationships and the roles define a workforce of the enterprise;determining a set of parameters with which the digital twin is configured based on the inferred set of relationships; andconfiguring the presentation layer of a digital twin based on the set of parameters.

Images