Control tower and enterprise management platform for value chain networks

Abstract

This invention provides methods and systems for managing value chain network entities, including supply chain and demand management entities. [Solution] The value chain network management platform 604 includes a set of interfaces 702 for accessing and configuring platform functions, a set of network connectivity equipment 642 for a set of value chain network entities to connect to the platform, a set of adaptive intelligent equipment 614 for automating a set of platform functions, a set of data storage equipment 624 for storing data collected and processed by the platform, and a set of monitoring equipment 614 for monitoring value chain network entities.

Description

Technical Field

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

[0002] (Field) This disclosure relates to information technology methods and systems for managing value chain network entities, including supply chain and demand management entities. The disclosure also relates to the area of ​​enterprise management platforms, more specifically including data management, artificial intelligence, network connectivity, and digital twins.

[0003] (background) Historically, many items across various categories purchased and used by household consumers, businesses, and other customers were supplied in a relatively linear fashion. Manufacturers and other suppliers of finished goods, parts, and other items would hand over the goods to carriers, freight forwarders, etc., who would then deliver them to temporary storage warehouses, retail stores where customers purchased them, or directly to the customer's location. Manufacturers and retailers engaged in various sales and marketing activities, such as product design, shelf and advertising placement, and pricing, to stimulate and meet customer demand.

[0004] Product orders were fulfilled by manufacturers through a supply chain as depicted in Figure 1, with suppliers 122 in various supply environments 160 operating production facilities 134 or acting as resellers or distributors for others, making products 130 available at the origin 102 upon order. Products 130 passed through the supply chain and were transported and stored via various transport facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112, and delivery systems 114 including trucks and other vehicles, trains, etc. Often, maritime facilities and infrastructure such as ships, barges, docks, and ports provided waterway transport between the origin 102 and one or more destinations 104.

[0005] Organizations have access to a virtually limitless amount of data. With the emergence of smart connected devices, wearable technology, and the Internet of Things (IoT), the amount of data available to organizations planning, overseeing, managing, and operating value chain networks has increased dramatically, and is likely to continue to increase. For example, in manufacturing facilities, warehouses, campuses, or other operating environments, there may be hundreds or even thousands of IoT sensors providing various metrics, such as vibration data to measure the vibration signatures of critical machinery, temperature 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. Furthermore, with the proliferation of wearable technology, wearables may enable organizations to understand workers' movements, health indicators, physiological status, activity levels, behavior, and other characteristics. Furthermore, as organizations implement CRM systems, ERP systems, business systems, information technology systems, advanced analytics, and other systems that leverage information and information technology, they will gain increasingly broad access to other large datasets, including marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, and supply chain data (including datasets generated by or for the organization and third-party datasets).

[0006] The presence of more data and new types of data offers organizations many opportunities to achieve a competitive advantage, but it also presents problems such as complexity and volume, which can overwhelm users and cause them to miss opportunities for insight. There is a need for methods and systems that enable companies not only to acquire data, but also to transform that data into insights, and to translate those insights into informed decision-making and the timely execution of efficient operations. [Overview of the Initiative]

[0007] According to some embodiments of the present disclosure, methods and systems relating to an information technology system may include a cloud-based management platform with a microservices architecture, a set of interfaces, network connectivity equipment, adaptive intelligent equipment, data storage equipment, and monitoring equipment, and a set of applications for managing a set of value chain network entities from the origin of the enterprise to the point of use of the customer.

[0008] In embodiments, provided herein are methods, systems, components, and other elements for an information technology system which may include a cloud-based management platform having a microservices architecture. The platform includes a set of interfaces for accessing and configuring the platform's functions, a set of network connectivity facilities for enabling a set of value chain network entities to connect to 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 intelligent facilities for automating a set of functions of the platform, a set of data storage facilities for storing data collected and processed by the platform, and a set of monitoring facilities for monitoring the value chain network entities. The platform hosts a set of applications that enable an enterprise to manage a set of value chain network entities from the origin of its products to the point of use by the customer.

[0009] In an embodiment, the information technology system includes a cloud-based management platform having a microservices architecture. The platform includes a set of interfaces configured to access and configure platform functions, a set of network connectivity equipment configured to direct a set of value chain network entities to connect to platform functions, a set of adaptive intelligent equipment configured to automate a set of platform functions relating to value chain network entities and at least one of the platform functions, and a set of data storage equipment configured to store data collected and processed by the platform, wherein the data relates to at least one of the value chain network entities and platform functions, and a set of monitoring equipment configured to monitor the value chain network entities. The platform is configured to host a set of applications that direct the enterprise to manage the value chain network entities from the origin of the enterprise's products to the point of use by the customer.

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

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

[0012] In embodiments, value chain network entities are selected from a group consisting of products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, and seaports. Other entities include airports, air routes, waterways, roadways, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origins, destinations, storage locations, usage locations, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export controls, border controls, drones, robots, autonomous vehicles, transport 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, supply factors are factors selected from the group consisting of: component availability, material availability, component location, material location, component price, material price, taxation, tariff, postal service, tariff, import restrictions, export restrictions, border control, trade restrictions, customs, navigation, traffic, congestion, vehicle capacity, vessel capacity, container capacity, package capacity, vehicle capacity, vessel capacity, container capacity, package capacity; package availability, vehicle location, vessel 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 capacity, worker location, commodity price, fuel price, energy price, route availability, route distance, route cost, and route safety factor.

[0014] In embodiments, demand factors are factors selected from the group consisting of product availability, product price, delivery timing, refilling need, replacement need, manufacturer recall, upgrade need, maintenance need, renewal need, repair need, consumable need, taste, preference, inferred need, inferred desire, group demand, individual demand, family demand, business demand, workflow need, processing need, treatment need, improvement, diagnosis, system compatibility, product compatibility, style compatibility, and brand compatibility. Demographics, psychographics, geolocation, indoor location, destination, route, home location, visiting location, work location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchase history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family member, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interests, and estimated interest factors.

[0015] In embodiments, supply chain infrastructure facilities are selected from the group consisting of ships, container ships, boats, barges, seaports, cranes, containers, container handling, shipyards, offshore docks, warehouses, distribution, fulfillment, refueling, nuclear fuel refueling, and waste disposal. Food supply, beverage supply, drones, robots, autonomous vehicles, aircraft, automobiles, trucks, trains, lifts, forklifts, transport equipment, conveyors, loading platforms, waterways, bridges, tunnels, airports, garages, railway stations, weighing stations, inspections, roads, railways, highways, customs, border control facilities, etc.

[0016] In embodiments, the set of applications includes a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, taxation, regulation, identity management, commerce, e-commerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote operation, automation, self-configuration, self-healing, self-organization, logistics, and reverse logistics. Waste reduction, augmented reality, virtual reality, mixed reality, customer profiling, corporate profiling, worker profiling, workforce profiling, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, product configuration, product updates, product updates, product updates, kit deployment, kit support, kit updates, kit maintenance, kit modifications, kit management, fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, fleet management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and corporate resource planning applications.

[0017] In an embodiment, the information technology system includes a cloud-based management platform having a microservices architecture, the platform having a set of interfaces configured to access and configure the platform's functions, a set of network connectivity equipment configured to direct a set of value chain network entities to connect to the platform's functions, and a set of adaptive intelligence equipment configured to automate a set of capabilities of the platform relating to at least one of the value chain network entities and the platform's functions; a set of applications configured to direct an enterprise to manage the platform's value chain network entities from place of origin to point of customer use, comprising a set of data storage equipment configured to store data collected and handled by the platform, and a set of monitoring equipment configured to monitor the value chain network entities, wherein the interfaces, network connectivity equipment, adaptive intelligence equipment, data storage equipment, and monitoring equipment are coordinated for monitoring and managing the value chain network entities; and a unified robotic process automation system that provides coordinated automation between at least two types of applications with respect to the platform's value chain network entities, including a set of demand management applications relating to product categories, a supply chain application set, an intelligent product application set, and an enterprise resource management application set.

[0018] In embodiments, a unified set of robotic process automation systems automates processes selected from a group consisting of selecting product quantities for orders, selecting carriers for shipments, selecting suppliers for parts, selecting suppliers for finished product orders, selecting product variations for marketing, and selecting product assortments for shelves. This includes determining the price of finished products, setting up service offerings related to products, setting up product bundles, setting up product kits, setting up product packaging, setting up product displays, setting up product images, setting up product descriptions, setting up website navigation paths related to products, determining product inventory levels, selecting logistics types, setting up product delivery schedules, setting up logistics schedules, setting up inputs for machine learning, creating product documentation, creating product disclosures, and setting up products to meet a set of local requirements. It also includes setting up product groups for compatibility, setting up requests for proposals, ordering warehouse equipment, ordering fulfillment center equipment, classifying product defects in images, inspecting products in images, inspecting product quality data from sensor sets, and inspecting data from product on-board diagnostic devices. This includes inspecting diagnostic data from products and Internet of Things systems, examining sensor data from environmental sensors in a series of supply chain environments, selecting inputs for digital twins, selecting outputs from digital twins, selecting visual elements for presentations in digital twins, diagnosing sources of supply chain delays, sources of supply chain shortages, sources of supply chain congestion, sources of supply chain cost excesses, identifying causes of product defects in the supply chain, and predicting maintenance requirements in supply chain infrastructure.

[0019] In an embodiment, one of the processes automated by the robotic process automation system includes selecting the quantity of products for an order. In an embodiment, one of the processes automated by the robotic process automation system includes selecting a carrier for shipment. In an embodiment, one of the processes automated by the robotic process automation system includes selecting a vendor for parts. In an embodiment, one of the processes automated by the robotic process automation system includes selecting a vendor for a finished product order. In an embodiment, one of the processes automated by the robotic process automation system includes selecting product variations for marketing. In an embodiment, one of the processes automated by the robotic process automation system includes selecting a product assortment for shelves. In an embodiment, one of the processes automated by the robotic process automation system includes determining the price of the finished product.

[0020] In an embodiment, one of the processes automated by the robotic process automation system includes configuring a service offer related to a product. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a product bundle. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a product kit. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a product package. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a product display. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a product image. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a product description. In an embodiment, one of the processes automated by the robotic process automation system includes setting up a navigation path for a website related to a product.

[0021] In an embodiment, one of the processes automated by the robotic process automation system includes determining inventory levels for a product. In an embodiment, one of the processes automated by the robotic process automation system includes selecting a logistics type. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a schedule for product delivery. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a logistics schedule. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a set of inputs for machine learning. In an embodiment, one of the processes automated by the robotic process automation system includes creating product documentation. In an embodiment, one of the processes automated by the robotic process automation system includes creating disclosures about a product. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a product for a set of local requirements. In an embodiment, one of the processes automated by the robotic process automation system includes configuring a set of products for compatibility. In an embodiment, one of the processes automated by the robotic process automation system includes configuring requests for proposals. In an embodiment, one of the processes automated by the robotic process automation system includes ordering equipment for a warehouse. In an embodiment, one of the processes automated by the robotic process automation system includes ordering equipment for a fulfillment center. In an embodiment, one of the processes automated by the robotic process automation system includes classifying product defects in an image. In an embodiment, one of the processes automated by the robotic process automation system includes inspecting a product in an image. In an embodiment, one of the processes automated by the robotic process automation system includes inspecting product quality data from a set of sensors. In an embodiment, one of the processes automated by the robotic process automation system includes inspecting data from a set of onboard diagnostics on the product.

[0022] In an embodiment, one of the processes automated by the robotic process automation system includes checking diagnostic data from an Internet of Things system. In an embodiment, one of the processes automated by the robotic process automation system includes checking sensor data from environmental sensors in a set of supply chain environments. In an embodiment, one of the processes automated by the robotic process automation system includes selecting inputs for a digital twin. In an embodiment, one of the processes automated by the robotic process automation system includes selecting outputs from a digital twin. In an embodiment, one of the processes automated by the robotic process automation system includes selecting visual elements for a presentation in a digital twin. In an embodiment, one of the processes automated by the robotic process automation system includes diagnosing the source of delays in the supply chain. In an embodiment, one of the processes automated by the robotic process automation system includes diagnosing the cause of shortages in the supply chain. In an embodiment, one of the processes automated by the robotic process automation system includes diagnosing the cause of congestion in the supply chain. In an embodiment, one of the processes automated by the robotic process automation system includes diagnosing the cause of cost overruns in the supply chain. In an embodiment, one of the processes automated by the robotic process automation system includes diagnosing the source of product defects in the supply chain. In one embodiment, one of the processes automated by the robotic process automation system includes predicting maintenance requirements in the supply chain infrastructure.

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

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

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

[0026] In embodiments, value chain network entities are selected from a group consisting of products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, and seaports. Other entities include airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origins, destinations, storage locations, utilization locations, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export controls, border controls, drones, robots, autonomous vehicles, transport facilities, drones / robots / AVs, waterways, port infrastructure facilities, etc.

[0027] In one embodiment, the platform is characterized by managing a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.

[0028] In an embodiment, the supply factors are factors selected from the group consisting of: availability of components, availability of materials, location of components, location of materials, price of components, price of materials, taxation, customs duties, postage, customs duties, import restrictions, export restrictions, border control, trade regulations, customs, navigation, transportation, traffic jams, vehicle capacity, ship capacity, container capacity, package capacity, vehicle utilization rate, ship utilization rate, container capacity, packing capacity, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, location of the fulfillment center, availability of the fulfillment center, capacity of the fulfillment center, identity of the asset owner, system compatibility, availability of workers, worker capabilities, worker location, commodity price, fuel price, energy price, route availability, route distance, route cost, and route safety factor.

[0029] In an embodiment, demand factors are factors selected from the group consisting of product availability, product price, delivery timing, need for repacking, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, consumables, taste, preference, presumed need, presumed desire, group demand, individual demand, family demand, business demand, workflow need, process need, treatment need, improvement, diagnosis, compatibility with a system, compatibility with a product, compatibility with a style, compatibility with a brand. Demographics, psychographics, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, individual activity, wealth, income, purchase history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family member, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, presumed interest factor.

[0030] In an embodiment, supply chain infrastructure is equipment selected from the group consisting of ships, container ships, boats, barges, seaports, cranes, containers, container handling, shipyards, marine docks, warehouses, distribution, fulfillment, refueling, refueling, nuclear fuel, waste removal. Food supply, beverage supply, drones, robots, self-driving vehicles, aircraft, automobiles, trucks, trains, lifts, forklifts, transportation equipment, conveyors, platforms, waterways, bridges, tunnels, airports, garages, railway stations, weighing stations, inspections, roads, railways, highways, customs, border control facilities, etc.

[0031] In embodiments, the set of applications includes a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, taxation, regulation, identity management, commerce, e-commerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote operation, automation, self-configuration, self-healing, self-organization, logistics, and reverse logistics. Waste reduction, augmented reality, virtual reality, mixed reality, customer profiling, corporate profiling, worker profiling, workforce profiling, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, product configuration, product updates, product updates, product updates, kit deployment, kit support, kit updates, kit maintenance, kit modifications, kit management, fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, fleet management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and corporate resource planning applications.

[0032] In an embodiment, the information technology system includes a cloud-based management platform having a microservices architecture, the platform having a set of interfaces configured to access and configure platform functions, a set of network connectivity facilities configured to direct a set of value chain network entities to connect to platform functions, and a set of adaptive intelligence facilities configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and platform functions. The information processing system further includes a set of data storage facilities configured to store data collected and handled by the platform, and a set of monitoring facilities configured to monitor value chain network entities, wherein the interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities are coordinated for monitoring and managing value chain network entities. A set of applications configured to instruct an enterprise to manage the platform's value chain network entities from origin to customer usage point; and a set of microservice layers comprising an application layer supporting at least one supply chain application and at least one demand management application, wherein the microservice layers comprise a data collection layer that collects information from a set of Internet of Things resources that collect information about the supply chain entities and demand management entities relating to the platform's value chain network entities.

[0033] In embodiments, a set of Internet of Things resources that collect information relating to supply chain entities and demand management entities collects information from entities selected from the group consisting of products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, and demand aggregation processes, and the information includes: 1. Machinery, ships, barges, warehouses, seaports, airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origins, destinations, storage locations, usage locations, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export controls, border control, drones, robots, autonomous vehicles, transport facilities, drones / robots / AVs, waterways, port infrastructure facilities, etc.

[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, in-vehicle sensor systems, in-vehicle diagnostic systems, environmental control systems, sensor-enabled network switches and routing systems, RF sensing systems, magnetic sensing systems, pressure monitoring systems, vibration monitoring systems, temperature monitoring systems, heat flow monitoring systems, biomedical measurement systems, chemical measurement systems, ultrasonic monitoring systems, radiography systems, LIDAR-based monitoring systems, access control systems, transmitted wave sensing systems, SONAR-based monitoring systems, radar-based monitoring systems, CT systems, magnetic resonance imaging systems, network monitoring systems, etc.

[0035] In one embodiment, the set of Internet of Things resources includes a set of camera systems. In another embodiment, the set of Internet of Things resources includes a set of lighting systems. In yet another embodiment, the set of Internet of Things resources includes a set of machine vision systems. In yet another embodiment, the set of Internet of Things resources includes a set of motion detection systems.

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

[0037] In the embodiment, the set of Internet of Things resources includes a set of magnetic sensing systems. In the embodiment, the set of Internet of Things resources includes a set of pressure monitoring systems. In the embodiment, the set of Internet of Things resources includes a set of vibration monitoring systems. In the embodiment, the set of Internet of Things resources includes a set of temperature monitoring systems. In the embodiment, the set of Internet of Things resources includes a set of heat flow monitoring systems. In the embodiment, the set of Internet of Things resources includes a set of biomedical measurement systems. In the embodiment, the set of Internet of Things resources includes a set of chemical measurement systems. In the embodiment, the set of Internet of Things resources includes a set of ultrasonic monitoring systems. In the embodiment, the set of Internet of Things resources includes a set of X-ray imaging systems. In the embodiment, the set of Internet of Things resources includes a set of LIDAR-based monitoring systems. In the embodiment, the set of Internet of Things resources includes a set of access control systems. In the embodiment, the set of Internet of Things resources includes a set of penetration wave sensing systems. In the embodiment, the set of Internet of Things resources includes a set of SONAR-based monitoring systems. In the embodiment, the set of Internet of Things resources includes a set of radar-based monitoring systems. In an embodiment, the set of Internet of Things resources includes a set of computed tomography systems. In an embodiment, the set of Internet of Things resources includes a set of magnetic resonance imaging systems. In an embodiment, the set of Internet of Things resources includes a set of network monitoring systems. In an embodiment, 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 the following: a demand management application, a supply chain application, an intelligent product application, and an enterprise resource management application, selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, placement, placement, and promotion. Blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, tax, regulation, identity management, commerce, e-commerce, 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, customer profile, entity profile, enterprise profile, worker profile, workforce profile, parts supply policy management, product design, product configuration, product update, product maintenance, product support, product testing, warehousing, delivery, fulfillment, kit configuration, kit update, product maintenance, product testing, warehouse management, delivery. Applications for fulfillment, kit configuration, kit deployment, kit support, kit updates, kit maintenance, kit modifications, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and enterprise resource planning.

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

[0040] In embodiments, value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, and seaports. Other entities include airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origins, destinations, storage locations, utilization locations, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export controls, border controls, drones, robots, autonomous vehicles, transport facilities, drones / robots / AVs, waterways, port infrastructure facilities, etc.

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

[0042] In embodiments, supply factors are factors selected from the group consisting of: availability of components, availability of materials, location of components, location of materials, price of components, price of materials, taxation, tariffs, postal service, tariffs, import restrictions, export restrictions, border control, trade restrictions, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, packaging capacity, vehicle utilization rate, ship utilization rate, container utilization rate, packaging capacity; 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 capacity, worker location, commodity price, fuel price, energy price, route availability, route distance, route cost, and route safety factor.

[0043] In embodiments, demand factors are factors selected from the group consisting of product availability, product price, delivery timing, refilling need, replacement need, manufacturer recall, upgrade need, maintenance need, renewal need, repair need, consumable need, preference, inferred need, inferred desire, group demand, individual demand, family demand, business demand, workflow need, processing need, treatment need, improvement, diagnosis, compatibility with the system, compatibility with the product, compatibility with the style, and compatibility with the brand. Demographics, psychographics, geolocation, indoor location, destination, route, home location, visiting location, work location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchase history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family member, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interests, and estimated interest factors.

[0044] In embodiments, supply chain infrastructure facilities are selected from the group consisting of ships, container ships, boats, barges, seaports, cranes, containers, container handling facilities, shipyards, offshore docks, warehouses, distribution, fulfillment, refueling, nuclear fuel refueling, and waste removal. Food supply, beverage supply, drones, robots, autonomous vehicles, aircraft, automobiles, trucks, trains, lifts, forklifts, transport equipment, conveyors, loading platforms, waterways, bridges, tunnels, airports, garages, railway stations, weighing stations, inspections, roads, railways, highways, customs, border control facilities, etc.

[0045] In embodiments, the set of applications includes a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, taxation, regulation, identity management, commerce, e-commerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote operation, automation, self-configuration, self-healing, self-organization, logistics, and reverse logistics. Waste reduction, augmented reality, virtual reality, mixed reality, customer profiles, corporate profiles, worker profiles, workforce profiles, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, unit configuration, nuts, nuts, nut configuration, nut configuration kit deployment, kit support, kit updates, kit maintenance, kit modification, kit management, fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, fleet management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and corporate resource planning applications.

[0046] In an embodiment, the information technology system includes a cloud-based management platform having a microservices architecture, the platform comprising: a set of interfaces configured to access and configure platform functions; a set of network connectivity facilities configured to direct a set of value chain network entities to connect to platform functions; a set of adaptive intelligent facilities configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and platform functions; a set of data storage facilities configured to store data collected and handled by the platform; and a set of monitoring facilities configured to monitor the value chain network entities, the interfaces, network connectivity A set of applications configured to instruct an enterprise to manage the platform's value chain network entities from origin to customer usage point, with a set of monitoring equipment, including equipment, adaptive intelligence equipment, data storage equipment, and monitoring equipment coordinated for monitoring and managing value chain network entities; and a set of microservice layers including an application layer supporting at least one supply chain application and at least one demand management application, wherein the microservice layer includes a robotic process automation layer that automates a set of actions of at least a subset of the applications with respect to the platform's value chain network entities using information collected by the data collection layer and a set of outcomes and activities involving the applications in the application layer.

[0047] In embodiments, the robotic process automation layer automates processes selected from a group consisting of selecting the quantity of products for an order, selecting a transport company for shipment, selecting a vendor for parts, selecting a vendor for a finished product order, selecting product variations for marketing, and selecting a product assortment for shelves. This includes determining the price of finished products, setting up service offerings related to the product, setting up product bundles, setting up product kits, setting up product packaging, setting up product displays, setting up product images, setting up product descriptions, setting up navigation paths for websites related to the product, determining product inventory levels, selecting logistics types, setting up product delivery schedules, setting up logistics schedules, setting up inputs for machine learning, creating product documentation, creating disclosures about the product, and setting up products to meet a set of local requirements. It also includes setting up product sets for compatibility, setting up requests for proposals, ordering warehouse equipment, ordering fulfillment center equipment, classifying product defects in images, inspecting products in images, inspecting product quality data from a set of sensors, and inspecting data from a set of on-board diagnostic devices for the product. Products, inspection of diagnostic data from Internet of Things systems, examination of sensor data from environmental sensors in a series of supply chain environments, selection of inputs for digital twins, selection of outputs from digital twins, selection of visual elements for presentations in digital twins, diagnosis of supply chain delay sources, diagnosis of supply chain shortage sources, diagnosis of supply chain congestion sources, diagnosis of supply chain cost excess sources, diagnosis of product defect sources in the supply chain, and prediction of maintenance requirements in supply chain infrastructure.

[0048] In an embodiment, one of the actions automated by the robotic process automation layer includes selecting the quantity of products for an order. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting a carrier for shipment. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting a vendor for parts. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting a vendor for a finished product order. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting product variations for marketing. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting a product assortment for shelves. In an embodiment, one of the actions automated by the robotic process automation layer includes determining the price for finished products. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a service offering related to the product. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a product bundle. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a product kit. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a product package. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a product display. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a product image. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a product description. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a website navigation path related to a product. In an embodiment, one of the actions automated by the robotic process automation layer includes determining an inventory level for a product. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting a logistics type.In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a schedule for product delivery. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a logistics schedule. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a set of inputs for machine learning. In an embodiment, one of the actions automated by the robotic process automation layer includes preparing product documentation. In an embodiment, one of the actions automated by the robotic process automation layer includes creating disclosures about products. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring products for a set of local requirements. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring a set of products for compatibility. In an embodiment, one of the actions automated by the robotic process automation layer includes configuring requests for proposals. In an embodiment, one of the actions automated by the robotic process automation layer includes ordering equipment for a warehouse. In an embodiment, one of the actions automated by the robotic process automation layer includes ordering equipment for a fulfillment center. In an embodiment, one of the operations automated by the robotic process automation layer includes classifying product defects in an image. In an embodiment, one of the operations automated by the robotic process automation layer includes inspecting a product in an image. In an embodiment, one of the operations automated by the robotic process automation layer includes inspecting product quality data from a set of sensors. In an embodiment, one of the operations automated by the robotic process automation layer includes inspecting data from a set of onboard diagnostics on the product. In an embodiment, one of the operations automated by the robotic process automation layer includes inspecting diagnostic data from an Internet of Things system. In an embodiment, one of the operations automated by the robotic process automation layer includes inspecting sensor data from environmental sensors in a set of supply chain environments.In an embodiment, one of the actions automated by the robotic process automation layer includes selecting inputs for a digital twin. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting outputs from a digital twin. In an embodiment, one of the actions automated by the robotic process automation layer includes selecting visual elements for a presentation in a digital twin. In an embodiment, one of the actions automated by the robotic process automation layer includes diagnosing the causes of delays in the supply chain. In an embodiment, one of the actions automated by the robotic process automation layer includes diagnosing the causes of scarcity in the supply chain. In an embodiment, one of the actions automated by the robotic process automation layer is involved in diagnosing the causes of congestion in the supply chain. In an embodiment, one of the actions automated by the robotic process automation layer is involved in diagnosing the causes of cost overruns in the supply chain. In an embodiment, one of the actions automated by the robotic process automation layer includes diagnosing the causes of product defects in the supply chain. In an embodiment, one of the actions automated by the robotic process automation layer includes predicting maintenance requirements in the supply chain infrastructure.

[0049] In one embodiment, 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 the following: supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, placement, promotion, demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications. Blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, tax, regulation, identity management, commerce, e-commerce, 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, customer profiles, entity profiles, company profiles, worker profiles, workforce profiles, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit updates, product maintenance, product testing, warehouse management, distribution. The applications are selected from a group consisting of fulfillment, kit configuration, kit deployment, kit support, kit updates, kit maintenance, kit modifications, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and enterprise resource planning.

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

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

[0053] In embodiments, value chain network entities are selected from a group consisting of products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, and seaports. Other entities include airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origins, destinations, storage locations, utilization locations, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export controls, border control, drones, robots, autonomous vehicles, transport facilities, drones / robots / AVs, waterways, port infrastructure facilities, etc.

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

[0055] In embodiments, supply factors are factors selected from the group consisting of: availability of components, availability of materials, location of components, location of materials, price of components, price of materials, taxation, tariffs, postal service, tariffs, import restrictions, export restrictions, border control, trade restrictions, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, packaging capacity, vehicle capacity, ship capacity, container capacity, packaging capacity; availability of packages, location of vehicles, location of ships, location of containers, location of ports, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, location of fulfillment centers, availability of fulfillment centers, capacity of fulfillment centers, asset owner identity, system compatibility, worker availability, worker capacity, worker location, commodity price, fuel price, energy price, route availability, route distance, route cost, and route safety factor.

[0056] In embodiments, demand factors are factors selected from the group consisting of product availability, product price, delivery timing, refilling need, replacement need, manufacturer recall, upgrade need, maintenance need, renewal need, repair need, consumable need, preference, inferred need, inferred desire, group demand, individual demand, family demand, business demand, workflow need, processing need, treatment need, improvement, diagnosis, compatibility with the system, compatibility with the product, compatibility with the style, and compatibility with the brand. Demographics, psychographics, geolocation, indoor location, destination, route, home location, visiting location, work location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchase history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family member, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interests, and estimated interest factors.

[0057] In embodiments, supply chain infrastructure facilities are selected from the group consisting of ships, container ships, boats, barges, seaports, cranes, containers, container handling, shipyards, offshore docks, warehouses, distribution, fulfillment, refueling, nuclear fuel refueling, and waste removal. Food supply, beverage supply, drones, robots, autonomous vehicles, aircraft, automobiles, trucks, trains, lifts, forklifts, transport equipment, conveyors, loading platforms, waterways, bridges, tunnels, airports, garages, railway stations, weighing stations, inspections, roads, railways, highways, customs, border control facilities, etc.

[0058] In embodiments, the set of applications includes a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, taxation, regulation, identity management, commerce, e-commerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote operation, automation, self-configuration, self-healing, self-organization, logistics, and reverse logistics. Waste reduction, augmented reality, virtual reality, mixed reality, customer profiling, corporate profiling, worker profiling, workforce profiling, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration management, kit configuration management, kit configuration management, kit configuration management, kit placement, product configuration management, kit deployment, kit support, kit updates, kit maintenance, kit modification, kit management, fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, fleet management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and corporate resource planning applications.

[0059] In an embodiment, the information technology system includes a cloud-based management platform having a microservices architecture, the platform comprising: a set of interfaces configured to access and configure platform functions; a set of network connectivity facilities configured to direct a set of value chain network entities to connect to platform functions; a set of adaptive intelligent facilities configured to automate a set of capabilities of the platform relating to at least one of the value chain network entities and platform functions; a set of data storage facilities configured to store data collected and processed by the platform functions, constituting part of the platform functions of the platform and having at least one of the value chain network entities and platform functions; and a set of value chain network entities automated by the platform functions connected and configured by the platform functions. The system comprises a set of data storage facilities configured to store data collected and processed by the platform, and a set of monitoring facilities configured to monitor the value chain network entities, wherein the interfaces, network connectivity facilities, adaptive intelligent facilities, data storage facilities, and monitoring facilities are coordinated for monitoring and managing the value chain network entities. The system comprises a set of applications configured to instruct a company to manage the value chain network entities of the platform from their origin until they are used by a customer, and a machine reaming / artificial intelligence system configured to generate recommendations for placing at least one of additional sensors and cameras on and / or adjacent to the value chain network entities, wherein data from at least one of the additional sensors and cameras is fed into both digital representations of 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 a demand management application, a supply chain application, an intelligent product application, and a corporate resource management application, selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, placement, and advertising. Blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, tax, regulation, identity management, commerce, e-commerce, 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, customer profiling, entity profiling, company profiling, worker profiling, workforce profiling, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit updates, product maintenance, product testing, warehouse management, distribution, product design, product configuration, product updates, product maintenance, product support, product testing, warehouse management, distribution, product maintenance, product testing, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit maintenance, kit modification, kit management, fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and enterprise resource planning applications.

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

[0062] In embodiments, value chain network entities are selected from a group consisting of products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, and seaports. Other entities include airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origins, destinations, storage locations, utilization locations, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export controls, border controls, drones, robots, autonomous vehicles, transport facilities, drones / robots / AVs, waterways, port infrastructure facilities, etc.

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

[0064] In embodiments, supply factors are factors selected from the group consisting of: availability of components, availability of materials, location of components, location of materials, price of components, price of materials, taxation, tariffs, postal service, tariffs, import restrictions, export restrictions, border control, trade restrictions, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, packaging capacity, vehicle utilization rate, ship utilization rate, container utilization rate, packaging capacity; 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 capacity, worker location, commodity price, fuel price, energy price, route availability, route distance, route cost, and route safety factor.

[0065] In embodiments, demand factors are factors selected from the group consisting of product availability, product price, delivery timing, refilling need, replacement need, manufacturer recall, upgrade need, maintenance need, renewal need, repair need, consumable need, taste, preference, reasoning need, reasoning desire, group demand, individual demand, family demand, business demand, workflow need, processing need, treatment need, improvement, diagnosis, system suitability, product suitability, style suitability, and brand suitability. Demographics, psychographics, geolocation, indoor location, destination, route, home location, visiting location, work location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchase history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family member, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interests, and estimated interest factors.

[0066] In embodiments, supply chain infrastructure facilities are selected from the group consisting of ships, container ships, boats, barges, seaports, cranes, containers, container handling, shipyards, offshore docks, warehouses, distribution, fulfillment, refueling, nuclear fuel, and waste removal. Food supply, beverage supply, drones, robots, autonomous vehicles, aircraft, automobiles, trucks, trains, lifts, forklifts, transport equipment, conveyors, loading platforms, waterways, bridges, tunnels, airports, departure and arrival points, vehicle stations, train stations, weighing stations, inspections, roads, railways, highways, customs, and border control facilities.

[0067] In embodiments, the set of applications includes a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, placement, deployment, promotion, blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, taxation, regulation, identity management, commerce, e-commerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote operation, automation, self-configuration, self-healing, self-organization, logistics, and reverse logistics. Waste reduction, augmented reality, virtual reality, mixed reality, customer profiling, corporate profiling, worker profiling, workforce profiling, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit placement, nut configuration, kit configuration, unit configuration, nut configuration kit deployment, kit support, kit updates, kit maintenance, kit modification, kit management, fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, fleet management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and corporate resource planning applications.

[0068] In one embodiment, a value chain system providing container convoy management decisions includes a machine learning system for training a machine learning model that outputs a container convoy management decision given each set of input features related to a particular shipping event, wherein the machine learning system includes an artificial intelligence system for training the machine learning model on a training dataset that defines the features of previous shipping events and the results of shipping events, an artificial intelligence system for receiving a request for container convoy management and making a container convoy management decision based on the machine learning model and the request, and a machine learning system for generating one or more environmental digital twins of a container convoy based on the machine learning model. A digital twin system for generating an environmental digital twin of the environment of a container convoy and one or more container digital twins of each container in a container convoy, wherein the digital twin system runs a container convoy simulation based on the environmental digital twin and one or more container digital twins, issues a container convoy management request from the artificial intelligence system based on the state of the container convoy simulation, and adjusts the state of the container convoy simulation based on a container convoy management decision output by the artificial intelligence system in response to the container convoy management request.

[0069] In an embodiment, the digital twin system outputs simulation results to a machine learning system, which then enhances a machine learning model used to determine container fleet management decisions based on the simulation results. In an embodiment, the artificial intelligence system receives a container fleet management request from the digital twin system and determines a container fleet management decision based on simulation features defined in the container fleet management request, where the simulation features indicate the state of the container fleet simulation. In an embodiment, the container fleet management request includes one or more characteristics of a simulated shipping event. In an embodiment, the artificial intelligence system determines a container fleet management decision based on one or more characteristics of the simulated shipping event and a machine learning model. In an embodiment, one or more characteristics include the type of goods being shipped. In an embodiment, one or more characteristics include the origin and destination of the containers. In an embodiment, the digital twin system provides result data to the machine learning system, where the result data defines the simulation results, which are the result of the container fleet management decision.

[0070] In one embodiment, a value chain system that provides recommendations for designing a logistics system includes a machine learning system that trains a machine learning model to output logistics design recommendations given a set of input features related to each specific logistics system, wherein the machine learning system trains the machine learning model on a training dataset that defines the features and results of the logistics system; an artificial intelligence system that receives a request for logistics system design and determines logistics system design recommendations based on the machine learning model and the request; and a digital twin system that generates an environmental digital twin of a logistics environment incorporating the logistics system design recommendations and one or more physical asset digital twins, wherein the digital twin system is as follows: a logistics simulation system characterized by performing a logistics simulation based on the logistics environment digital twin and the one or more physical asset digital twins, issuing a logistics system design request from the artificial intelligence system based on the state of the logistics simulation, and adjusting the state of the logistics simulation based on the logistics system design recommendations output by the artificial intelligence system in response to the logistics system design request.

[0071] In one embodiment, the digital twin system outputs a graphical representation of the environmental digital twin to a display, thereby allowing the user to view the simulation via the display. In another embodiment, the digital twin system outputs the simulation results to a machine learning system, which then augments a machine learning model used to determine logistics system design recommendations based on the simulation results. In yet another embodiment, the artificial intelligence system receives a request from a logistics design system that designs a logistics system, the request including one or more logistics factors corresponding to a proposed logistics solution for an organization. In yet another embodiment, the logistics factors include one or more of the following: the type of product corresponding to the proposed logistics solution, one or more characteristics of the product type, the location of the manufacturing site, the location of the distribution facility, the location of the warehouse, the location of the customer base, the proposed extension area of ​​the organization, and the characteristics of the supply chain. In yet another embodiment, the logistics design system provides the machine learning system with result data related to logistics system design recommendations, which then augments a machine learning model used to determine logistics system design recommendations based on the result data. In yet another embodiment, the artificial intelligence system determines logistics system design recommendations to minimize delay time. In one embodiment, the artificial intelligence system determines logistics system design recommendations to comply with regulatory requirements.

[0072] In one embodiment, a value chain system for designing packaging includes a machine learning system that trains a machine learning model to output packaging design recommendations, given each set of input features associated with each specific packaging design, the machine learning system trains the machine learning model on a training dataset that defines the features and outcomes of the packaging design. An artificial intelligence system receives a request for packaging design and determines a packaging design recommendation based on the machine learning model and the request, and a digital twin system generates a package digital twin of the package incorporating the packaging design recommendation, the digital twin system is as follows: it runs a packaging simulation based on the package digital twin, issues a packaging design request from the artificial intelligence system based on the 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 an embodiment, the digital twin system outputs a graphical representation of the package digital twin to a display, thereby allowing the user to view the simulation via the display. In an embodiment, the digital twin system outputs a graphical representation of the package digital twin to a graphical user interface, thereby allowing the user to edit the packaging design via the graphical user interface. In an embodiment, the digital twin system outputs the simulation results to a machine learning system, which then augments a machine learning model used to determine packaging design recommendations based on the simulation results. In an embodiment, the artificial intelligence system receives a request from a packaging design system that designs packaging for a physical object, the request including one or more packaging factors corresponding to proposed packaging designs for the physical object. In an embodiment, the packaging factors include one or more of the type of physical object, the dimensions of the physical object, the mass of the physical object, and the shipping method of the physical object. In an embodiment, the packaging design system provides the machine learning system with result data related to packaging design recommendations, which then augments a machine learning model used to determine packaging design recommendations based on the result data. In an embodiment, the artificial intelligence system determines packaging design recommendations to minimize damage. In an embodiment, the artificial intelligence system determines packaging design recommendations to minimize costs. In one embodiment, the artificial intelligence system determines packaging design recommendations to mitigate environmental impacts.

[0074] In an embodiment, an information technology system for leveraging a digital twin in a value chain having multiple value chain entities includes: a plurality of sensors, at least one of which are located in, on, and near the set of value chain entities, and which are configured to collect sensor data related to the set of value chain entities, wherein the sensor data is substantially real-time sensor data; and an adaptive intelligence system connected to the plurality of sensors and configured to receive sensor data from the plurality of sensors, wherein the adaptive intelligence system includes: an artificial intelligence system configured to input the sensor data into a machine learning model so that the sensor data is used as training data for a machine learning model, and which is configured to convert the sensor data into simulation data; and a digital twin system configured to create digital replicas of a set of value chain entities based on the simulation data, wherein the digital replicas of the value chain entities are configured to provide simulations of possible future states of the value chain entities via the simulation data and to be used for substantially real-time representations.

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

[0076] In an embodiment, a machine learning system comprising a value chain network management platform, which includes a machine learning system that trains one or more machine learning models to output one or more e-commerce recommendations to customers of a value chain network via an interface using training data including product features and outcomes; and an artificial intelligence system that receives e-commerce requests from an e-commerce system, wherein the artificial intelligence is configured to determine and generate e-commerce recommendations based on one or more machine learning models and requests, and the artificial intelligence is configured to perform simulations based on one or more customer digital twins, one or more product digital twins and e-commerce recommendations, leveraging one or more product digital twins and one or more customer digital twins.

[0077] In an embodiment, the machine learning system is integrated with a model interpretability system, which is configured to perform testing using a Concept Activation Vector (TCAV) function, thereby facilitating the learning of human-interpretable concepts by the machine learning model. In an embodiment, one or more machine learning models are trained and retrained using simulation data from one or more simulations, including one or more customer profile digital twins.

[0078] In one embodiment, the value chain network management platform includes a machine learning system that trains one or more machine learning models using training data including component characteristics and results to output one or more risk management decisions, the machine learning system trains one or more machine learning models, and an artificial intelligence system that receives a risk management request from a risk management system, the artificial intelligence system is configured to determine and generate a risk management decision based on one or more machine learning models and the request, the artificial intelligence system is configured to perform a simulation based on one or more component digital twins, one or more environmental digital twins and the risk management decision, utilizing one or more component digital twins and one or more environmental digital twins.

[0079] In the embodiment, the risk management decision is related to the state of the component. In the embodiment, one or more machine learning models are trained and retrained using simulation data from one or more simulations that include one or more components.

[0080] In an embodiment, the information technology system includes a value chain network management platform having an asset management application associated with maritime assets, the platform comprising a data processing layer which includes information used to input a set of maritime activities of one or more maritime assets and a training set based on at least one of design results, parameters, and data associated with one or more maritime assets; an artificial intelligence system configured to run on a training set collected from a data source, the artificial intelligence system configured to simulate one or more attributes of one or more maritime assets, and the artificial intelligence system configured to generate one or more sets of recommendations for changes in one or more attributes based on a training set collected from a data source; and a digital twin system configured to provide visualizations of one or more digital twins of the maritime assets, including details of the one or more attributes generated by the artificial intelligence system in combination with the one or more sets of generated recommendations.

[0081] In an embodiment, the maritime asset includes one or more container ships, and the digital twin system further provides visualizations of digital twins of one or more container ships, including one or more attributes combined with one or more sets of recommendations associated with the container ships. In an embodiment, the maritime asset includes one or more barges, and the digital twin system further provides visualizations of digital twins of one or more barges, including one or more attributes combined with one or more sets of recommendations associated with the barges. In an embodiment, the maritime asset includes one or more components of port infrastructure installed on land or adjacent to land, and the digital twin system further provides visualizations of digital twins of the port infrastructure components, including one or more attributes combined with one or more sets of recommendations associated with the port infrastructure components. In an embodiment, the maritime asset also includes container ships moored to the port infrastructure components. In an embodiment, the maritime asset includes one or more moored navigation units deployed on the water. In an embodiment, the maritime asset includes one or more vessels, each connected to a barge. In an embodiment, the maritime asset is associated with a real-world seaport, and the digital twin system further provides a visualization of a digital twin of one or more of the real-world seaport components, including one or more attributes in combination with one or more of a set of recommendations associated with the components of the real-world seaport. In an embodiment, the maritime asset is associated with a real-world shipyard, and the digital twin system further provides a visualization of a digital twin of the real-world shipyard components, including one or more attributes in combination with one or more of a set of recommendations associated with the components of the real-world shipyard.

[0082] In an embodiment, one or more digital twins of maritime assets are floating asset twins related to a vessel. In an embodiment, the floating asset twin is configured to provide a visualization of the vessel's course relative to its planned course and one or more sets of recommendations from an artificial intelligence system for changes to the vessel's course. In an embodiment, the floating asset twin is configured to provide a visualization of the vessel's engine performance and one or more sets of recommendations from an artificial intelligence system for changes to the vessel's engine performance. In an embodiment, the engine performance visualization includes the vessel's exhaust gas profile. In an embodiment, the floating asset twin is configured to provide a visualization of the vessel's hull integrity and one or more sets of recommendations from an artificial intelligence system for changes to the maintenance of the vessel's hull. In an embodiment, the floating asset twin is configured to provide a visualization of in-situ hydrodynamic changes to a portion of the hull located below the vessel's waterline and one or more sets of recommendations from an artificial intelligence system for changes to the hydrodynamic surface to change the vessel's performance. In one embodiment, the floating asset twin is configured to determine a schedule for modifying the hydrodynamic surface of the hull positioned below the waterline of the vessel, based on known routes and weather patterns, in order to improve fuel efficiency.

[0083] In an embodiment, the floating asset twin is configured to provide one or more of the following: visualization of in-situ aerodynamic changes to a portion of the hull positioned above the waterline of the vessel, and a set of recommendations from an artificial intelligence system for changes to the aerodynamic surfaces to alter the performance of the vessel. In an embodiment, the floating asset twin is configured to use known routes and historical weather patterns to determine a schedule for changes to the aerodynamic surfaces positioned above the waterline of the vessel to improve fuel efficiency. In an embodiment, the floating asset twin is configured to provide visualization of expandable buoyancy components from the hull of the vessel, and a set of recommendations from an artificial intelligence system for changes to the expandable buoyancy components to alter the performance of the vessel, in order to improve the stability of the vessel during a particular maneuver. In an embodiment, the floating asset twin is configured to provide visualization of a plurality of inspection points on the vessel and the maintenance history associated with those inspection points. In an embodiment, the floating asset twin is further configured to provide one or more of the following of the following of recommendations from an artificial intelligence system for changes to the maintenance of a plurality of inspection points. In an embodiment, the floating asset twin is further configured to provide a visualization of multiple inspection points on a vessel affected by movement within a geofenced area, and the maintenance history associated with those inspection points. In an embodiment, the floating asset twin is further configured to provide a visualization of multiple inspection points on a vessel affected by movement within a geofenced area, and details of a ledger of activities associated with the maintenance history associated with those inspection points. In an embodiment, the floating asset twin is configured to provide a visualization to a first user of either the vessel's navigation course or the vessel's engine performance within a first geofenced area, and to provide a visualization to a second user of either the vessel's navigation course or the vessel's engine performance within a second geofenced area, and to provide when passage between the first and second geofenced areas motivates the handoff of the vessel's floating asset twin between the first and second users.

[0084] In an embodiment, the digital twin is configured to at least partially represent one or more maritime assets related to an event survey and to at least partially detail the event survey and the timeline of the related maritime assets. In an embodiment, the digital twin is further configured to provide one or more sets of recommendations from an artificial intelligence system for modifying one of the attributes of the related maritime assets based on the event survey and the timeline. In an embodiment, the digital twin is configured to at least partially represent one or more maritime assets related to a legal proceeding and to at least partially detail at least a portion of the timeline related to the legal proceeding and the related maritime assets. In an embodiment, the digital twin is further configured to provide one or more sets of recommendations from an artificial intelligence system for modifying one of the attributes of the related maritime assets based on the legal proceeding and the timeline. In an embodiment, the digital twin is configured to at least partially represent one or more maritime assets related to at least one casualty forecast and casualty report and to at least partially detail at least a portion of the timeline related to the casualty forecast, casualty report and at least one of the related maritime assets. In an embodiment, the digital twin is further configured to provide one or more sets of recommendations from an artificial intelligence system to modify one of the attributes of the relevant maritime asset to reduce exposure compared to a previous set of casualty forecasts, based on at least one of casualty forecasts and casualty reports and a timeline. In an embodiment, the maritime asset includes port infrastructure facilities, and data collected by the value chain network management platform facilitates the identification of theft or misuse at the port infrastructure facilities by associating a set of data collectors for one or more physical items at the port infrastructure facilities with a digital twin detailing said one or more physical items for at least one of a set of port infrastructure facilities and operators.In an embodiment, the digital twin details one or more physical items of a port infrastructure facility for at least one operator, including a view of the expected state of at least some of the physical items. In an embodiment, the maritime assets include a shipyard, and data collected by the value chain network management platform facilitates the identification of the theft or misuse of one or more physical items of the shipyard by correlating data between a set of data collectors for one or more physical items and a digital twin detailing one or more physical items of the shipyard for at least one of a set of shipyards and operators. In an embodiment, the digital twin details one or more physical items of the shipyard for at least one operator, including a view of the expected state of at least some of the physical items. In an embodiment, an artificial intelligence system determines a set of geofence parameters, and the digital twin provides further visualization of at least one geofence, integrating a representation of the set of maritime assets with a representation of the maritime environment adjacent to the geofence. In an embodiment, the digital twin is further configured to provide one or more of a set of recommendations from the artificial intelligence system for modifying one of the attributes of the set of maritime assets based on the visualization of at least one geofence. In one embodiment, the offshore asset is a cargo-carrying vessel, the artificial intelligence system determines a set of geofence parameters, and the digital twin further provides a visualization of at least one geofence that integrates a representation of the cargo-carrying vessel with a representation of the offshore environment. In another embodiment, the digital twin is further configured to provide one or more of a set of recommendations from the artificial intelligence system for modifying one of the attributes of the cargo-carrying vessel based on the visualization of at least one geofence.

[0085] In one embodiment, the information technology system having a management platform is a user interface that includes 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 the relationships between the demand management applications and the supply chain applications based on inputs used by the applications and results generated by the applications, wherein the set of adaptive intelligence systems is a set of adaptive intelligence systems that provide coordinated intelligence for a set of demand management applications and a set of supply chain applications for a category of goods based on inputs used by the applications and a set of artificial intelligence systems as part of an adaptive intelligence system that provides coordinated intelligence for the set of demand management applications and the set of supply chain applications for a category of goods by determining the temporal priority of demand management application outputs that affect the control of the supply chain applications to satisfy the temporal demand for at least one product in the category of goods.

[0086] In embodiments, the adaptive intelligence system processes data available from one of several data sources, such as processes, bills of materials, weather, traffic, design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, customer experience management (CEM) systems, service lifecycle management (SLM) systems, and product lifecycle management (PLM) systems, to facilitate coordinated artificial intelligence for demand management applications or supply chain applications for product categories, or both. In embodiments, a set of adaptive intelligence systems provides user access to tailored artificial intelligence capabilities for use in a set of applications. In embodiments, a user interface presents a set of tailored artificial intelligence capabilities that respond to product categories. In embodiments, a user interface facilitates the configuration of a set of adaptive intelligence systems with at least one artificial intelligence system. In embodiments, at least one artificial intelligence system is a hybrid artificial intelligence system. In embodiments, at least one artificial intelligence system comprises a hybrid neural network. In embodiments, a set of adaptive intelligence systems providing collaborative artificial intelligence operates based on or in response to data collected or generated by other systems in the adaptive intelligence system layer. In an embodiment, a set of adaptive intelligence systems providing tailored artificial intelligence provides tailored intelligence to specific operators and / or companies participating in the supply chain of a product category. In an embodiment, a set of adaptive intelligence systems providing tailored artificial intelligence employs a neural network that processes at least one of the output of a demand management application and the output of a supply chain application to provide tailored intelligence.

[0087] In one embodiment, a set of adaptive intelligence systems providing collaborative artificial intelligence is configured via a user interface for at least two demand management applications selected from a list consisting of demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, and advertising applications.

[0088] In an embodiment, a set of adaptive intelligence systems providing tailored artificial intelligence is configured through a user interface for at least two supply chain applications selected from a list consisting of a product timing management application, a product quantity management application, a logistics management application, a shipping application, a delivery application, orders for the product management application, and orders for the parts management application. In an embodiment, a set of adaptive intelligence systems provides a set of functions that facilitate the 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 trading, intelligent operation, remote control, analysis, monitoring, reporting, status management, event management, and process management. In an embodiment, an artificial intelligence system in the adaptive intelligence system layer operates on or in response to data collected or generated by other systems in the adaptive intelligence system layer. In an embodiment, a set of artificial intelligence systems may provide tailored intelligence to specific operators and / or companies participating in the supply chain of a product category. In an embodiment, the tailored intelligence includes a portion of a set of artificial intelligence systems that employ a neural network to process at least one of the demand management application output and the supply chain application output to provide tailored intelligence.

[0089] In an embodiment, the demand management application includes at least two of the following: a demand planning application, a demand forecasting 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 behavior tracking application, a marketing analytics application, a location-based product or service targeting application, a collaborative filtering application, and a product or service recommendation engine.

[0090] In this embodiment, the supply chain application includes at least two of the following: a product timing management application, a product quantity management application, a logistics management application, a shipping application, a delivery application, an order management application for product management applications, and a parts management application.

[0091] In one embodiment, the artificial intelligence system processes data available from one of several data sources, such as processes, bills of materials, weather, traffic, design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, customer experience management (CEM) systems, service lifecycle management (SLM) systems, and product lifecycle management (PLM) systems, to facilitate the collaborative intelligence of a set of applications.

[0092] In an embodiment, a set of adaptive intelligence systems is configured in a topology that facilitates shared adaptive capabilities among at least two adaptive intelligence systems within the set. In an embodiment, a pair of adaptive intelligence systems employ artificial intelligence to provide network resources available for both a set of demand management applications and a set of supply chain applications. In an embodiment, the set of demand management applications includes a demand planning application. In an embodiment, a pair of adaptive intelligence systems employ artificial intelligence to improve at least one of an output list consisting of process outputs, application outputs, process outcomes, and application outcomes.

[0093] One way to extract information is through digital twin technology, which can present large amounts of data in a digestible format that represents the prominent features of an item, and often the twin is updated in real time or near real time to reflect the current state based on a data pipeline about the represented item. While this is a useful feature, current digital twin technology has limitations because different roles within an organization may need different information to gain insights. For example, the CEO of a certain industrial facility makes decisions based on a "10,000-foot view" of the company. The CEO may review profit and loss data, industry trends, and employee trends (such as employee satisfaction and retention rates) to make overall decisions on behalf of the organization, but may not necessarily need to review detailed data points for decision-making. Another user, such as a CFO, may need more granular information to draw conclusions, such as sales by region, marketing costs, maintenance costs, depreciation information, human capital costs, and third-party vendor costs, but may not be as interested in employee or industry trends. Similarly, a CTO may not need profit and loss data, but may need detailed visualizations of processes across various manufacturing facilities to gain a deeper understanding of opportunities to improve process outcomes or to diagnose problems within processes, equipment, and systems. Thus, digital twins and other interfaces need to be configured to suit specific roles.

[0094] A further challenge is that assigned roles may have diverse needs depending on the context. For example, a CEO might focus on higher-level data in many activities, such as strategic decision-making and communication with the board of directors, but the same CEO might find finer-grained, micro-scale data useful in other activities, such as when a problem is escalated from lower levels of the organization and they are asked for their opinion. Thus, there is a need for context-adaptive digital twins that provide the right type of display and information at the right time for the various situations and activities that each role performs.

[0095] More generally, ubiquitous connectivity and the proliferation of larger datasets offer business leaders unprecedented opportunities for awareness and control over their assets and activities. There is a need and opportunity for an enterprise control tower where business leaders can receive timely information tailored to evoke relevant insights, support effective decision-making, and enable operational management, through a variety of interfaces including digital twins of executives, dashboards, and similar systems.

[0096] According to several embodiments of this disclosure, an enterprise management platform is disclosed. In some embodiments, the enterprise management platform ingests data from intelligent data and networking pipelines and integrates role-specific capabilities, including AI-enabled expert agent capabilities and enhanced collaboration capabilities, as well as a set of executive digital twins that provide a remarkable view of the enterprise entities and workflows, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree, using familiar taxonomy and decision-making frameworks at the appropriate granularity.

[0097] Further provided herein are methods and systems for an enterprise management tower that enable executives to obtain curated, timely information (often in real time or near real time) through various interfaces, including executive digital twins, dashboards, and similar systems, to evoke relevant insights, support effective decision-making, and enable operational control. The disclosure further relates to an executive control tower and enterprise management platform configured to provide and enable a centralized technology stack, including intelligent sensing and data acquisition, distributed storage, networking and connectivity pipelines (from a series of local operating environments through information technology networks to various distributed on-premises and cloud computing environments), and the deployment of various application-specific and general artificial intelligence capabilities, to enable an executive control tower, including role-specific executive digital twins, for use by executives in managing enterprise value chain network operations.

[0098] Embodiments of the present disclosure provide a method for constructing a role-based digital twin, comprising: receiving an organizational definition of an enterprise by a processing system having one or more processors, the organizational definition defining a set of roles within the enterprise; and generating an organizational digital twin of the enterprise based on the organizational definition, the organizational digital twin being a digital representation of the enterprise's organizational structure. The method includes: determining a set of relationships between different roles within the set of roles based on the organizational definition by the processing system; determining a set of settings for roles from the set of roles based on the determined set of relationships by the processing system; and linking the identity of each individual to a role. The method also includes: determining a configuration of a presentation layer of a role-based digital twin corresponding to a role based on the identity-linked role settings by the processing system, the presentation layer configuration defining a set of states to be depicted in the role-based digital twin associated with the role. A processing system characterized by: determining a set of data sources that provide data corresponding to a set of states, each data source providing one or more of each type of data; and configuring one or more data structures to be received from one or more data sources, the one or more data structures being configured to provide data used to populate one or more sets of states in a role-based digital twin.

[0099] In this embodiment, the definition of an organization may further identify a set of the company's physical assets.

[0100] In the embodiment, determining a set of relationships may include analyzing the definition of an organization to identify the reporting structure of a company and one or more business units.

[0101] In some embodiments, a series of relationships may be inferred from the reporting structure and business units.

[0102] In this embodiment, a set of identities may be linked to a set of roles, and each identity corresponds to a specific role within that set of roles.

[0103] In one embodiment, the role-based digital twin may be integrated with an enterprise resource planning system that operates on an organizational digital twin representing a set of roles within the enterprise, so that changes in the enterprise resource planning system are automatically reflected in the organizational digital twin.

[0104] In some embodiments, the organizational structure may include hierarchical components that can be embodied in a graph data structure.

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

[0106] In this embodiment, role-based permission settings can be based on hierarchical components defined in the organization definition.

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

[0108] In this embodiment, role-based preference settings may be configured based on a set of role-specific templates.

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

[0110] In an embodiment, the setting of a series of roles may include the setting of a role-based taxonomy.

[0111] In one embodiment, the classification setting may specify a classification used to characterize the data presented in the role-based digital twin, such that the data is presented in a classification linked to the role corresponding to the role in the role-based digital twin.

[0112] In embodiments, the set of classifications includes at least one of the following: CEO classification, COO classification, CFO classification, legal counsel classification, director classification, CTO classification, chief marketing officer classification, information technology manager classification, chief information officer classification, chief data officer classification, investor classification, customer classification, vendor classification, supplier classification, engineering manager classification, project manager classification, operations manager classification, sales manager classification, salesman classification, service manager classification, maintenance operator classification, and business development classification.

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

[0114] In the embodiment, at least one role may be selected from the following roles: factory manager role, factory worker role, power plant manager role, power plant worker role, equipment service role, and equipment maintenance operator role.

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

[0116] In each embodiment, at least one role may be selected from the roles of Chief Marketing Officer, Product Development Officer, Supply Chain Manager, Product Design Officer, Marketing Analyst, Product Manager, Competition Analyst, Customer Service Officer, Procurement Operator, Inbound Logistics Operator, Outbound Logistics Operator, Customer, Supplier, Vendor, Demand Manager, Marketing Manager, Sales Manager, Service Manager, Demand Forecaster, Retail Manager, Warehouse Manager, Salesperson, Distribution Center Manager.

[0117] Embodiments of the present disclosure provide a method for training an expert agent, which includes: receiving digital twin data from a set of data sources, the digital twin data comprising: sensor data received from a set of sensors monitoring a set of monitored physical entities relating to an enterprise, the sensor data being carried by a set of network entities; enterprise data streams generated by a set of enterprise assets, the enterprise assets comprising at least one of an enterprise-related physical entity and an enterprise-related digital entity; structuring the digital twin data into a set of digital twin data structures configured to provide a plurality of different role-based digital twins; receiving a request for a role-based digital twin from a client application, the role-based digital twin being configured with respect to defined roles within the enterprise; determining a subset of the structured digital twin data to correspond to a set of states depicted in the role-based digital twin; providing the subset of the structured digital twin data to the client application; receiving an expert agent training dataset from the client application, the expert agent training dataset representing each action performed by a user using the client application and one or more features corresponding to each action. The process includes the step of training an expert agent on behalf of a user based on an expert agent training dataset, wherein the expert agent is configured to determine an action to be performed on behalf of the user, and the determined action is either recommended to the user or performed automatically on behalf of the user.

[0118] In this embodiment, the defined roles may be selected from among the roles of CEO, COO, CFO, attorney, director, CTO, information technology manager, chief information officer, chief data officer, investor, engineering manager, project manager, operations manager, and business development.

[0119] In the embodiment, the defined roles may be selected from among the roles of factory manager, factory worker, power plant manager, power plant worker, equipment service, and equipment maintenance operator.

[0120] In the embodiment, the defined roles may be selected from among market maker roles, exchange administrator roles, broker-dealer roles, trading roles, coordination roles, contract counterparty roles, exchange rate setting roles, market orchestration roles, market configuration roles, and contract configuration roles.

[0121] In each embodiment, the defined roles may be selected from the roles of Chief Marketing Officer, Product Development, Supply Chain Manager, Customer, Supplier, Vendor, Demand Management, Marketing Manager, Sales Manager, Service Manager, Demand Forecasting, Retail Manager, Warehouse Manager, Salesperson, and Distribution Center Manager.

[0122] In one embodiment, the expert agent training data may include interaction training data that shows a set of interactions between a user performing a role and an expert.

[0123] In an embodiment, a set of interactions used to train an expert agent may include user interactions with a physical entity, user interactions with a role-based digital twin, user interactions with sensor data depicted in the role-based digital twin, expert interactions with data streams generated by the physical entity, expert interactions with one or more computational entities, user interactions with one or more network entities, or several other types of interactions.

[0124] In an embodiment, an expert agent may be trained to determine an action to be selected from a group including: tool selection, task selection, dimension selection, parameter setting, object selection, workflow selection, workflow triggering, process ordering, workflow ordering, workflow stopping, dataset selection, design selection, creation of a set of design selections, failure mode identification, fault identification, operating mode identification, problem identification, human resource selection, workforce resource selection, providing instructions to human resources, and providing instructions to workforce resources. To determine an action to be selected from a group including these, the expert agent may be trained as follows:

[0125] In an embodiment, officers may be trained on a set of training scenarios for the consequences arising from actions taken by the officers.

[0126] In an embodiment, the training set of outcomes may include data relating to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0127] In embodiments, the expert agent may be trained to perform actions selected from the following: determining the system architecture, reporting on states, reporting on events, reporting on context, reporting on conditions, determining a model, configuring a model, inputting a model, designing a system, designing a process, designing equipment, engineering a system, engineering equipment, engineering a process, engineering a product, maintaining a system, maintaining equipment, maintaining a process, maintaining equipment, maintaining hardware, maintaining a system, maintaining equipment, maintaining a network, maintaining a process, maintaining a network, maintaining computing resources, maintaining equipment, and maintaining hardware. Maintaining systems, maintaining equipment, maintaining processes, maintaining networks, maintaining computing resources, maintaining equipment, maintaining hardware, repairing systems, repairing equipment, repairing processes, repairing networks, repairing computing resources, repairing equipment, repairing hardware, assembling systems, assembling equipment, assembling processes, assembling networks, assembling computing resources, assembling equipment, assembling hardware, pricing, physically securing systems, physically securing devices. Physically secure systems, devices, processes, networks, computing resources, equipment, hardware; cybersecure systems, devices, processes, networks, computing resources, equipment, hardware; threat detection, fault detection, system tuning, device tuning, process tuning, network tuning, computing resource tuning, equipment tuning.Hardware tuning, system optimization, equipment optimization, process optimization, network optimization, computing resource optimization, device optimization, hardware optimization, system monitoring, device monitoring, process monitoring, network monitoring, computing resource monitoring, device monitoring, hardware monitoring, system configuration, device configuration, process configuration, network configuration, computing resource configuration, device configuration, and hardware configuration.

[0128] In an embodiment, the expert agent is at least one that is trained and configured through feedback from at least one expert of a defined role with respect to the expert agent's output set.

[0129] In an embodiment, the set of outputs of an expert agent that provides feedback to an expert may include at least one of the following: recommendation, classification, prediction, control command, input selection, protocol selection, communication, warning, target selection for communication, data storage selection, computation selection, configuration, event detection, and prediction.

[0130] In one embodiment, feedback from at least one expert may be solicited to train expert agents to replicate the expertise of the role-playing expert.

[0131] In the embodiment, feedback from at least one expert may be used to modify the input set to the expert agent and / or to identify and characterize at least one error made by the expert agent.

[0132] In some embodiments, a report of a series of errors may be provided to the user of the expert agent in order to enable the expert agent to be reconfigured based on feedback from the expert.

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

[0134] In an embodiment, expert agents may be trained to provide at least one of training and / or guidance to individuals responsible for performing defined roles, based on a set of outcome training.

[0135] In an embodiment, the training set of outcomes may include data relating to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0136] Embodiments of the present disclosure provide a method for taking an information technology architecture that supports a digital twin of a set of physical and digital entities, the architecture comprising: a set of sensors that provide sensor data relating to 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 computing entities for processing the data; a set of network entities for transmitting the data obtained from the set of sensors and the set of data streams; a set of data processing systems for extracting, transforming and loading the data carried by the network entities into a set of resources that are the sources of the digital twin; and an artificial intelligence system integrated with the information technology architecture, the artificial intelligence system being configured to operate as a double of expert workers for a defined role in the enterprise.

[0137] In one embodiment, the artificial intelligence system can be trained based on a training set of data that includes a series of interactions between specific expert workers while they perform a defined role.

[0138] In one embodiment, the set of interactions used to train the artificial intelligence system may include interactions between an expert and a physical entity, and the set of interactions used to train the artificial intelligence system may include interactions between an expert and a digital twin.

[0139] In one embodiment, a set of interactions used to train an artificial intelligence system may include an interaction between an expert and sensor data, and a set of interactions used to train an artificial intelligence system may include an interaction between an expert and a data stream generated by a physical entity.

[0140] In one embodiment, the set of interactions used to train the artificial intelligence system may include interactions between experts and computational entities, and the set of interactions used to train the artificial intelligence system may also include interactions between experts and network entities.

[0141] In one embodiment, a set of interactions is analyzed to identify a chain of inferences made by an expert worker on a set of information, and this chain of inferences can be embodied in the configuration of an artificial intelligence system.

[0142] In an embodiment, the artificial intelligence system may be trained to determine, based on a set of interactions, an action to be selected from the following: tool selection, task selection, dimension selection, parameter setting, object selection, workflow selection, workflow triggering, process ordering, workflow ordering, workflow stopping, dataset selection, design selection, creation of a set of design selections, failure mode identification, fault identification, operating mode identification, problem identification, human resource selection, workforce resource selection, commanding human resources, and commanding workforce resources.

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

[0144] In this embodiment, the chain of inferences may be a deductive chain of inferences from a set of data.

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

[0146] In embodiments, the artificial intelligence system may be trained on a training set to perform actions selected from: determining the architecture of the system, reporting on the state, reporting on events, reporting on the context, reporting on conditions, determining a model, configuring a model, feeding in a model, designing a system, designing a process, designing equipment, engineering a system, engineering equipment, engineering a process, engineering a product, maintaining a system, maintaining equipment. Maintaining a system, maintaining equipment, maintaining a process, maintaining a network, maintaining computing resources, maintaining equipment, maintaining hardware, repairing a system, repairing equipment, repairing a process, repairing a network, repairing computing resources, repairing equipment, repairing hardware, assembling a system, assembling equipment, assembling a process, assembling a network, assembling computing resources, assembling equipment, assembling hardware, pricing, physically securing a system, physically securing equipment. Physically secure systems, devices, processes, networks, computing resources, equipment, and hardware; cybersecure systems, devices, processes, networks, computing resources, equipment, and hardware; threat detection; fault detection; system optimization; device optimization; process optimization; network optimization; computing resource optimization; equipment optimization; hardware optimization; system monitoring; equipment monitoring; process monitoring; network monitoring; computing resource monitoring; equipment monitoring; hardware monitoring; system configuration; equipment configuration; process configuration; network configuration; computing resource configuration; equipment configuration; and hardware configuration.

[0147] In one embodiment, the interaction training set may be analyzed to identify the type of processing an expert worker performs on a set of information, and the type of processing is embodied in the configuration of the artificial intelligence system.

[0148] In one embodiment, the type of processing may be visual processing performed by an expert worker, and the artificial intelligence system is configured to operate on image or video information.

[0149] In one embodiment, one type of processing may involve expert worker voice processing, and the artificial intelligence system may be configured to operate on voice information.

[0150] In the embodiment, the type of processing may be touch processing by an expert worker, and the artificial intelligence system may be configured to operate based on physical sensor information.

[0151] In the embodiment, the type of processing may be olfactory processing performed by a specialist worker, and the artificial intelligence system may be configured to operate based on chemical sensing information.

[0152] In this embodiment, the type of processing may be text information processing by expert workers, and the artificial intelligence system may be configured to operate on text information.

[0153] In this embodiment, the type of processing may be motion processing performed by an expert worker, and the artificial intelligence system may be configured to operate on motion information.

[0154] In the embodiment, the type of processing may be the taste processing performed by a professional worker, and the artificial intelligence system may be configured to operate on chemical information.

[0155] In some embodiments, one type of processing may involve mathematical processing performed by an expert worker, and the artificial intelligence system may be configured to operate mathematically on the available data.

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

[0157] In one embodiment, one type of processing may involve creative processing by an expert worker, and the artificial intelligence system may be configured to provide a set of alternative options.

[0158] In one embodiment, the type of processing may involve analytical processing by an expert worker to select from a set of available options, and the artificial intelligence system may be configured to provide recommendations from the set of options.

[0159] In one embodiment, the artificial intelligence system may be trained with a set of results.

[0160] In an embodiment, the training set of outcomes may include data relating to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0161] In one embodiment, the artificial intelligence system can undergo at least one of training and configuration through feedback from a specific expert worker regarding a set of outputs of the artificial intelligence system.

[0162] In an embodiment, a set of outputs from an artificial intelligence system to which an expert provides feedback may include at least one of the following: recommendation, classification, prediction, control command, input selection, protocol selection, communication, warning, target selection for communication, data storage selection, computation selection, configuration, event detection, and prediction.

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

[0164] In one embodiment, a report of a series of errors may be provided to a manager associated with the artificial intelligence system in order to enable the reconfiguration of the artificial intelligence system based on feedback from experts.

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

[0166] In an embodiment, the artificial intelligence system may be configured to provide at least one of training and / or guidance to other workers in order to enable them to perform defined roles.

[0167] In one embodiment, the artificial intelligence system may include a training set of results to enhance training and guidance.

[0168] In an embodiment, the training set of outcomes may include data relating to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0169] In an embodiment, the artificial intelligence system may be configured to provide at least one of training and / or guidance to other workers in order to enable them to perform defined roles.

[0170] In one embodiment, the artificial intelligence system may include a training set of results to enhance training and guidance.

[0171] In an embodiment, the training set of outcomes may include data relating to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0172] In one embodiment, the artificial intelligence system may be configured to provide at least one of training and / or guidance to expert workers so that they can perform defined roles.

[0173] In one embodiment, the artificial intelligence system may relearn the resulting training set to enhance training and guidance.

[0174] In an embodiment, the training set of outcomes may include data related to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0175] In one embodiment, the results can be compared between a series of actions by an expert worker and a series of outputs from an artificial intelligence system.

[0176] In some embodiments, comparisons may be used to train expert workers.

[0177] In some embodiments, comparisons may be used to improve artificial intelligence systems.

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

[0179] In this embodiment, the computation entity and the network entity may be integrated as a converged computation entity and network entity.

[0180] Embodiments of the present disclosure provide a method for maintaining an information technology architecture that supports a digital twin of a set of physical entities, the architecture comprising: a set of sensors providing sensor data relating to 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 computing entities for processing the data; a set of network entities for transporting the data obtained from the set of sensors and the set of data streams; and a set of data processing systems for extracting, transforming and loading the data transported by the network entities into a set of resources that are the source of the digital twin; and integrating an artificial intelligence system into the information technology architecture, wherein the artificial intelligence system is configured to operate as a dual personality of an expert worker for a defined role in the enterprise, and an electronic account associated with the expert worker is awarded benefits for training the artificial intelligence system.

[0181] In embodiments, the benefits may be rewards based on the results of using the artificial intelligence system, rewards based on the productivity of the artificial intelligence system, and / or rewards based on the measurement of the expertise of the artificial intelligence system.

[0182] In an embodiment, the profit may be revenue or profit sharing generated by the work of the artificial intelligence system, and / or a reward tracked via a distributed ledger on a blockchain that captures information related to a series of actions and events involving the artificial intelligence system.

[0183] In one embodiment, the reward may be managed via a smart contract operating on the blockchain.

[0184] In one embodiment, the artificial intelligence system may be trained based on a training set of data that includes a series of interactions between specific expert workers while they perform defined roles.

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

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

[0187] In an embodiment, the artificial intelligence system may be trained on interaction to determine an action to be selected from the following: selecting a tool, selecting a task, selecting a dimension, setting a parameter, selecting an object, selecting a workflow, triggering a workflow, ordering a process, ordering a workflow, stopping a workflow, selecting a dataset, selecting a design selection, creating a set of design selections, identifying a failure mode, identifying a fault, identifying an operating mode, identifying a problem, selecting human resources, selecting workforce resources, providing instructions to human resources, and providing instructions to workforce resources.

[0188] In one embodiment, the interaction training set may be analyzed to identify a chain of reasoning by an expert worker based on a set of information, and this chain of reasoning is embodied in the configuration of an artificial intelligence system.

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

[0190] In this embodiment, the chain of inferences may be a deductive chain of inferences from a set of data.

[0191] In embodiments, an artificial intelligence system may be trained to perform actions selected from the following: determining the architecture of a system, reporting on the status, reporting on events, reporting on the context, reporting on conditions, determining a model, configuring a model, deploying a model, designing a system, designing a process, designing equipment, engineering a system, engineering equipment, engineering a process, engineering a product, maintaining a system, maintaining equipment, maintaining a process, maintaining a network, maintaining computed resources, maintaining equipment, maintaining hardware, repairing a system, repairing equipment, repairing a process, repairing a network, repairing computing resources, repairing equipment, repairing hardware, assembling a system, assembling equipment, assembling a process, assembling a network, assembling computing resources, assembling equipment, assembling hardware, setting prices, physically securing a system, physically securing equipment, physically securing a process, physically securing a network. Physically secure systems, physically secure devices, physically secure hardware, physically secure devices, physically secure processes, physically secure networks, physically secure computing resources, physically secure equipment, physically secure hardware, threat detection, fault detection, system adjustment, equipment adjustment, process adjustment, network adjustment, computing resource adjustment, equipment adjustment, hardware adjustment, system optimization, equipment adjustment, computing resource adjustment. Equipment optimization, process optimization, network optimization, computing resource optimization, equipment optimization, hardware optimization, system monitoring, equipment monitoring, process monitoring, network monitoring, computing resource monitoring, equipment monitoring, hardware monitoring, system configuration, equipment configuration, process configuration, network configuration, computing resource configuration, equipment configuration, and hardware configuration.

[0192] Embodiments of the present disclosure provide a method for taking an information technology architecture that supports a digital twin of a set of physical entities, the architecture comprising: a set of sensors providing sensor data relating to 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 computing entities for processing the data; a set of network entities for transmitting the data obtained from the set of sensors and the set of data streams; a set of data processing systems for extracting, transforming and loading the data transmitted by the network entities into a set of resources that are the source of the digital twin; and integrating an artificial intelligence system into the information technology architecture, characterized in that the artificial intelligence system is configured to operate as a dual of a defined workforce comprising a defined set of roles for an enterprise.

[0193] In one embodiment, the artificial intelligence system may be trained based on a training set of data that includes a series of interactions between defined members of a defined workforce while performing a defined set of roles.

[0194] In an embodiment, a set of interactions used to train an artificial intelligence system may include interactions between a physical entity and a workforce, interactions between a digital twin and a workforce, interactions between sensor data and a workforce, interactions between a data stream generated by a physical entity and a workforce, interactions between a computational entity and a workforce, and / or interactions between a network entity and a workforce.

[0195] In one embodiment, the interaction training set may be analyzed to identify a sequence of worker actions based on a set of information, and the sequence of inferences may be embodied in the configuration of an artificial intelligence system.

[0196] In one embodiment, the interaction training set may be analyzed to identify the type of processing a workforce performs on a set of information, and the type of processing may be embodied in the configuration of the artificial intelligence system.

[0197] In an embodiment, an artificial intelligence system may be trained on interaction to determine an action to be selected from the following: tool selection, task selection, dimension selection, parameter setting, object selection, workflow selection, workflow triggering, process ordering, workflow ordering, workflow stopping, dataset selection, design selection, creation of a set of design selections, failure mode identification, fault identification, operating mode identification, problem identification, human resource selection, workforce resource selection, providing instructions to human resources, and providing instructions to workforce resources.

[0198] In one embodiment, the artificial intelligence system may be trained on a set of results.

[0199] In an embodiment, the training set of outcomes may include data relating to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0200] In one embodiment, the artificial intelligence system may be trained and configured through feedback from members of the workforce regarding a set of outputs of the artificial intelligence system.

[0201] In an embodiment, the set of outputs of the artificial intelligence system to which a workforce member provides feedback may include at least one of recommendation, classification, prediction, control command, input selection, protocol selection, communication, warning, target selection for communication, data storage selection, computation selection, configuration, event detection, and prediction.

[0202] In one embodiment, feedback from workforce members may be solicited to train an artificial intelligence system to replicate the workforce's behavior in a defined set of roles.

[0203] In one embodiment, feedback from a workforce member may be used to modify the set of inputs to the artificial intelligence system.

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

[0205] In one embodiment, a report on a series of errors may be provided to the manager of the artificial intelligence system to enable the reconfiguration of the artificial intelligence system based on the feedback.

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

[0207] In an embodiment, the artificial intelligence system may be configured to provide at least one of training and instruction to enable other workers to perform roles within a defined set of roles for workers.

[0208] In one embodiment, the artificial intelligence system may relearn the resulting training set to enhance training and guidance.

[0209] In an embodiment, the training set of outcomes may include data related to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0210] In embodiments, an artificial intelligence system may be trained to perform selected actions from among: determining the architecture of a system, reporting on status, reporting on events, reporting on context, reporting on conditions, determining a model, configuring a model, inputting a model, designing a system, designing a process, designing equipment, engineering a system, engineering equipment, engineering a process, engineering a product, maintaining a system, maintaining equipment. Maintaining a system, maintaining equipment, maintaining a process, maintaining a network, maintaining computing resources, maintaining equipment, maintaining hardware, repairing a system, repairing equipment, repairing a process, repairing a network, repairing computing resources, repairing equipment, repairing hardware, assembling a system, assembling equipment, assembling a process, assembling a network, assembling computing resources, assembling equipment, assembling hardware, pricing, physically securing a system, physically securing equipment. Physically secure systems, devices, processes, networks, computing resources, equipment, and hardware; cybersecure systems, devices, processes, networks, computing resources, equipment, and hardware; threat detection; fault detection; system optimization; device optimization; process optimization; process optimization; network optimization; equipment optimization; hardware optimization; system monitoring; equipment monitoring; process monitoring; network monitoring; computing resource monitoring; equipment monitoring; hardware monitoring; system configuration; equipment configuration; process configuration; network configuration; computing resource configuration; equipment configuration; and hardware configuration.

[0211] In one embodiment, the artificial intelligence system may be configured to provide the workforce with at least one of training and guidance so that the workforce can perform defined roles.

[0212] In one embodiment, the artificial intelligence system may include a training set of results to enhance training and guidance.

[0213] In an embodiment, the training set of outcomes may include data related to at least one of the following: financial outcomes, operational outcomes, failure outcomes, success outcomes, performance indicator outcomes, output outcomes, consumption outcomes, energy utilization outcomes, resource utilization outcomes, cost outcomes, profit outcomes, revenue outcomes, sales outcomes, and production outcomes.

[0214] In embodiments, the results may be compared between a set of worker actions and a set of outputs from the artificial intelligence system, which is used to train the worker and / or improve the artificial intelligence system.

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

[0216] In this embodiment, the workforce may be a factory operations workforce, a plant operations workforce, a resource extraction operations workforce, a network operations workforce responsible for operating networks for industrial production environments, a supply chain management workforce, a demand planning workforce, a logistics planning workforce, a vendor management workforce, or any other type of workforce.

[0217] In some embodiments, the workforce may be an intermediary workforce for the marketplace, a trading workforce for the marketplace, a trading coordination workforce for the marketplace, a trading execution workforce for the marketplace, or any other type of workforce.

[0218] In this embodiment, the computational entities and network entities may be integrated as converged computational entities and network entities.

[0219] Embodiments of the present disclosure provide a method for constructing a digital twin of a workforce, which includes representing the organizational structure of the enterprise in the enterprise's digital twin, analyzing the structure to infer relationships between a set of roles within the organizational structure, defining the enterprise's workforce as relationships and roles, and constructing a presentation layer of the digital twin to represent the enterprise as a set of workforces having a set of attributes and relationships.

[0220] In one embodiment, the digital twin can be integrated with an enterprise resource planning system that operates on a data structure representing a set of roles within the enterprise, and changes in the enterprise resource planning system can be automatically reflected in the digital twin.

[0221] In the embodiment, the organizational structure may include hierarchical components.

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

[0223] In this embodiment, the workforce may be a factory operations workforce, a plant operations workforce, a resource extraction operations workforce, or several other types of workforces.

[0224] In embodiments, the workforce may be a network operations workforce responsible for operating a network for an industrial production environment, and the workforce may be a supply chain management workforce, a demand planning workforce, a logistics planning workforce, a vendor management workforce, a market intermediary workforce, a market trading workforce, a market trading coordination workforce, a market trading execution workforce, or any other type of workforce.

[0225] In one embodiment, at least one workforce role may be selected from the roles of CEO, COO, CFO, attorney, director, CTO, information technology manager, chief information officer, chief data officer, investor, engineering manager, project manager, operations manager, and business development.

[0226] In the embodiment, at least one worker role may be selected from among the roles of factory manager, factory worker, power plant manager, power plant worker, equipment service, and equipment maintenance operator.

[0227] In an embodiment, at least one workforce role may be selected from among market maker, foreign exchange manager, broker-dealer, trading, coordination, contract trading partner, exchange rate setting, market orchestration, market structuring, and contract structuring roles.

[0228] In one embodiment, at least one workforce role may be selected from the roles of Chief Marketing Officer, Product Development, Supply Chain Manager, Customer, Supplier, Vendor, Demand Management, Marketing Manager, Sales Manager, Service Manager, Demand Forecasting, Retail Manager, Warehouse Manager, Salesperson, and Distribution Center Manager.

[0229] In one embodiment, the digital twin may represent recommendations for training a workforce, recommendations for augmenting a workforce, recommendations for configuring a set of operations involving a workforce, recommendations for configuring a workforce, or any other type of recommendation.

[0230] Embodiments of this disclosure provide a method for providing a digital twin of a workforce, which includes the following steps: maintaining an information technology architecture that supports a digital twin of a set of physical and digital entities, the architecture including: An implementation of an information technology architecture comprising: a set of sensors providing sensor data relating to a 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 computation entities processing the data; a set of network entities transporting the data obtained from the set of sensors and data streams; a set of data processing systems extracting, transforming, and loading the data transported by the network entities into a set of resources that are the source of a digital twin; representing the organizational structure of a company as a digital twin of the company; analyzing the structure and the following steps to infer the relationships between a set of roles within the organizational structure, wherein the relationships and roles define the workforce of the company; and integrating an artificial intelligence system with the information technology architecture, wherein the artificial intelligence system is configured to act as a worker double for a set of defined roles in the company, and constituting a presentation layer of the digital twin to represent the company as a workforce having a set of attributes and relationships, wherein the attributes and relationships include the attributes and relationships of human workers as well as the attributes and relationships of an artificial intelligence double.

[0231] In one embodiment, the digital twin can be integrated with an enterprise resource planning system that operates on a data structure representing a set of roles within the enterprise, and changes in the enterprise resource planning system can be automatically reflected in the digital twin.

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

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

[0234] In embodiments, the 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 the industrial production environment, a supply chain management workforce, a demand planning workforce, a logistics planning workforce, a vendor management workforce, an intermediary workforce, a transaction workforce, a transaction coordination workforce, a transaction execution workforce, or any other type of workforce.

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

[0236] In an embodiment, at least one workforce role may be selected from among the roles of factory manager, factory worker, power plant manager, power plant worker, equipment service, and equipment maintenance operator.

[0237] In an embodiment, at least one workforce role may be selected from among a market maker role, a foreign exchange manager role, a broker-dealer role, a trading role, a coordination role, a contract trading partner role, a foreign exchange rate setting role, a market orchestration role, a market structuring role, and a contract structuring role.

[0238] In one embodiment, at least one workforce role may be selected from the roles of Chief Marketing Officer, Product Development, Supply Chain Manager, Customer, Supplier, Vendor, Demand Management, Marketing Manager, Sales Manager, Service Manager, Demand Forecasting, Retail Manager, Warehouse Manager, Salesperson, and Distribution Center Manager.

[0239] In embodiments, the digital twin can represent recommendations for training the workforce, recommendations for augmenting the workforce, recommendations for configuring a series of operations involving the workforce, the capabilities and capabilities of a collection of workers and a collection of impersonators, and / or a collection of mixed workgroups of human workers and artificial intelligence impersonators.

[0240] Embodiments of this disclosure provide a method for providing a digital twin, which includes: a processing system for a digital twin system receiving a digital twin request from a user device of a user relating to an enterprise, wherein the enterprise deploys a sensor system to monitor one or more of the enterprise's facilities, and the processing system determines the user's workforce role relating to the enterprise; the processing system generating a role-based digital twin corresponding to the user's workforce role based on a viewpoint view corresponding to the user's workforce role, wherein the role-based digital twin depicts one or more states and / or entities relating to the enterprise; and the processing system providing the role-based digital twin to the user device, the step of providing the role-based digital twin including: the processing system identifying a set of data types used to populate at least one of the states and / or entities of the role-based digital twin, wherein the set of data types includes one or more sensor data feeds received from a sensor system deployed by the enterprise; and the processing system connecting one or more sensor data streams to the role-based digital twin.

[0241] In an embodiment, generating a role-based digital twin may involve determining a perspective view corresponding to a user's workforce role based on the user's workforce role and a set of data types associated with that workforce role.

[0242] In one embodiment, determining a perspective view corresponding to a user's workforce role may include determining an appropriate level of granularity for each data type.

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

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

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

[0246] In this embodiment, the edge device receives sensor data from a set of sensors and selectively compresses the sensor data based on the values ​​indicated in the sensor data to obtain a sensor data stream.

[0247] In one embodiment, connecting one or more sensor streams may include receiving sensor data streams from an edge device and routing the sensor data streams to a user device that presents a role-based digital twin to the user.

[0248] In one embodiment, connecting one or more sensor streams may include receiving sensor data streams from an edge device, analyzing the sensor data streams to identify one or more fault conditions corresponding to objects being monitored by the sensor system, and routing indicators of the fault conditions to a user device presenting a role-based digital twin to the user.

[0249] In one embodiment, connecting one or more sensor streams may include receiving sensor data streams from an edge device, analyzing the sensor data streams to identify recommendations corresponding to the user's workforce role, and routing indicators of the recommendations to a user device that presents a role-based digital twin to the user.

[0250] In an embodiment, connecting one or more sensor streams may include receiving sensor data streams from an edge device, analyzing the sensor data streams to identify recommendations corresponding to a user's workforce role, and routing indicators of the recommendations to a user device that presents a role-based digital twin to the user.

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

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

[0253] In an embodiment, at least one workforce role may be selected from among the roles of factory manager, factory worker, power plant manager, power plant worker, equipment service, and equipment maintenance operator.

[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 settlement role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market structuring role, and a contract structuring role.

[0255] In one embodiment, at least one workforce role may be selected from the roles of Chief Marketing Officer, Product Development, Supply Chain Manager, Customer, Supplier, Vendor, Demand Management, Marketing Manager, Sales Manager, Service Manager, Demand Forecasting, Retail Manager, Warehouse Manager, Salesperson, and Distribution Center Manager.

[0256] Embodiments of this disclosure provide a method for providing a digital twin of a workforce, comprising the steps of: maintaining an information technology architecture to support a digital twin of a set of physical and digital entities, the architecture comprising: a set of sensors providing sensor data relating to a 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 processing the data; a set of network entities transporting the data obtained from the set of sensors and data streams; a set of data processing systems for extracting, transforming and loading the data transported by the network entities into a set of resources that are the source of the digital twin; and a method for representing the organizational structure of an enterprise in a digital twin for the enterprise: representing the organizational structure of an enterprise in a digital twin of an enterprise; analyzing the structure to infer relationships between a set of roles within the organizational structure; the relationships and roles defining the enterprise's workforce; determining a set of parameters that constitute the digital twin based on the inferred set of relationships; and configuring a presentation layer of the digital twin based on the set of parameters.

[0257] A more complete understanding of this disclosure will be derived from the following description and accompanying drawings, as well as the claims. All documents referenced herein are incorporated herein by reference. [Brief explanation of the drawing]

[0258] The accompanying drawings included to provide a better understanding of this disclosure illustrate embodiments of this disclosure and, together with the description, help to illustrate many aspects of this disclosure.

[0259] [Figure 1] Figure 1 is a block diagram showing the relationship between prior art of various entities and facilities in a supply chain.

[0260] [Figure 2] Figure 2 is a block diagram showing the system and process components and interrelationships of the value chain network as described in this disclosure.

[0261] [Figure 3] Figure 3 is another block diagram showing the system and process components and interrelationships of the value chain network as disclosed herein.

[0262] [Figure 4] Figure 4 is a block diagram showing the components and interrelationships of the system and process of the digital product network shown in Figures 2 and 3 of this disclosure.

[0263] [Figure 5] Figure 5 is a block diagram showing the components and interrelationships of a value chain network technology stack system and process in accordance with this disclosure.

[0264] [Figure 6]Figure 6 is a block diagram illustrating the platform and relationships for orchestrating the control of various entities in a value chain network, in accordance with this disclosure.

[0265] [Figure 7] Figure 7 is a block diagram showing the components and relationships in an embodiment of the value chain network management platform according to this disclosure.

[0266] [Figure 8] Figure 8 is a block diagram showing the components and relationships of a value chain entity managed by an embodiment of the value chain network management platform according to this disclosure.

[0267] [Figure 9] Figure 9 is a block diagram showing the network relationships of entities in the value chain network as described in this disclosure.

[0268] [Figure 10] Figure 10 is a block diagram showing the set of applications supported by a unified data processing layer in the value chain network management platform described herein.

[0269] [Figure 11] Figure 11 is a block diagram showing the components and relationships in an embodiment of the value chain network management platform according to this disclosure.

[0270] [Figure 12] Figure 12 is a block diagram showing the components and relationships of the data storage layer in an embodiment of the value chain network management platform according to this disclosure.

[0271] [Figure 13]Figure 13 is a block diagram showing the components and relationships of the adaptive intelligent system layer in an embodiment of the value chain network management platform according to this disclosure.

[0272] [Figure 14] Figure 14 is a block diagram illustrating an adaptive intelligence system for coordinated intelligence on a set of demand and supply applications for a category of goods, in accordance with this disclosure.

[0273] [Figure 15] Figure 15 is a block diagram showing the provision of a hybrid adaptive intelligence system for collaborative intelligence for a set of demand and supply applications or a category of goods, in accordance with the present disclosure.

[0274] [Figure 16] Figure 16 is a block diagram showing the provision of an adaptive intelligence system for predictive intelligence on a set of demand and supply applications for a product category, in accordance with the present disclosure.

[0275] [Figure 17] Figure 17 is a block diagram illustrating an adaptive intelligence system for classification intelligence for a set of demand and supply applications for a category of goods, in accordance with this disclosure.

[0276] [Figure 18] Figure 18 is a block diagram showing the provision of an adaptive intelligent system for generating automatic control signals for a set of demand and supply applications for a category of goods as disclosed herein.

[0277] [Figure 19]FIG. 19 is a block diagram showing training an artificial intelligence / machine learning system to generate information routing recommendations for a selected value chain network in accordance with the present disclosure.

[0278] [Figure 20] FIG. 20 is a block diagram showing a semi-sensory problem recognition system for recognizing pain points / problem states in a value chain network according to the present disclosure.

[0279] [Figure 21] FIG. 21 is a block diagram showing a set of artificial intelligence systems operating on value chain information to enable automatic adjustment of value chain activities for an enterprise in accordance with the present disclosure.

[0280] [[ID=第十七]] [Figure 22] FIG. 22 is a block diagram showing components and relationships involved in integrating a set of digital twins in one embodiment of a value chain network management platform according to the present disclosure.

[0281] [Figure 23] FIG. 23 is a block diagram showing a set of digital twins involved in an embodiment of a value chain network management platform according to the present disclosure.

[0282] [Figure 24] FIG. 24 is a block diagram showing components and relationships of an entity discovery and management system in an embodiment of a value chain network management platform according to the present disclosure.

[0283] [Figure 25] FIG. 25 is a block diagram showing components and relationships of a robotic process automation system in an embodiment of a value chain network management platform according to the present disclosure.

[0284] [Figure 26] Figure 26 is a block diagram showing the components and relationships of the collection of opportunity miners in an embodiment of the value chain network management platform according to this disclosure.

[0285] [Figure 27] Figure 27 is a block diagram showing the components and relationships of the set of edge intelligence systems in an embodiment of the value chain network management platform according to this disclosure.

[0286] [Figure 28] Figure 28 is a block diagram showing the components and relationships in an embodiment of the value chain network management platform according to this disclosure.

[0287] [Figure 29] Figure 29 is a block diagram showing additional details of the components and relationships in the embodiment of the value chain network management platform according to this disclosure.

[0288] [Figure 30] Figure 30 is a block diagram showing the components and relationships in an embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities according to this disclosure.

[0289] [Figure 31] Figure 31 is a block diagram showing the components and relationships of the unified database in an embodiment of the value chain network management platform according to this disclosure.

[0290] [Figure 32] Figure 32 is a block diagram showing the components and relationships of a set of unified data collection systems in an embodiment of the value chain network management platform according to this disclosure.

[0291] [Figure 33] FIG. 33 is a block diagram showing components and relationships of a series of Internet of Things monitoring systems in an embodiment of a value chain network management platform according to the present disclosure.

[0292] [Figure 34] FIG. 34 is a block diagram showing components and relationships of a machine vision system and a digital twin in an embodiment of a value chain network management platform according to the present disclosure.

[0293] [Figure 35] FIG. 35 is a block diagram showing components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0294] [Figure 36] FIG. 36 is a block diagram showing additional details of components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0295] [Figure 37] FIG. 37 is a block diagram showing components and relationships of a set of unified adaptive intelligent systems in an embodiment of a value chain network management platform according to the present disclosure.

[0296] [Figure 38] FIG. 38 is a schematic diagram of a system configured to train an artificial system utilized by a value chain system using real-world result data and a digital twin system according to some embodiments of the present disclosure.

[0297] [Figure 39]Figure 39 is a schematic diagram of a system configured, according to some embodiments of the present disclosure, to train an artificial system to be utilized by a container fleet management system using real-world result data and a digital twin system.

[0298] [Figure 40] Figure 40 is a schematic diagram of a system configured, according to several embodiments of the present disclosure, to train an artificial system that is utilized by a logistics design system using real-world result data and a digital twin system.

[0299] [Figure 41] Figure 41 is a schematic diagram of a system configured, according to some embodiments of the present disclosure, to train an artificial system to be utilized by a packaging design system using real-world result data and a digital twin system.

[0300] [Figure 42] Figure 42 is a schematic diagram of a system configured, according to some embodiments of the present disclosure, to train an artificial system to be utilized by a waste reduction system using real-world result data and a digital twin system.

[0301] [Figure 43] Figure 43 is a schematic diagram illustrating some examples of information technology systems for value chain artificial intelligence utilizing digital twins, according to several embodiments of the present disclosure.

[0302] [Figure 44] Figure 44 is a block diagram showing the components and relationships of a set of intelligent project management equipment in an embodiment of the value chain network management platform according to this disclosure.

[0303] [Figure 45]Figure 45 is a block diagram showing the components and relationships of the intelligent task recommendation system in an embodiment of the value chain network management platform according to this disclosure.

[0304] [Figure 46] Figure 46 is a block diagram showing the components and relationships of the routing system between nodes of a value chain network in an embodiment of the value chain network management platform according to this disclosure.

[0305] [Figure 47] Figure 47 is a block diagram showing the components and relationships of a dashboard for managing a series of digital twins in an embodiment of a value chain network management platform.

[0306] [Figure 48] Figure 48 is a block diagram showing the components and relationships in an embodiment of a value chain network management platform using a microservices architecture.

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

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

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

[0310] [Figure 52] Figure 52 is a perspective view showing an embodiment of a set of value chain network digital twins representing a virtual model of the set of value chain network entities as disclosed herein.

[0311] [Figure 53] Figure 53 is a perspective view showing an embodiment of the warehouse digital twin kit system according to this disclosure.

[0312] [Figure 54] Figure 54 is a perspective view illustrating an embodiment of a stress test performed on a value chain network in accordance with this disclosure.

[0313] [Figure 55] Figure 55 is a perspective view showing an embodiment of a method used by a machine to detect failures and predict any future failures of the machine in accordance with the present disclosure.

[0314] [Figure 56] Figure 56 is a perspective view showing an embodiment of a machine twin deployment for performing predictive maintenance on a set of machines according to this disclosure.

[0315] [Figure 57] Figure 57 is a schematic diagram illustrating some examples of systems for value chain customer digital twins and customer profile digital twins according to several embodiments of the present disclosure.

[0316] [Figure 58] Figure 58 is a schematic diagram showing an example of an advertising application that interfaces with the adaptive intelligent system layer according to this disclosure.

[0317] [Figure 59] Figure 59 is a schematic diagram illustrating an example of an e-commerce application integrated with the adaptive intelligent system layer according to this disclosure.

[0318] [Figure 60] Figure 60 is a schematic diagram illustrating an example of a demand management application integrated with the adaptive intelligent system layer according to this disclosure.

[0319] [Figure 61] Figure 61 is a schematic diagram showing an example of a system portion for a value chain smart supply component digital twin according to some embodiments of the present disclosure.

[0320] [Figure 62] Figure 62 is a schematic diagram illustrating an example of a risk management application that interfaces with the adaptive intelligent system layer according to this disclosure.

[0321] [Figure 63] Figure 63 is a perspective view of the maritime assets related to the value chain network management platform, including port infrastructure components, as disclosed in this disclosure.

[0322] [Figure 64] Figures 64 and 65 are illustrative diagrams of maritime assets related to a value chain network management platform, including ship components, in accordance with this disclosure. [Figure 65] Figures 64 and 65 are illustrative diagrams of maritime assets related to a value chain network management platform, including ship components, in accordance with this disclosure.

[0323] [Figure 66] Figure 66 is a perspective view of maritime assets related to a value chain network management platform, including barge components, in accordance with this disclosure.

[0324] [Figure 67]Figure 67 is a perspective view of maritime assets related to the value chain network management platform, including those involved in maritime events, legal proceedings, and the use of geofence parameters, in accordance with this disclosure.

[0325] [Figure 68] Figure 68 is a schematic diagram illustrating exemplary environments of enterprise and executive control towers and management platforms, including data sources that communicate with them, according to several embodiments of the present disclosure.

[0326] [Figure 69] Figure 69 is a schematic diagram showing an exemplary set of components of an enterprise control tower and management platform according to some embodiments of the present disclosure.

[0327] [Figure 70] Figure 70 is a schematic diagram illustrating examples of enterprise data models according to several embodiments of the present disclosure.

[0328] [Figure 71] Figure 71 is a schematic diagram illustrating different types of enterprise digital twins, including executive digital twins, related to the data layer, processing layer, and application layer of an enterprise digital twin framework according to several embodiments of the present disclosure.

[0329] [Figure 72] Figure 72 is a schematic diagram illustrating exemplary implementations of enterprise and executive control towers and management platforms according to several embodiments of the present disclosure.

[0330] [Figure 73] Figure 73 is a flowchart illustrating a set of operations for configuring and delivering an enterprise digital twin.

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

[0332] [Figure 75] Figure 75 shows an example of a set of operations for generating an executive digital twin.

[0333] [Figure 76] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 77] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 78] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 79]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 80] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 81] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 82] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 83]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 84] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 85] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 86] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 87]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 88] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 89] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 90] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 91]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 92] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 93] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 94] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 95]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 96] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 97] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 98] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 99]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 100] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 101] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 102] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 103]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that can be connected to and integrated and accessed by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes according to embodiments of the present disclosure. [Modes for carrying out the invention]

[0334] Over time, companies have increasingly adopted technological solutions that improve traditional supply chain outcomes, as illustrated in Figure 1, such as software systems for forecasting and managing customer demand, RFID and asset tracking systems for tracking goods moving through the supply chain, and navigation and route selection systems for improving the efficiency of route selection. However, several major trends are pushing companies, such as manufacturers and retailers, to improve the performance of their supply chains. First, online and e-commerce operators, particularly Amazon®, have become the largest retail channels for many product categories, operating 112 distribution and fulfillment centers across some regions, including the United States, housing hundreds of thousands, sometimes more, of product categories (SKUs), enabling customers to receive their orders the day after they place them, and sometimes even the same day (sometimes delivered to their doorstep by drones, robots, and / or autonomous vehicles). For retailers who do not geographically distribute their fulfillment centers and warehouses over a wide area, customer expectations for delivery speed increase the pressure on supply chain efficiency and optimization. Thus, the need for improved supply chain methods and systems remains.

[0335] Secondly, agile manufacturing capabilities such as 3D printing and robotic assembly technologies, customer profiling techniques, and online evaluation and review are increasing customer expectations for product customization and personalization. Therefore, manufacturers and retailers need to improve the methods and systems for understanding, predicting, and satisfying customer demand in order to stay competitive.

[0336] Historically, supply chain management and demand planning and control were primarily separate activities, unified mainly when demand was converted into orders and passed to the supply side for fulfillment within the supply chain. As expectations for speed and personalization increase, there is a need for methods and systems that can provide unified orchestration of supply and demand.

[0337] Alongside these major trends, the emergence of the Internet of Things has enabled several categories of products, particularly smart home products such as thermostats, lighting systems, and speakers, to become increasingly powerful with onboard network connectivity and processing capabilities, often including voice-controlled intelligent agents such as Alexa® and Siri®, which can trigger specific application functions such as device control or music playback, or enable product ordering. In some cases, smart products may even initiate orders, such as ordering refill cartridges for a printer. While intelligent products may sometimes be involved in collaborative systems, such as Amazon®'s Echo® products controlling a TV or sensor-enabled thermostats or security cameras connecting to mobile devices, most intelligent products are still involved in a large, isolated set of application-specific interactions. As artificial intelligence capabilities increase and more computing and networking capabilities move to network-enabled edge devices and systems present in all locations, systems, and facilities along the path from the manufacturer's entry point to the customer's 662 or retailer's 664 destination 612, there will be a need for intelligence, control, and automation that will dramatically improve all elements of supply and demand. Value chain network

[0338] Referring to Figure 2, a block diagram is presented in 200 showing the components and interrelationships of the system and processes of a value chain network. In exemplary embodiments, the “value chain network” as used herein means the elements and interconnections of historically separated demand management systems and processes and supply chain management systems and processes, made possible by the development and convergence of numerous diverse technologies. In exemplary embodiments, the value chain control tower 260 (for example, referred herein as the “Value Chain Network Management Platform,” “VCNP,” or simply “System,” or “Platform”) may be connected to, communicate with, or otherwise operationally coupled to data processing facilities, including, for example, a big data center (but not limited to, big data processing 230), and related processing functions that receive data flows, data pools, data streams, and / or other data configurations and transmission modes received from, for example, a digital product network 252, directly from customers (e.g., directly connected customers 250), or from several other third parties 220. Market orchestration activities and communications related to communications 210, analyses 232, or any other type of input may also be utilized by the value chain control tower for demand enhancement 262, synchronous planning 234, intelligent procurement 238, dynamic execution 240, or any other smart operations informed by coordinated and adaptive intelligence, as described herein.

[0339] Referring to Figure 3, another block diagram is presented showing the system and process components and interrelationships of the value chain network, as well as related use cases, data processing, and associated entities. In exemplary embodiments, the value chain control tower 360 may coordinate market orchestration activities 310, including but not limited to demand curve management 352, ecosystem synchronization 348, intelligent procurement 344, dynamic fulfillment 350, value chain analysis 340, and / or smart supply chain operations 342. In exemplary embodiments, the value chain control tower 360 may be further connected, communicate with, or otherwise operably coupled to an adaptive data pipeline 302 and an external data source 320 and a data processing stack 330 (e.g., value chain network technology), which may include an intelligent, user-adaptive interface, 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, communicate with, or otherwise operable with additional value chain entities, including but not limited to, the digital product network 360, customers (e.g., designated connected customers 362), and / or other connected operations 364 and value chain network entities. Digital Product Networks (hereinafter referred to as DPN)

[0340] Referring to Figure 4, a block diagram is presented showing the system and process components and interrelationships of the digital product network in 400. In exemplary embodiments, a product (including goods and services) may generate data, such as product-level data, and transmit it to the communication layer and / or edge data processing facilities within the value chain network technology stack. This data may generate enhanced product-level data and may be combined with third-party data for further processing, modeling, or other adaptive or collaborative intelligence activities, as described herein. This includes, but is not limited to, generating and / or simulating use cases for products and the value chain, and the data for which can be used by products, product development processes, product designs, etc. Stack View Example

[0341] Referring to Figure 5, a block diagram 500 is shown illustrating the system and process components and interrelationships of the value chain network technology stack, which may include, but is not limited to, a presentation layer, an intelligence layer, and serverless functionalities such as a platform (e.g., a development platform and a host platform), data facilities (e.g., data relationships with IoT and big data), and data aggregation facilities. In an exemplary embodiment, the presentation layer may include, but is not limited to, a user interface, and modules for investigation and discovery, as well as tracking user experience and engagement. In an exemplary embodiment, the intelligence layer may include, but is not limited to, statistical and computational methods, semantic models, analytical libraries, development environments for analysis, algorithms, logic and rules, and machine learning. In an exemplary embodiment, the platform or value chain network technology stack may include a development environment, APIs for connectivity, cloud and / or hosting applications, and device discovery. In an exemplary embodiment, 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 heterogeneous devices. In exemplary embodiments, the data infrastructure or layer may include, but is not limited to, access, control, and collection of IoT and big data. In exemplary 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] Figure 6 illustrates a linked value chain network 668 in which a value chain network management platform 604 (hereinafter referred to as the “Value Chain Control Tower,” “VCNP,” or simply “System,” or “Platform” in some cases) directs various factors related to the planning, monitoring, control, and optimization of various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, and logistics and distribution factors. A unified platform 604 for monitoring and managing not only supply and demand factors but also status information (e.g., quality and status, planning, ordering and confirmation, and / or tracking) may be shared with and among various entities (e.g., distribution, such as customers / consumers, suppliers, distributors, etc., and production, such as producers and production facilities) so that demand factors are understood and explained, orders are generated and fulfilled, and products are created and moved throughout the supply chain. The value chain network 668 may include not only intelligent products 650 but also all facilities, infrastructure, personnel, and other entities involved in planning and meeting the demand for them. Value chain network and value chain network management platform

[0343] Referring to Figure 7, the value chain network 668 managed by the value chain management platform 604 may include, but are not limited to, a set of value chain network entities 652 such as: a set of production facilities 674 involved in the production of products 650 which may be intelligent products 650, finished goods, components, systems, subsystems, materials used in goods, etc.; suppliers 642; various entities, activities, and other supply factors 648 involved in the supply environment 670 such as places of origin 610; various entities, activities, and other demand factors 644 involved in the demand environment 672 such as customers 662 located and / or operating in various destinations 612 (including consumers, businesses, and intermediate customers such as value-added resellers and distributors), retailers 664 (including online retailers, mobile retailers, traditional brick-and-mortar retailers, pop-up shops, etc.), and such. These include various distribution environments 678 and distribution facilities 658 such as warehouse facilities 654, fulfillment facilities 628, and distribution systems 632, as well as maritime facilities 622 such as port infrastructure facilities 660, floating assets 620, and shipyards 638. In embodiments, the value chain network management platform 604 enables monitoring, control, and other means of managing (and in some cases operating autonomously or semi-autonomously) a wide range of processes, workflows, activities, events, and applications 630 (in some cases collectively referred to simply as "Applications 630") of the value chain network 668.

[0344] Referring further to Figure 7, a high-level schematic diagram of the Value Chain Network Management Platform 604 is shown. The Value Chain Network Management Platform 604 may include a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements that work together to enable the intelligent management of a set of value chain entities 652 that occur, operate, trade, etc., within which they may be owned, operate, support, or enable. Other elements that work together to enable the intelligent management of a set of value chain entities 652 that may be part of, integrated, linked, or operated by VCNP 604 in relation to one or more value chain network processes, workflows, activities, events and / or applications 630 or products 650 (which may be products of any category such as finished goods, software products, hardware products, component products, materials, equipment items, consumer packaged goods items, etc.). The intelligent product 650 may be a consumer product, food product, beverage product, household goods, business supplies, consumables, pharmaceuticals, medical device products, technology products, entertainment products, or any other type of product and / or related service set, and in embodiments may include, but are not limited to, an intelligent product 650 that enables a set of functions such as data processing. The intelligent product 650 may include a set of functions, not particularly limited to, networking, sensing, autonomous operation, intelligent agents, natural language processing, speech recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, voice processing systems, data storage, data integration, and / or various Internet of Things capabilities.

[0345] In embodiments, the management platform 604 may include a set of data processing layers 624, each configured to provide a set of capabilities that facilitate the development and deployment of intelligence, such as facilitating automation, machine learning, artificial intelligence applications, 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 processing layer 624 is configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604, through a value chain monitoring system layer 614. The value chain monitoring system layer 614 may integrate and / or cooperate with various data collection and management systems 640, sometimes conveniently referred to as data collection systems 640, to collect and organize data from or about value chain entities 652, as well as data from or about various data layers 624 or services or components. In embodiments, the data processing layer 624 is 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 system layer 624, which may in some cases be conveniently referred to herein simply as the data storage layer 624 or storage layer 624. As shown in Figure 7, the data processing layer 624 may also include an adaptive intelligence system layer 614. The adaptive intelligence system layer 614 may include a set of data processing, artificial intelligence, and computing systems 634, which are described in more detail elsewhere throughout this disclosure. The data processing, artificial intelligence, and computing systems 634 may relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, model-based systems, etc.).Specifically, the data processing, artificial intelligence, and computing systems 634 may, in some embodiments, be used in the following ways: the use of recurrent networks as adaptive intelligent systems operating on a blockchain of transactions in a supply chain to determine patterns; use with biosystems; opportunity mining (e.g., used to monitor new data sources as opportunities for artificial intelligence systems to automatically deploy intelligence); robotic process automation (e.g., automation of intelligent agents for various workflows); edge and network intelligence (e.g., involved in monitoring systems such as adaptive use of available RF spectrum, adaptive use of available fixed network spectrum, adaptive storage of data based on available storage conditions, and adaptive sensing based on a kind of contextual sensing).

[0346] In embodiments, the data processing layer 624 may be depicted as a vertical stack or ribbon in the figure and can represent many functionalities available to platform 604, including storage, monitoring, and processing applications and resources, as well as combinations thereof. In embodiments, the set of functionalities of the data processing layer 624 may include a shared microservices architecture. In these examples, the set of capabilities may be deployed to provide several different services or applications, which may be configured as one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within a particular application or process, or reside within a particular application or process. In some examples, the set of capabilities may include one or more activities marshalled 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 platform's set of capabilities may be deployed within at least a portion of a common architecture, such as a common architecture that supports a common data schema. In embodiments, one of the platform's set of capabilities may be deployed within at least a portion of a common architecture that can support common storage. In embodiments, one of the platform's set of capabilities may be deployed within at least a portion of a common architecture that can support a common monitoring system. In embodiments, one or more sets of capabilities of the platform may be deployed within at least part of a common architecture capable of supporting one or more common processing frameworks. In embodiments, the set of capabilities of the data processing layer 624 may include examples where the storage capabilities support scalable processing capabilities, scalable monitoring systems, digital twin systems, payment interface systems, and the like.These examples show that one or more software development kits may be provided by the platform, along with deployment interfaces to facilitate the connection and use of the capabilities of the data processing layer 624. In further examples, an adaptive intelligent system may analyze, ream, configure, and reconfigure one or more of the capabilities of the data processing layer 624. In embodiments, platform 604 may include, for example, a common data storage schema that serves shipyard entity-related services and warehouse entity services. Many other applications and combinations are applicable to the above examples, which include many of the value chain entities disclosed herein. These examples show that 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 pairs of similar types of value chain entities (duplicate, triple, quadruple, etc.) that use one or more smaller sets of capabilities of the data processing layer 624 to deploy (interact with, depend on, etc.) a common data schema, a common architecture, a common interface, etc. While services and capabilities can be provided to a single value chain entity, a platform can be demonstrated to offer countless benefits to the value chain and consumers by supporting connectivity between value chain entities and the applications used by those entities. The value chain network managed by the platform is the main entity.

[0347] Refer to Figure 8. 8. The value chain network management platform 604 is illustrated in relation to a set of value chain entities 652 that may be subject to management by the platform 604, may be integrated with the platform 604, and / or may supply inputs to the platform 604, and / or may take outputs from the platform 604, such as those involved in or relating to a wide range of value chain activities (supply chain activities, logistics activities, and other activities), such as demand management and planning activities, distribution activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others relating to various value chain network processes, workflows, activities, events, and applications 630 (collectively referred to as “Applications 630” or simply “Activities”). Connection with the value chain entities 652 may be facilitated by a set of connectivity equipment 642 and interfaces 702, including a wide range of components and systems described in more detail throughout this disclosure and below. This may include connectivity and interface functions for individual services of the platform, for the data processing layer, for the entire platform, and / or between value chain entities 652.

[0348] These value chain entities 652 may include, but are not limited to, any of the wide variety of assets, systems, devices, machines, components, equipment, individuals or other entities referred to in this disclosure or in documents incorporated herein by reference: machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loaders, cranes, lifts, transport vehicles, trucks, loaders, unloaders, packaging machines, picking machines, and many others); 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 finished goods, software products, hardware products, component products, materials, equipment items, consumer goods package items, consumer goods, food products, beverage products, household goods, business supplies, consumables, pharmaceuticals, medical device products, technology products, entertainment products, or sets of other types of products and / or related services); value chain processes 722 (e.g., shipping processes, transportation processes, sea shipping processes, etc.) Processes, inspection processes, transportation processes, loading / unloading processes, packaging / thawing processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, etc.), border control processes, port-related processes, software processes (including applications, programs, services, etc.), packaging and loading processes, financial processes (insurance processes, reporting processes, transaction processes, etc.), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, etc.); wearables and portable devices 720 (mobile phones, tablets, portable devices dedicated to value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, headwarm devices, clothing-integrated devices, armbands, bracelets, neck-worn devices, AR / VR devices, headphones, etc.); workers 718 (delivery drivers, seafarers, barge workers, port workers, dock workers, train workers, sailors,Fulfillment center distribution workers, warehouse workers, vehicle drivers, business managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, load handlers, inspectors, delivery personnel, environmental management managers, financial asset managers, process supervisors and (for any of the processes described herein) workers, security personnel, safety officers and many others, etc., suppliers 642 (suppliers of all kinds of goods and related services, parts suppliers, component suppliers, material suppliers, manufacturers, and their (and many others), customers 662 (including consumers, licensees, businesses, corporations, value-added and other resellers, retailers, end users, distributors, and others who purchase, license or otherwise use categories of goods and / or related services), extensive operating facilities 712 (including loading / unloading docks, storage and warehousing facilities 654, safes, distribution facilities 658 and fulfillment centers 628), air travel facilities 740 (including aircraft, airports, hangars, runways, refueling depots, etc.), offshore facilities 622 (including docks, yards, cranes, roll-on / Items or factors that take demand into account (i.e., demand factors 644) (market) include port infrastructure facilities such as roll-off facilities, ramps, containers 622, container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (ships, barges, boats, etc.), facilities and other goods at the origin 610 and / or destination 628, transport facilities 710 (container ships, barges, other floating assets 620, as well as land vehicles and other delivery systems used for transporting goods, such as trucks and trains 632, etc.) Factors, events, and many others); items or elements that are factors of supply (i.e., supply factors 648) (including market factors, weather, availability of parts and materials, and many others); logistics factors 750 (including availability of transport routes, weather, fuel prices, regulatory factors, availability of space (on vehicles, in containers, in packages, in warehouses, in fulfillment centers, on shelves, and many others), retailers 664 (including online retailers such as eCOMMERCE sites 730); routes for transport (waterways 732, roads 734, air routes,Examples include railways (738, etc.), robotic systems (744, including mobile robots, cobots, robotic systems to assist human workers, robotic delivery systems, etc.), drones (748, including for package delivery, site mapping, monitoring or inspection, etc.), autonomous vehicles (742, including for package delivery, etc.), software platforms (752, including 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, Infrastructure as a Service, software-based data storage platforms, analytics platforms, artificial intelligence platforms, etc.), and many others. In some exemplary embodiments, product 650 may be encompassed as intelligent product 650, or VCNP 604 may include intelligent product 650. The intelligent product 650 may be enabled by a set of functions, but is not limited to, data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, speech recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computing, artificial intelligence, analog or digital sensors, cameras, voice processing systems, data storage, data integration, and / or various Internet of Things functions. The intelligent product 650 may also include a form of information technology. The intelligent product 650 may comprise a processor, computer random access memory, and communication modules. The intelligent product 650 may be a passive intelligent product similar to an RFID-type data structure that can be pinged or read. The product 650 may also be considered as a value chain network entity (e.g., under the control of a platform) that surrounds the infrastructure and may be made intelligent by adding RFID so that data can be read from the intelligent product 650. The intelligent product 650 may include sensors, IoT devices, tags,Alternatively, it may be adapted to a value chain network in a connectivity manner such that connectivity is established around the intelligent product 650 via another component.

[0349] In embodiments, the monitoring system layer 614 may monitor any or all of the value chain entities 652 in the value chain network 668, exchange data with the value chain entities 652, provide control instructions to or take instructions from any of the value chain entities 652 through the various functions of the data processing layer 624 described throughout this disclosure, and so on. Characteristics of a value chain network; entities within the network

[0350] Referring to Figure 9, the orchestration of a set of deeply interconnected value chain network entities 652 within a value chain network 668 by the value chain network management platform 604 is illustrated. Each value chain network entity 652 may have a connection to VCNP 604, a connection to another set of value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a cloud connection, or other connection), and / or a connection to another value chain network entity 652 through VCNP 604. The value chain network management platform 604 can manage the connections, configure or provide resources that enable the connections, and / or manage applications 630 that utilize the connections, by coordinating the activities of a set of entities 652, by providing input about the artificial intelligence system of VCNP 604 or a set of entities 652, and by interacting with entities 652 and edge computing systems deployed on or in their environment, etc.

[0351] Entity 652 may be external, such that VCNP604 can interact with these entities 652. VCNP604 may function as a control tower for establishing monitoring (e.g., establishing monitoring such as common monitoring across multiple entities 652). There may be an interface on a single unified platform where a user can view various items such as their destinations, ports, air and rail assets, as well as orders. Next, a common data schema may be established to enable services operating in any one of these applications. This may include taking any of the data flowing through or about any of these entities 652 and drawing the data into a framework that other applications across supply and demand can interact with the entities 652. This may be a shared data pipeline coming from IoT systems and other external data sources, fed into the monitoring layer, stored in the common data schema in the storage layer, and then various intelligences may be trained to identify meaning across these entities 652. In an exemplary embodiment, if a supplier goes bankrupt or a determination is made that a supplier has gone bankrupt, VCNP604 can then automatically trigger an alternative smart contract and send it to a secondary supplier with modified terms. It could also involve managing different aspects of the supply chain. For example, in response to the confirmation of bankruptcy of another supplier (e.g., following a bankruptcy announcement), the demand side could immediately and automatically change its pricing. Other similar examples could be used based on what happens at its automation layer, which can be enabled by VCNP604. Then, at the interface layer of this VCNP604, a digital twin could be used to allow the user to view all these entities 652 that would not normally be displayed together and monitor what is happening at each of these entities 652, including identifying problem conditions. For example, after seeing a poor three-quarter financial report regarding a supplier, the report could be flagged to closely monitor the possibility of future bankruptcy, and so on.

[0352] For example, an IoT system deployed at fulfillment center 628 may work with an intelligent product 650 to receive customer feedback on the product 650, and application 630 for fulfillment center 628, upon receiving customer feedback on a problem with product 650 via a connection to the intelligent product 650, may initiate a workflow to take corrective action on similar products 650 before they are shipped from fulfillment center 628. Similarly, port infrastructure facilities 660, such as yards for holding shipping containers, may, via connections to floating assets 620 (ships, barges, etc.), notify a fleet of floating assets 620 when the port is nearing capacity, thereby initiating a negotiation process for the remaining capacity (which may include automated negotiations governed by smart contracts based on a set of rules) to redirect some assets 620 to alternative ports or holding facilities. These and many other connections between value chain network entities 652, whether one-to-one, one-to-many, many-to-many, or connections between defined groups of entities 652 (such as those controlled by the same owner or operator), are included herein as applications 630 managed by VCNP 604. Value chain network activities and applications managed by the platform

[0353] Referring to Figure 10, the set of applications 630 provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or relating to a set of value chain network entities 652, may include, but are not limited to, one or more of a wide range of application types such as the following: Supply chain management applications 812 (for managing the timing, quantity, logistics, shipment, delivery, and other details of orders for goods, components, and other items, etc., etc.), asset management applications 814 (for managing value chain assets such as, for example, floating assets (ships, boats, barges, and floating platforms, etc.), real estate (used for the locations of warehouses, ports, shipyards, distribution centers, and other buildings, etc.), equipment, machinery and fixtures (used for handling containers, etc., etc.), cargo, packages, goods, and other items, not limited to these), vehicles (forklifts, delivery trucks, autonomous vehicles, and other systems used to move goods, etc.), human resources (workers, etc.), software, information technology resources, data processing resources, data storage resources, power generation and / or energy storage resources, computing resources, and other assets), financial applications 82 2 (for example, but not limited to, applications for handling financial matters relating to entities and assets in the value chain, including payments, collateral, debt, customs, duties, levies, taxes, etc.), Risk Management Applications 818 (for managing risks or liabilities relating to shipments, goods, products, assets, people, floating assets, vehicles, equipment, parts, information technology systems, security systems, security events, cybersecurity systems, property, health conditions, death, fire, flood, weather, failure, negligence, business interruption, injury, property damage, business damage, breach of contract, etc., but not limited to, applications for managing risks or liabilities relating to shipments, goods, products, assets, people, floating assets, vehicles, equipment, parts, information technology systems, security systems, security events, cybersecurity systems, property, health conditions, death, fire, flood, weather, failure, negligence, business interruption, injury, property damage, business damage, breach of contract, etc.), Demand Management Applications 824 (for example, demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, marketing analytics applications, customer relationship management applications, etc.)Applications that analyze, plan, or promote customer interest in categories of goods that can be supplied by or using value chain products or services, search engine optimization applications, sales applications, customer relationship management applications, search engine optimization applications, sales management applications, advertising network applications, behavioral tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, product or service recommendation engines, and others (including those that use or are enabled by using one or more features of Intelligent Product 650, or are performed using intelligence functions on Intelligent Product 650), transaction applications 858 (but not limited to purchase applications, sales applications, bidding applications, auction applications, reverse auction applications, bid / ask matching applications, value chain performance, yield, return on investment, or other metrics). Analytical applications for analysis, or other such things), tax applications 850 (for managing, calculating, reporting, optimizing, or otherwise handling data, events, workflows, etc. related to taxes, duties, levies, tariffs, customs duties, credits, fees or other charges imposed by the government, such as customs duties, value-added tax, sales tax, income tax, property tax, municipal fees, pollution tax, renewable energy credits, pollution mitigation credits, import taxes, export taxes, etc.), identity management applications 830 (for managing the identity of one or more entities involved in the value chain, but not limited to identity verification applications, biometric verification applications, pattern-based identity verification applications, location-based identity verification applications, user behavior-based applications, fraud detection applications, network address-based fraud detection applications, blacklist applications, whitelist applications, content inspection-based fraud detection applications,Inventory management applications 820 (not limited to) for managing inventory in fulfillment centers, distribution centers, warehouses, storage facilities, stores, ports, ships, or other floating assets or other locations, which are one or more other fraud detection applications, security applications, solutions or services 834 (hereinafter referred to as security applications, for example, but not limited to any of the identity management applications 830 described above, physical security systems (for example, access control systems (using biometric access control, fingerprints, retinal scans, passwords and other access controls, etc.), safes, vaults, cages, safe rooms, secure storage facilities, etc.), surveillance systems (using cameras, etc.), surveillance systems (using cameras, etc.), perimeter security systems, floating security systems for floating assets, cybersecurity systems (virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineer detection and remediation, cyberattack detection and remediation, packet inspection, traffic inspection, Security applications 840 (including, but not limited to, any applications that detect, characterize or predict the likelihood and / or extent of an accident or other damage event, such as DNS attack remediation and detection or other security applications), security applications 844 (including, but not limited to, distributed ledger or other blockchain-based applications that capture a series of transactions such as debits or credits, purchases or sales, exchanges of physical considerations, smart contract events, etc.), facility management applications 850 (e.g., infrastructure, buildings, systems, real estate, movable property, etc., involved in supporting value chains such as shipyards, ports, distribution centers, warehouses, docks, stores, fulfillment centers, storage facilities, etc.),For the purpose of managing and designing other assets, and for the purpose of managing or controlling systems and equipment on or around the premises, including but not limited to information technology systems, robot / autonomous vehicle systems, packaging systems, packing systems, picking systems, inventory tracking systems, inspection systems, routing systems for mobile robots, and workflow systems for human assets, regulatory applications 852 (including but not limited to applications to regulate any of the applications, services, transactions, activities, workflows, events, entities, or other items described herein and in documents incorporated herein by reference, such as permitted routes, permitted goods and products, permitted parties to transactions, required disclosures, privacy, pricing, marketing, provision of goods and services, use of data (such as data privacy regulations, data retention regulations), banking, marketing, sales, financial planning, and many other regulations), commerce applications, solutions or services 854 (e-commerce sites, marketplaces, online sites, auction sites or markets 832 (an application for managing a set of vendors or prospective vendors and / or for managing the procurement of a set of goods, components or materials that may be supplied in a value chain, such as vendor qualification, vendor rating, request for proposal, request for information, other assurance of debt or performance, contract management, etc.), 838 (an analytical application relating to any of the data types, applications, events, workflows or entities referred to in this disclosure or through documents incorporated herein by reference, such as big data applications, user behavior applications, predictive applications, classification applications, dashboards, pattern recognition applications, econometric applications, financial yield applications, return on investment applications, etc.)Scenario planning applications, decision support applications, demand forecasting applications, demand planning applications, route planning applications, weather forecasting applications, and many other applications), pricing applications 842 (for example, but not limited to goods, services (including those referred to through this disclosure and documents incorporated herein by reference), and smart contract applications, solutions, or services (collectively referred herein as smart contract applications 848, for example, any of the smart contract types referred to in this disclosure or documents incorporated herein by reference, such as smart contracts for the sale of goods, smart contracts for the order of goods, smart contracts for shipping resources, smart contracts for workers, smart contracts for the delivery of goods, smart contracts for the installation of goods, etc., smart contracts for the consideration of tokens or cryptocurrency Smart contracts include smart contracts that determine rights, options, futures, or interests based on future conditions; smart contracts relating to securities, commodities, futures, options, derivatives, etc.; smart contracts relating to current or future resources; smart contracts configured to consider or address parameters of taxes, regulations, or compliance; smart contracts configured to execute arbitrage trades, etc., or many others. Therefore, the value chain management platform 604 may, through shared microservices, shared data infrastructure, and shared intelligence, enable and host interactions between a wide range of heterogeneous applications 630 (including the above and other value chain applications, services, solutions, etc.) so that any set or larger combination or permutation of such services can be improved compared to isolated applications of the same type.

[0354] Referring further to Figure 10, the set of applications 630 provided on VCNP 604, integrated with VCNP 604, and / or managed by or for VCNP 604, and / or including a set of value chain network entities 652, may further include, but are not limited to: Payment applications 860 (for any of the applications 630 described herein, for the calculation of payments (including those based on situational factors such as taxes and tariffs applicable to the geography of entities 652), transfer of funds, settlement of payments to parties, etc.); Process management applications 862 (for the management of any of the processes or workflows described throughout this disclosure, including supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, order processes, navigation processes, and many others). Compatibility testing applications 864 include, for example, to evaluate the compatibility between value chain network entities 652 or activities involved in any of the processes, workflows, activities, or other applications 630 described herein (e.g., compatibility between containers or packages and product 650, to determine the compatibility of product 650 with a set of customer requirements, etc.; compatibility between product 650 and another product 650 (e.g., when one is a replenishment, replacement, or replacement part for the other), compatibility between infrastructure and equipment entities 652 (e.g., between container ships or barges and ports or waterways, between containers and storage facilities, between trucks and roads, between drones or robots and packages, between drones, AVs or robots and delivery destinations, etc.); and infrastructure testing applications 802 (to test the capabilities of infrastructure elements supporting product 650 or application 630 (but not limited to storage capabilities, lifting capabilities, mobility capabilities, storage capacity, network capabilities, environmental control capabilities, software capabilities, security capabilities, and many others)).and / or Incident Management Application 910 (for managing events, accidents, and other incidents that may occur in one or more environments, including Value Chain Network Entity 652, but not limited to, vehicle accidents, worker injuries, breakdowns, property damage, product damage, product liability, regulatory non-compliance, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, etc., and combinations thereof), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, misuse incidents, and many others).

[0355] Furthermore, referring to Figure 10, the set of applications 630 that are provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or include a set of value chain network entities 652, may also include, but are not limited to, the following: Predictive maintenance applications 910 (for predicting, forecasting, and implementing actions to manage failures, malfunctions, shutdowns, damage, necessary maintenance, necessary repairs, necessary services, and necessary support for a set of value chain network entities 652 such as products 650, equipment, infrastructure, buildings, and vehicles); logistics applications 912 (for logistics management for other activities related to scheduling and managing the movement of products 650 and other items between a point of origin and a destination via various intermediate locations, such as pickup, delivery, transfer of goods to transport facilities, loading, unloading, packing, picking, shipping, driving; reverse logistics applications 914 (for handling the logistics of returned products 650, waste, damaged goods, or other items that can be transferred via a return logistics route); and waste reduction applications 920 (for reducing packaging waste, solid waste, energy waste, liquid waste, pollution, computing resource waste, human resource waste, or other waste related to value chain network entities 652 or activities).Augmented reality, mixed reality and / or virtual reality applications 930 (for example, but not limited to, the movement of product 650, the interior of a facility, the condition or state of an item of goods, one or more environmental conditions, weather conditions, packaging configuration for a container or set of containers, or one or more value chain network entities 652 or activities relating to one or more of the applications 630); demand forecasting applications 940 (for example, for forecasting demand for product 650, product categories, potential products, and / or factors influencing demand such as market factors, wealth factors, demographic factors, weather factors, economic factors, etc.); demand aggregation applications 942 (for one or more products 650, including current demand for existing products and future demand for products not yet available, A customer profiling application 944 (for profiling one or more demographic, psychological, behavioral, economic, geographical, or other attributes of a set of customers, such as based on historical purchase data, loyalty program data, behavioral tracking data (including data captured in customer interactions with smart products 650), online clickstream data, interactions with intelligent agents, and other data sources); and / or a component supply application 948 (for managing the supply chain of components for a set of products 650).

[0356] See Figure 10 for further reference. 10, The set of applications 630 that are provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or include a set of value chain network entities 652, may, but are not limited to, include: Policy management application 868 (for example, to manage the execution of one or more workflows (which may include configuring policies in platform 604 for each workflow), to manage compliance with regulations (including maritime, food and drug, medical, environmental, health, health insurance, etc.), to deploy one or more policies, rules, etc. for the management of one or more value chain network entities 652 or application 630) may, without limitation, further include: one policy, rule, or other for managing the execution of one or more workflows or other; to manage compliance with regulations (including maritime, food and drug, medical, environmental, health, safety, tax, financial reporting, commercial, and other regulations described throughout this disclosure or as will be understood in the art); to manage the provision of resources (such as connectivity, computing, human, energy, and other resources); to manage compliance with corporate policies; and to manage compliance with contracts (including smart contracts). Here, platform 604 may automatically deploy governance functions to the relevant entities 652 and applications 630, such as via connectivity equipment 642; govern interactions with other entities (including policies for information sharing and access to resources); govern data access (including privacy data, operational data, status data, and many other data types); govern secure access to infrastructure, products, equipment, locations, or similar; and many other things.Product configuration application 870 (for example, to enable a product manager and / or automated product configuration process (optionally using robotic process automation) to determine the configuration of product 650, including on-the-fly configuration such as during agile manufacturing, or configuration or customization at the route (such as by 3D printing of one or more features or elements), or remote configuration or customization such as firmware download, field-programmable gate array configuration, or software installation). Warehouse management and fulfillment application 872 (for managing warehouses, distribution centers, fulfillment centers, etc., including product selection, configuration of product storage locations, determination of routes for personnel and mobile robots to move products within the facility, picking and packing schedules, routes) This includes determining workflows, managing the operation of robots, drones, conveyors, and other facilities, determining the schedule for moving products to exits, and many other functions. It also provides kit configuration and deployment applications 874 (for example, to enable VCNP users to configure a kit, box, or otherwise a pre-integrated, pre-visioned, and / or pre-configured system so that customers or workers can quickly deploy a subset of VCNP 604 capabilities for specific value chain network entities 652 and / or applications 630), and / or product testing applications 878 for testing products 650 (including performance testing, capability and feature activation, safety, compliance with policies or regulations, quality, quality of service, potential for failure, and many other factors).

[0357] See also Figure 10. 10. A set of applications 630 provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or with a set of value chain network entities 652, may further include, but are not limited to, a maritime fleet management application 880 (for managing a set of maritime assets such as container ships, barges, boats, etc., to determine the optimal route of 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 indicators; and many other purposes), as well as managing related infrastructure facilities such as docks, cranes, and ports. A shipment management application 882 (for managing a set of shipment assets such as trucks, trains, and airplanes, etc., to optimize financial returns; to improve safety; to reduce energy consumption; to reduce delays; to mitigate environmental impacts; and many other purposes). Opportunity matching applications 884 (for matching one or more demand elements with one or more supply elements, for matching the needs and capabilities of value chain network entities 652, for identifying reverse logistics opportunities, for identifying input opportunities to enrich analysis, artificial intelligence and / or automation, for identifying cost reduction opportunities, for identifying profit and / or arbitrage opportunities, and many others). Workforce management applications 888 (for managing workers in various workforces, including workforces in or for fulfillment centers, ships, ports, warehouses, distribution centers, corporate management locations, retail stores, online / e-commerce site management facilities, ports, ships, boats, barges, trains, depots, and other facilities referred to through this disclosure), distribution and delivery applications 890 (for planning, scheduling, routing, and otherwise managing the distribution and delivery of products 650 and other items), and / or Enterprise Resource Planning (ERP) applications 892 (for planning the use of enterprise resources, including workforce resources, financial resources, energy resources, physical assets, digital assets, and other resources). Core functions and interactions of the data handling layer (adaptive intelligence, monitoring, data storage, applications) Referring to Figure 11, a high-level schematic diagram of an embodiment of the Value Chain Network Management Platform 604 is shown. Figure 11 shows a high-level schematic diagram of an embodiment of the Value Chain Network Management Platform 604, which includes a collection of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections and other elements that work together to enable the intelligent management of a collection of value chain entities 652 that occur, operate, trade, etc., or are owned, operated, supported, or enabled, or otherwise may be part of, integrated with, or linked to, one or more value chain network processes, workflows, activities, events and / or applications 630. Alternatively, a product 650 (which may be a finished product, software product, hardware product, component product, material, equipment item, consumer packaged goods, consumer product, food product, beverage product, household goods, business supply product, consumables, pharmaceuticals, medical device products, technology product, entertainment product, or any other type of product or related service, and which in embodiments may include intelligent products enabled by processing, networking, sensing, computing, and / or other Internet of Things functions) may be operated by platform 604 in connection with 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 other processes, workflows, activities, events and applications 630 of various value chain networks, and which may include a wide range of assets, systems, devices, machines, parts, equipment, individuals or other entities referred to in this disclosure or in documents incorporated herein by reference).

[0358] In embodiments, the value chain network management platform 604 may include a set of data processing layers 624, each configured to provide a set of functions for facilitating the development and deployment of intelligence for a wide variety of value chain network applications and end uses, such as automation, machine learning, artificial intelligence applications, intelligent trading, intelligent operation, remote control, analysis, monitoring, reporting, status management, event management, process management, and many others. In embodiments, the data processing layer 624 may include a value chain network monitoring system layer 614, a value chain network entity-oriented data storage system layer 624 (hereinafter referred to as the data storage layer 624 in some cases for convenience), an adaptive intelligent system layer 614, and a value chain network management platform layer 604. The Value Chain Network Management Platform Layer 604 may include a Data Handling Layer 624 to provide management of the Value Chain Network Management Platform Layer 604 and / or management of other layers such as the Value Chain Network Monitoring System Layer 614, the Value Chain Network Entity-Oriented Data Storage System Layer 624 (e.g., Data Storage Layer 624), and the Adaptive Intelligent System Layer 614. Each of the Data Processing Layers 624 may include various services, programs, applications, workflows, systems, components, and modules, as further described herein and in documents incorporated herein by reference. In embodiments, each of the Data Processing 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 other systems (e.g., as the Platform as a Service deployed on a set of cloud infrastructure components of a microservices architecture).For example, platform 604 may have (or configure and / or provide) a set of connectivity equipment 642, including network connectivity (including various configurations, types and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless links, and human-accessible interfaces, which data processing layer 608 may use; software interfaces, microservices, SaaS interfaces, PaaS interfaces, IaaS interfaces, cloud functions, or between data processing layer 608 and other layers, systems or subsystems of platform 604, as well as between other systems such as value chain entities 652 or external systems, and cloud-based or on-premise enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems, and many others). Each of the data processing layers 624 may include a set of data processing services (e.g., microservices), including equipment for data extraction, transformation and loading, data cleansing and deduplication, data normalization, data synchronization, data security, computing (e.g., for performing predefined computational operations on a data stream and providing an output stream), compression and decompression, analysis (e.g., for providing automatic generation of data visualizations), and others.

[0359] In embodiments, each data processing layer 608 has a set of application programming connectivity functions 642 for automating data exchange with each of the other data processing layers 624. These may include data integration functions such as extracting, transforming, loading, normalizing, compressing, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information exchanged between layers and / or applications 630, for example, transforming data from one format or protocol to another as needed to consume the output of another layer from one layer. In embodiments, the data processing layer 624 is 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 system layer 614. The value chain monitoring system layer 614 may integrate and / or cooperate with various data collection and management systems 640, which in some cases are conveniently referred to as data collection systems 640, to collect and organize data from or about value chain entities 652, as well as data from or about various data layers 624 or services or components. 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 system layer 614 to multiple different applications within the value chain management platform layer 604, such as facilitating monitoring of the worker's physiological, psychological, performance levels, attention, or other states, and others that facilitate operational efficiency and / or effectiveness. In embodiments, the monitoring system layer 614 facilitates alignment, such as time synchronization and normalization, of data collected with respect to one or more value chain network entities 652.For example, one or more video streams or other sensor data collected from or relating to a worker 718 or other entity within a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock so that the relative timing of the set of video or other data can be grasped by a system capable of processing the video, such as a machine learning system that manipulates the images in the video and the changes between images in different frames of the video. In such an example, the monitoring system layer 614 may further align the set of video, camera images, sensor data, etc., with other data such as streams of data from wearable devices, streams of data generated by value chain network systems (ships, lifts, vehicles, containers, cargo handling systems, packaging systems, delivery systems, drones / robots, etc.), and streams of data collected by mobile data collectors. Configuring the monitoring system layer 614 as a common platform accessed across many applications, or as a set of microservices, can dramatically reduce the number of interconnections required by owners or other operators within the value chain network to have a set of applications that monitor the growing set of IoT devices and other systems and devices under its control.

[0360] In some embodiments, the data processing layer 624 is 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 system layer 624, which in some cases is simply referred herein to as the data storage layer 624 or storage layer 624 for convenience. For example, various data collected about value chain entities 652, as well as data generated by other data processing layers 624, may be stored in the data storage layer 624 so that any of the services, applications, programs, etc. of the various data processing layers 624 can access a common data source (which may consist of a single logical data source distributed across heterogeneous physical and / or virtual storage locations). This can facilitate a dramatic reduction in the amount of data storage required to handle the enormous amounts of data generated by or about value chain network entities 652 as applications 630 and value chain network uses grow and proliferate. For example, a supply chain or inventory management application in value chain management platform layer 604, such as one for ordering replacement parts for machine or equipment items, may have access to a dataset of which parts have been replaced for a set of machines, similar to predictive maintenance applications used to predict whether a ship's components or port equipment are likely to require replacement parts. Similarly, predictions can also be used for the resupply of goods.

[0361] In an embodiment, the value chain network data object 1004 may be provided according to an object-oriented data model that defines classes, objects, attributes, parameters, and other characteristics of a set of data objects handled by the platform 604 (such as those related to the value chain network entity 652 and application 630).

[0362] In embodiments, the data storage system layer 624 may provide an extremely rich environment for collecting data that can be used to extract features or inputs for intelligent systems such as expert systems, analytical systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems disclosed through the documents incorporated herein by reference. As a result, each application 630 of platform 604 and each adaptive intelligent system of adaptive intelligent system layer 614 can benefit from the data collected or generated by or for each of the others. In embodiments, the data storage system layer 624 can facilitate the collection of data that can be used to extract features or inputs for intelligent systems such as development frameworks from artificial intelligence. For example, data collection may involve pulling and / or accommodating event logs (naturally stored or ad-hoc, as needed), performing periodic checks of onboard diagnostic data, or so. For example, feature pre-computation may be deployed using, for example, AWS LAMBDA, or various other cloud-based on-demand computing capabilities, such as pre-computation, multiplexed signals, etc. In many examples, even when aggregating hundreds or tens of data types from relatively heterogeneous entities, there exist pairs of similar types of value chain entities (2x, 3x, 4x, etc.) that can utilize one or more capabilities of the data processing layer 624 to deploy connectivity and services across value chain entities and applications used by those entities. In these examples, various pairs of similar types of value chain entities that use connectivity and services across value chain entities and applications may at least partially direct information from the connected data pairs to artificial intelligence services, including various neural networks and hybrid combinations thereof disclosed herein. In these examples, genetic programming techniques may be deployed to prune some of the input features in the information from the connected data pairs.In these examples, genetic programming techniques may also be deployed to add to and enhance input features in the information from the pairings. These genetic programming techniques may be shown to enhance the effectiveness of decisions established by artificial intelligence services. In these examples, information from pairings of connected data may be transferred to other layers on the platform to support or deploy robotic process automation, prediction, forecasting, and other resources, so that the shared data schema can be facilitated as a capability and resource for 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, but are not limited to these. There are asset and facility data 1030, status data 1140 (in particular, showing the state, status status, or other indicators relating to any of the value chain network entities 652, any of the applications 630 or their components or workflows, or any of the components or elements of the platform 604), 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 (including operational data, transaction data, workflow data, maintenance data, and data relating to any of the broad range of events, including process events, financial events, transaction events, output events, input events, state change events, operation events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, charging events, shipping events, warehouse events, goods movement, cross-border events, cargo movement, inspection events, supply events, and many other types of data relating to events occurring within the value chain network 668 or events relating to one or more applications 630), output events, input events, state change events, operation events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, charging events, shipping events, warehouse events, goods movement, cross-border events, cargo movement, inspection events, supply events, and many other events). Claims Data 664 (including claims relating to insurance claims such as business interruption insurance, product liability insurance, insurance for goods, facilities, or equipment, flood insurance, and insurance for contract-related risks; as well as claims data relating to product liability, general liability, workers' compensation, injury, and other liability claims; and many other types of claims data relating to contracts, such as claims for performance of supply contracts, product delivery requirements, warranty claims, compensation claims, delivery requirements, timing requirements, milestones, and key performance indicators).This includes accounting data 730 (data related to the completion of contract requirements, fulfillment of obligations, payment of customs duties, and other related data), risk management data 732 (data related to supplied items, amounts, prices, delivery, sources, routes, customs information, and many other related data), value chain network entities 652, and many other data types related to applications 630.

[0364] In some embodiments, the data processing layer 624 is configured in a topology that facilitates shared adaptive capabilities, which may be provided, managed, or mediated by one or more of a set of services, components, programs, systems, or capabilities of the adaptive intelligent systems layer 614, which in some cases refer to the adaptive intelligence layer 614 for convenience. The adaptive intelligent systems layer 614 may include a set of data processing, artificial intelligence, and computing systems 634, which are described in more detail elsewhere throughout this disclosure. Therefore, computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for single devices or peering networks), and available cloud infrastructure), data storage resources (various resources such as local storage on devices, storage resources within or on the environment of value chain entities or environments (such as on-device storage, storage on asset tags, local area network storage), network storage resources, cloud-based storage resources, database resources, etc.), network resources (including cellular network spectrum, wireless network resources, fixed network resources, etc.), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others), etc., may be optimized in a coordinated or shared manner on behalf of operators, enterprises, etc. for multiple applications, programs, workflows, etc. For example, the adaptive intelligence layer 614 may manage and provide available network resources so that low-latency resources are used for supply chain management applications (where rapid decisions may be important) and higher-latency resources are used for demand planning applications (among many other possibilities), for both supply chain management applications and demand planning applications.As will be described in more detail through the documents incorporated herein by reference, it is also possible to provide various adaptations instead of a variety of services and capabilities across various tiers, such as those based on application requirements, service quality, on-time delivery, service objectives, budget, cost, pricing, risk factors, operational objectives, efficiency objectives, optimization parameters, return on investment, profitability, uptime / downtime, worker utilization, and many others.

[0365] The Value Chain Management Platform Layer 604, which may be referred to as the Platform Layer 604 for convenience herein, may be integrated with and enable operators to manage a common application environment (e.g., a value chain network environment or various value chain network processes, workflows, activities, events, and applications 630 described throughout this disclosure, enabling the management of multiple aspects of an entity 652, whether the shared data is shared, pooled, one-person, multiple-person, or anonymized), for example, the common data storage of the Data Storage Layer 624, the common data collection or monitoring of the Monitoring System Layer 614, and / or the common adaptive intelligence of the Adaptive Intelligence Layer 614. Outputs from applications 630 of the Platform Layer 604 may be provided to other data handling layers 624. These may include, but are not limited to, state and status information of various objects, entities, processes, flows, etc., object information such as identity, attribute, and parameter information of various classes of objects of various data types, and event and change information of workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information. Output from each application 630 is stored in the data storage layer 624, distributed for processing by the data collection layer 614, and can be used by the adaptive intelligence layer 614.The cross-application nature of platform layer 604 therefore facilitates the convenient configuration of all the necessary infrastructure elements to add intelligence to any given application, such as supplying machine learning on results across applications, providing enrichment of automation for a given application through machine learning based on results from other applications or other elements of platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of platform 604. Examples may include systems, components, services and other capabilities that control, automate or optimize one or more performance characteristics of one or more value chain network entities 652; or anything that can generally improve any of the processes and application outputs and results 1040 pursued by the use of platform 604. In some examples, outputs and results 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction or similar involved in steps of a process intended to be automated. Details of the Data Storage Layer - Alternative Data Architecture

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

[0367] The adaptive intelligent systems layer 614 of platform 604 may include one or more protocol adapters 1110 to facilitate data storage, retrieval access, query management, loading, extraction, normalization, and / or transformation, enabling the use of various other data storage architectures 1002, such as enabling extraction from one form of database and loading into data systems using different protocols or data structures.

[0368] In embodiments, the value chain network-oriented data storage system layer 624 may include, but is not limited to, physical storage systems, virtual storage systems, local storage systems (e.g., part of local storage architecture 1190), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of network storage architecture 1174, such as the use of NVMe, storage-attached networks, and other network storage systems), and many others.

[0369] In an embodiment, the storage layer 624 may store data in one or more knowledge graphs (such as directed acyclic graphs, data maps, data hierarchies, data clusters including links and nodes, self-organizing maps, etc.) in the graph database architecture 1124. In an exemplary embodiment, the knowledge graph may be a prime 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 an accidental workflow, a ring graph may be used. A graph database (e.g., graph database architecture VPC608) may include a knowledge graph, or the knowledge graph may be an example of a graph database. In an exemplary embodiment, the knowledge graph may include ontologs and connections (e.g., relationships) between ontologs in the knowledge graph. In one example, the knowledge graph may be used to capture the explicit knowledge domain of a human expert, such that there may be an identification of an opportunity to design and build robotic process automation or other intelligence that can replicate this knowledge set. The platform may be used to recognize that the type of expert is using this de facto knowledge base (from the knowledge graph) combined with capabilities that can be replicated by artificial intelligence, which may differ depending on the type of expert involved. For example, artificial intelligence such as a convolutional neural network may be used for spatiotemporal aspects that may be used for diagnosing problems or packing them into boxes in a warehouse. On the other hand, the platform may use different types of knowledge graphs for self-organizing maps of experts whose main job is to segment customers into customer segmentation groups. In some examples, the knowledge graph may be constructed from various data such as job credentials, job lists, and analysis output artifacts. In embodiments, the data storage layer 624 may store data in digital threads, ledgers, etc., for example, to maintain serial or other records over time of entity 652, which includes any of the entities described herein.In an embodiment, the data storage layer 624 may use and enable asset tags 1178, which include data structures associated with assets and accessed and managed by means of access control, such as the use of access control, so that data storage and retrieval are optionally linked to local processes but optionally open to remote retrieval and storage options. In an embodiment, the storage layer 624 may include one or more blockchains 1180, such as those storing identity data, transaction data, historical interaction data, etc., with access control that may be role-based or based on credentials associated with value chain entities 652, services, or one or more applications 630. The data stored by the data storage system 624 may include accounting and other financial data 730, access data 734, asset and facility data 1030 (such as relating to 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 related to, generated by, or generated by any of the value chain entities and activities described herein and in documents incorporated by reference. Adaptive intelligent systems and monitoring layers

[0370] Referring to Figure 13, additional details, components, subsystems, and other elements of an optional embodiment of platform 604 are illustrated. In various arbitrary embodiments, the management platform 604 may include a set of applications 630, thereby enabling operators or owners of value chain network entities, or other users, to manage, monitor, control, analyze, or otherwise interact with one or more elements of value chain network entities 652, such as any of the elements pointed out in relation above and throughout this disclosure.

[0371] In embodiments, the adaptive intelligent system layer 614 can enhance one or more applications 630 in the application platform layer 604; improve the performance of one or more components or overall performance (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors); improve other capabilities within the adaptive intelligent system layer 614; improve performance (e.g., speed / latency, energy utilization, storage capacity, storage efficiency, reliability, security, etc.) of one or more components of the value chain network-oriented data storage system 624 or overall performance; optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or generally improve any of the processes and application outputs and results 1040 pursued by the use of the platform 604.

[0372] These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adapters 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a ...

Claims

1. It is an information technology system, It includes a cloud-based management platform with a microservices architecture, and this platform is A set of interfaces configured to access and configure the functions of the aforementioned platform, A set of network connectivity equipment configured to instruct a set of value chain network entities to connect to platform functions, A set of adaptive intelligent equipment configured to automate a set of platform functions related to at least one of the value chain network entities and platform functions, A set of data storage facilities configured to store data collected and handled by the platform, wherein the data relates to at least one value chain network entity and the functionality of the platform. Includes a set of monitoring equipment configured to monitor value chain network entities, The platform is a system configured to host a set of applications for directing a company to manage value chain network entities from the origin of the company's products to the point of use by the customer.

2. The system according to 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 according to claim 1, wherein the set of network connectivity equipment includes a 5G network system deployed in supply chain infrastructure equipment operated by an enterprise.

4. The system according to claim 1, wherein the set of network connectivity equipment includes an Internet of Things system deployed in supply chain infrastructure equipment operated by a company.

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

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

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

8. The system according to claim 1, wherein the set of adaptive intelligent equipment includes a robotic process automation system.

9. The adaptive intelligence equipment set includes a self-configured data collection system deployed in a supply chain infrastructure facility operated by an enterprise, according to claim 1.

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

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

12. The set of data storage equipment uses a distributed data architecture, according to claim 1.

13. The set of data storage equipment uses blockchain, according to claim 1.

14. The system according to claim 1, characterized in that the set of data storage equipment uses a distributed ledger.

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

16. The system according to claim 1, wherein the collection of monitoring equipment includes an Internet of Things monitoring system.

17. The system according to claim 1, wherein the set of monitoring equipment includes a sensor system deployed in infrastructure facilities operated by the company.

18. The system according to claim 1, wherein the set of applications includes at least two sets of applications selected from the sets of supply chain management applications, demand management applications, intelligent product applications, and enterprise resource management applications.

19. The system according to claim 1, characterized in that the set of applications includes an asset management application.

20. The system according to claim 1 is characterized in that the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, seaports, airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origins, destinations, storage locations, usage locations, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export controls, border control, drones, robots, autonomous vehicles, transport facilities, drones / robots / AVs, waterways, and port infrastructure facilities.

21. The system according to 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 according to claim 21, wherein the supply factors are factors selected from the group consisting of: availability of components, availability of materials, location of components, location of materials, price of components, price of materials, taxation, duty, postal service, duty, import restrictions, export restrictions, border control, trade restrictions, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, packaging capacity, vehicle utilization rate, ship utilization rate, container utilization rate, packaging capacity, 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 capacity, worker location, commodity price, fuel price, energy price, route availability, route distance, route cost, and route safety factor.

23. The system according to claim 21, wherein the demand factors are factors selected from the group consisting of product availability, product price, delivery timing, refilling need, replacement need, manufacturer recall, upgrade need, maintenance need, update need, repair need, consumables, taste, preference, inferred need, inferred desire, group demand, individual demand, family demand, business demand, workflow need, processing need, treatment need, improvement, diagnosis, compatibility with the system, compatibility with the product, compatibility with the style, compatibility with the brand, demographics, psychographics, geolocation, indoor location, destination, route, home location, visiting location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchase history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group activity, family member, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and estimated interest factors.

24. The system according to claim 21, wherein the supply chain infrastructure equipment is equipment selected from the group consisting of ships, container ships, boats, barges, seaports, cranes, containers, container handling facilities, shipyards, offshore docks, warehouses, distribution, fulfillment, refueling, nuclear fuel refueling, waste removal food supply, beverage supply, drones, robots, autonomous vehicles, aircraft, automobiles, trucks, trains, lifts, forklifts, transport equipment, conveyors, loading docks, waterways, bridges, tunnels, airports, garages, railway stations, weighing stations, inspection facilities, roads, railways, highways, customs, and border control facilities.

25. The set of applications includes supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, deployment, promotion, blockchain, smart contracts, infrastructure management, facility management, analytics, finance, trading, tax, regulation, identity management, commerce, e-commerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote operation, automation, self-configuration, self-healing, self-organization, logistics, and reverse logistics. The system according to claim 1, comprising a set selected from the group consisting of waste reduction, augmented reality, virtual reality, mixed reality, customer profiles, corporate profiles, worker profiles, workforce profiles, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing, delivery, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit deployment, kit support, kit updates, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, kit configuration, product configuration, product updates, product updates, product update kit deployment, kit support, kit updates, kit maintenance, kit modification, kit management, fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, fleet management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, and corporate resource planning applications.

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