SYSTEM, METHOD, KITS AND APPARATUS FOR MANAGING A VALUE CHAIN ​​NETWORK IN A SYSTEM OF SYSTEMS - Patent application

Abstract

The methods and systems described herein can provide a control tower and / or mini-control tower and / or digital twin for managing a value chain network (VCN). Information related to a set of value chain network entities in the VCN may be received from various sources. That information may be provided to a set of artificial intelligence (AI)-based learning models. The AI-based learning models can use that information in conjunction with the control tower and / or mini-control tower and / or digital twin to determine potential risks in the VCN, determine potential actions to take in the VCN, configure intelligent procurement in the VCN, configure predictive sourcing in the VCN, automate VCN entities, direct smart machines in the VCN, and configure robotic process automation in the VCN.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 381,545, filed October 28, 2022, the entire contents of which are incorporated by reference. [Background technology]

[0002] Historically, many of the various categories of goods purchased and used by residential consumers, businesses, and other customers were primarily supplied in a relatively linear manner, with manufacturers and other suppliers of finished goods, components, and other items delivering the goods to shipping companies, freight forwarders, and others, who then delivered the goods to warehouses for temporary storage, to retailers where the customers purchased them, or directly to the customers' locations. Manufacturers and retailers used various sales and marketing activities, such as product design, shelf and advertising placement, and pricing, to stimulate and meet customer demand. Summary of the Invention

[0003] In one exemplary embodiment, a method performed by one or more computing devices may include, but is not limited to, configuring a set of secondary computing devices of a value chain network entity for communication with a primary computing device of an enterprise operator, and the primary computing device may manage the set of secondary computing devices. At least one member of the set of secondary computing devices may receive a set of primary commands from the primary computing device, each of which may be at least one of a task or a request. At least a portion of the one or more computing devices capable of executing the primary commands may be assigned as a set of one or more computing devices managed by the set of secondary computing devices. The set of secondary computing devices may be configured to execute one or more secondary commands based at least in part on the primary commands. The set of secondary computing devices may execute the primary commands based at least in part on the set of one or more computing devices executing the one or more secondary commands. A generated system output may be sent to the primary computing device responsive to the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands.

[0004] One or more of the following example features may be included: Configuring the set of one or more computing devices to execute one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing output of the primary computing device as an input for generating one or more control parameters. Configuring the set of one or more computing devices to execute one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least a portion of the one or more computing devices capable of executing the primary commands as the set of one or more computing devices managed by the secondary computing device may include performing a registration process between the set of one or more computing devices and the secondary computing device. Performing the registration process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The secondary computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format conversion between each computing device in the set of one or more computing devices and the secondary computing device. The primary computing device may be bypassed to receive external data at the secondary computing device if the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in the management stack. Intelligent decision-making regarding a strategy for primary command may be performed based at least in part on the configured intelligence service (CIS).The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

[0005] In another exemplary implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include, but are not limited to, configuring a set of secondary computing devices of a value chain network entity for communication with a primary computing device of an enterprise operator, where the primary computing device may manage the set of secondary computing devices. At least one member of the set of secondary computing devices may receive a set of primary commands from the primary computing device, where each of the set of commands may be at least one of a task or a request. At least a portion of the one or more computing devices capable of executing the primary commands may be assigned as a set of one or more computing devices managed by the set of secondary computing devices. The set of secondary computing devices may configure the set of one or more computing devices to execute one or more secondary commands based at least in part on the primary command. The set of secondary computing devices may execute the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands. A generated system output may be sent to the primary computing device responsive to the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands.

[0006] One or more of the following example features may be included: Configuring the set of one or more computing devices to execute one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing output of the primary computing device as input for generating one or more control parameters. Configuring the set of one or more computing devices to execute one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least a portion of the one or more computing devices capable of executing the primary commands as the set of one or more computing devices managed by the secondary computing device may include performing a registration process between the set of one or more computing devices and the secondary computing device. Performing the registration process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The secondary computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format conversion between each computing device in the set of one or more computing devices and the secondary computing device. The primary computing device may be bypassed to receive external data at the secondary computing device if the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in the management stack. Intelligent decision-making regarding a strategy for primary command may be performed based at least in part on the configured intelligence service (CIS).The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

[0007] In another exemplary implementation, a computer program product may reside on a computer-readable storage medium having stored thereon a plurality of instructions that, when executed across one or more processors, cause at least a portion of the one or more processors to perform operations that may include, but are not limited to, configuring a set of secondary computing devices of a value chain network entity for communication with a primary computing device of an enterprise operator, wherein the primary computing device is capable of managing the set of secondary computing devices. At least one member of the set of secondary computing devices may receive a set of primary commands from the primary computing device, each of the set of commands may be at least one of a task or a request. At least a portion of the one or more computing devices capable of executing the primary commands may be assigned as a set of one or more computing devices managed by the set of secondary computing devices. The set of secondary computing devices may configure the set of one or more computing devices to execute one or more secondary commands based at least in part on the primary commands. The set of secondary computing devices may execute the primary commands based at least in part on the set of one or more computing devices executing one or more secondary commands. The generated system output may be transmitted to a primary computing device responsive to the primary command based at least in part on the set of one or more computing devices executing one or more secondary commands.

[0008] One or more of the following example features may be included: Configuring the set of one or more computing devices to execute one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing output of the primary computing device as input for generating one or more control parameters. Configuring the set of one or more computing devices to execute one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least a portion of the one or more computing devices capable of executing the primary commands as the set of one or more computing devices managed by the secondary computing device may include performing a registration process between the set of one or more computing devices and the secondary computing device. Performing the registration process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The secondary computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format conversion between each computing device in the set of one or more computing devices and the secondary computing device. The primary computing device may be bypassed to receive external data at the secondary computing device if the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in the management stack. Intelligent decision-making regarding a strategy for primary command may be performed based at least in part on the configured intelligence service (CIS).The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage point, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

[0009] In one example implementation, a method performed by one or more computing devices may include, but is not limited to, configuring a set of sub-level computing devices for communication with a primary computing device, where the primary computing device may manage the set of sub-level computing devices to orchestrate performance of a set of entities in a value chain network. The set of sub-level computing devices may receive a primary command from the primary computing device, where the command is one of a task or request related to the value chain network. At least a portion of the one or more computing devices capable of executing the primary command may be assigned as the set of one or more computing devices managed by the sub-level computing device, where the sub-level computing device may be a computing device that manages or executes performance of a particular entity or relationship in the value chain network. The sub-level computing device may configure the set of one or more computing devices to execute one or more secondary commands based at least in part on the primary command. The sub-level computing device may execute the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands.

[0010] One or more of the following example features may be included: Configuring the set of one or more computing devices to satisfy the one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing output from multiple sources as input to generate one or more control parameters. Configuring the set of one or more computing devices to execute the one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least a portion of the one or more computing devices capable of executing the primary commands as the set of one or more computing devices managed by the sub-level computing device may include performing a registration process between the set of one or more computing devices and the sub-level computing device. Performing the registration process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The sub-level computing device may obtain one or more application programming interfaces (APIs) that enable communication and data format conversion between each computing device of the set of one or more computing devices and the sub-level computing device. The primary computing device may be bypassed to receive external data at a sub-level computing device if the external data is unused by the primary computing device. Generating at least one set of CSSs may include generating at least one CSS for each interface layer in the management stack. Intelligent decision-making may be performed regarding a strategy of the primary command based at least in part on the configured intelligence service (CIS).The set of entities in the value chain network may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The generated system output may be sent to a primary computing device responsive to the primary command based at least in part on a set of one or more computing devices executing the one or more secondary commands.

[0011] In another exemplary implementation, a computing system may include one or more processors and one or more memories configured to perform operations, which may include, but are not limited to, configuring a set of sub-level computing devices for communication with a primary computing device, where the primary computing device may manage the set of sub-level computing devices to coordinate the performance of a set of entities in a value chain network. The set of sub-level computing devices may receive a primary command from the primary computing device, where the command is one of a task or request related to the value chain network. At least a portion of the one or more computing devices capable of executing the primary command may be assigned as a set of one or more computing devices managed by the sub-level computing device, where the sub-level computing device may be a computing device that manages or executes the performance of a particular entity or relationship in the value chain network. The sub-level computing device may configure the set of one or more computing devices to execute one or more secondary commands based at least in part on the primary command. The sub-level computing device may execute the primary command based at least in part on the set of one or more computing devices executing one or more secondary commands.

[0012] One or more of the following example features may be included: Configuring the set of one or more computing devices to execute one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing output from multiple sources as input to generate one or more control parameters. Configuring the set of one or more computing devices to execute one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least a portion of the one or more computing devices capable of executing the primary commands as the set of one or more computing devices managed by the sub-level computing device may include performing a registration process between the set of one or more computing devices and the sub-level computing device. Performing the registration process may include performing an inventory of communication protocols and data formats used by the set of one or more computing devices. The sub-level computing device may obtain one or more application programming interfaces (APIs) that enable communication and data format conversion between each computing device of the set of one or more computing devices and the sub-level computing device. The primary computing device may be bypassed to receive external data at a sub-level computing device if the external data is unused by the primary computing device. Generating at least one set of CSSs may include generating at least one CSS for each interface layer in the management stack. Intelligent decision-making may be performed regarding a strategy of the primary command based at least in part on the configured intelligence service (CIS).The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, an agent, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The generated system output may be sent to a primary computing device responsive to the primary command based at least in part on a set of one or more computing devices executing the one or more secondary commands.

[0013] In another exemplary embodiment, a computer program product may reside on a computer-readable storage medium having stored thereon a plurality of instructions that, when executed across one or more processors, cause at least a portion of the one or more processors to perform operations that may include, but are not limited to, configuring a set of sub-level computing devices for communication with a primary computing device, where the primary computing device may manage the set of sub-level computing devices to coordinate performance of a set of value chain network entities. The set of sub-level computing devices may receive a primary command from the primary computing device, where the command is one of a task or request related to the value chain network. At least a portion of one or more computing devices that can execute the primary command may be assigned as a set of one or more computing devices managed by the sub-level computing device, where the sub-level computing device may be a computing device that manages or executes performance of a particular entity or relationship in the value chain network. The sub-level computing device may configure the set of one or more computing devices to execute one or more secondary commands based at least in part on the primary command. The sub-level computing device may execute the primary command based at least in part on the set of one or more computing devices executing one or more secondary commands.

[0014] One or more of the following example features may be included: Configuring the set of one or more computing devices to execute one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing output from multiple sources as input to generate one or more control parameters. Configuring the set of one or more computing devices to execute one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least a portion of the one or more computing devices capable of executing the primary commands as the set of one or more computing devices managed by the sub-level computing device may include performing a registration process between the set of one or more computing devices and the sub-level computing device. Performing the registration process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The sub-level computing device may obtain one or more application programming interfaces (APIs) that enable communication and data format conversion between each computing device of the set of one or more computing devices and the sub-level computing device. The primary computing device may be bypassed to receive external data at a sub-level computing device if the external data is unused by the primary computing device. Generating at least one set of CSSs may include generating at least one CSS for each interface layer in the management stack. Intelligent decision-making may be performed regarding a strategy of the primary command based at least in part on the configured intelligence service (CIS).The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage point, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The generated system output may be transmitted to a primary computing device responsive to the primary command based at least in part on the set of one or more computing devices executing one or more secondary commands.

[0015] In one exemplary implementation, a method performed by one or more computing devices may include, but is not limited to, receiving, by the computing devices, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, and at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. Potential risks in the value chain are identified based at least in part on the output of the AI-based learning classification. Actions may be taken to mitigate the potential risks in the value chain network.

[0016] One or more of the following example features may be included: Performing actions to mitigate potential risks in the value chain network may include flagging potential risks in the value chain network. Performing actions to mitigate potential risks in the value chain network may include responding to potential risks in the value chain network. Data related to warehousing, inventory management, order management, and analytics may be integrated to optimize omni-channel fulfillment. Performing actions to mitigate potential risks in the value chain network may include resolving out-of-stock situations. Performing actions to mitigate potential risks in the value chain network may include predicting timing of order placement based at least in part on upstream data. Performing actions to mitigate potential risks in the value chain network may include planning supply. Performing actions to mitigate potential risks in the value chain network may include optimizing inventory mix. External data may be received and a strategy to reduce transportation costs may be determined based at least in part on the external data. The platform may be provided with multiple AI-based learning models for download.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage point, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of value chain network entity outcomes, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0017] In another exemplary implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include, but are not limited to, receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, and at least one member of the set of AI-based learning models may be trained with a training dataset of operating data of the set of value chain network entities to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. Potential risks in the value chain are identified based, at least in part, on the output of the AI-based learning classification. Actions may be taken to mitigate potential risks in the value chain network.

[0018] One or more of the following example features may be included: Performing actions to mitigate potential risks in the value chain network may include flagging potential risks in the value chain network. Performing actions to mitigate potential risks in the value chain network may include responding to potential risks in the value chain network. Data related to warehousing, inventory management, order management, and analytics may be integrated to optimize omni-channel fulfillment. Performing actions to mitigate potential risks in the value chain network may include resolving out-of-stock situations. Performing actions to mitigate potential risks in the value chain network may include predicting timing of order placement based at least in part on upstream data. Performing actions to mitigate potential risks in the value chain network may include planning supply. Performing actions to mitigate potential risks in the value chain network may include optimizing inventory mix. External data may be received and a strategy to reduce transportation costs may be determined based at least in part on the external data. The platform may be provided with multiple AI-based learning models for download.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of value chain network entity outcomes, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0019] As another example embodiment, a computer program product may be present on a computer-readable storage medium having a plurality of instructions stored thereon, the instructions executing on one or more processors to cause at least a portion of the one or more processors to perform the following operations: receive, by a computing device, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. Potential risks in the value chain may be identified based, at least in part, on an output of the AI-based learning classification. Actions may be taken to mitigate potential risks in the value chain network.

[0020] One or more of the following example features may be included: Performing actions to mitigate potential risks in the value chain network may include flagging potential risks in the value chain network. Performing actions to mitigate potential risks in the value chain network may include responding to potential risks in the value chain network. Data related to warehousing, inventory management, order management, and analytics may be integrated to optimize omni-channel fulfillment. Performing actions to mitigate potential risks in the value chain network may include resolving out-of-stock situations. Performing actions to mitigate potential risks in the value chain network may include predicting timing of order placement based at least in part on upstream data. Performing actions to mitigate potential risks in the value chain network may include planning supply. Performing actions to mitigate potential risks in the value chain network may include optimizing inventory mix. External data may be received and a strategy to reduce transportation costs may be determined based at least in part on the external data. The platform may be provided with multiple AI-based learning models for download.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of value chain network entity outcomes, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0021] In one exemplary implementation, a method performed by one or more computing devices may include, but is not limited to, receiving, by the computing devices, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, and at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A procurement action to be performed in the value chain network may be determined based at least in part on an output of the set of AI-based learning models. The procurement action may be performed to facilitate an improvement of at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one entity of the set of value chain network entities.

[0022] One or more of the following example features may be included: Alerts describing the procurement action taken may be provided. Information may include historical behavior over time, historical data, and current data. Real-time information regarding supplier performance may be provided. Supplier and procurement team compliance may be monitored. Purchase orders related to the procurement action may be automatically generated. Invoice processing related to the procurement action may be automatically handled. Data related to warehouse management, inventory management, order management, and analytics may be integrated to optimize omnichannel fulfillment. Executing the procurement action may include resolving out-of-stock situations. Executing the procurement action may include predicting when to place an order based at least in part on upstream data. The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of value chain network entity outcomes, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.The training data set for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy, which may include at least one of an operating state, a fault state, an operating flow, or a behavior. The procurement action may be performed by the value chain network digital twin.

[0023] In another exemplary embodiment, a computing system may include one or more processors and one or more memories configured to perform operations that may include, but are not limited to, receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, and at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A procurement action to be performed in the value chain network may be determined based at least in part on an output of the set of AI-based learning models. The procurement action may be performed to facilitate an improvement of at least one of an operating state, a fault state, an operating flow, or a behavior of at least one entity of the set of value chain network entities.

[0024] One or more of the following example features may be included: Alerts may be provided describing the procurement action taken. Information may include historical behavior over time, historical data, and current data. Real-time information regarding supplier performance may be provided. Supplier and procurement team compliance may be monitored. Purchase orders related to the procurement action may be automatically generated. Invoice processing related to the procurement action may be automatically handled. Data related to warehouse management, inventory management, order management, and analytics may be integrated to optimize omnichannel fulfillment. Executing the procurement action may include resolving out-of-stock situations. Executing the procurement action may include predicting when to place an order based at least in part on upstream data. The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, a business, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.The training data set for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy, which may include at least one of an operating state, a fault state, an operating flow, or a behavior. The procurement action may be performed by the value chain network digital twin.

[0025] As another example embodiment, a computer program product may reside on a computer-readable storage medium having a plurality of instructions stored thereon that, when executed by one or more processors, cause at least some of the processors to perform operations including, but not limited to, receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A procurement action to be performed in the value chain network may be determined based at least in part on an output of the set of AI-based learning models. The procurement action may be performed to facilitate an improvement in at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one entity of the set of value chain network entities.

[0026] One or more of the following example features may be included: Alerts may be provided describing the procurement action taken. Information may include past behavior over time, historical data, and current data. Real-time information regarding supplier performance may be provided. Supplier and procurement team compliance may be monitored. Purchase orders related to the procurement action may be automatically generated. Invoice processing related to the procurement action may be automatically handled. Data related to warehouse management, inventory management, order management, and analytics may be integrated to optimize omnichannel fulfillment. Executing the procurement action may include resolving out-of-stock situations. Executing the procurement action may include predicting when an order should be placed based at least in part on upstream data. The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.The training data set for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy, which may include at least one of an operating state, a fault state, an operating flow, or a behavior. The procurement action may be performed by the value chain network digital twin.

[0027] In one exemplary implementation, a method performed by one or more computing devices may include, but is not limited to, receiving, by the computing devices, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a first set of artificial intelligence (AI)-based learning models, where at least one member of the first set of AI-based learning models may be trained on a training dataset of the set of value chain network data to generate a prediction of future demand for items in the value chain network. The information may be provided to a second set of AI-based learning models, where at least one member of the second set of AI-based learning models may be trained on a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A potential risk in the value chain network associated with at least one value chain network entity may be identified based at least in part on the output of the AI-based learning model, and a recommendation for mitigating the potential risk in the value chain network may be output or an action may be automatically taken to mitigate the potential risk in the value chain network.

[0028] One or more of the following example features may be included: Taking action to mitigate a potential risk in a value chain network associated with the item may include flagging a potential risk in a value chain network associated with the item. Taking action to mitigate a potential risk in a value chain network associated with the item may include responding to a potential risk in a value chain network associated with the item. Data related to warehouse management, inventory management, order management, and analytics may be integrated to optimize omni-channel fulfillment. Taking action to mitigate a potential risk in a value chain network associated with the item may include resolving an out-of-stock situation. Taking action to mitigate a potential risk in a value chain network associated with the item may include predicting when to place an order based at least in part on upstream data. An alert may be provided describing an action that has been taken to mitigate a potential risk in a value chain network associated with the item. The information may include past behavior over time, historical data, and current data. The potential risk may be a potential disruption in a value chain network associated with the item. A visualization related to at least one of inbound shipments or outbound shipments associated with the item may be rendered.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0029] In another exemplary embodiment, a computing system may include one or more processors and one or more memories configured to perform operations that may include, but are not limited to, receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a first set of artificial intelligence (AI)-based learning models, where at least one member of the first set of AI-based learning models may be trained with a training dataset of the set of value chain network data to generate a prediction of future demand for items in the value chain network. The information may be provided to a second set of AI-based learning models, where at least one member of the second set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A potential risk in the value chain network associated with at least one value chain network entity may be identified based, at least in part, on output of the AI-based learning model, and a recommendation for mitigating the potential risk in the value chain network is output or an action is automatically taken to mitigate the potential risk in the value chain network.

[0030] One or more of the following example features may be included: Taking action to mitigate a potential risk in a value chain network associated with the item may include flagging a potential risk in a value chain network associated with the item. Taking action to mitigate a potential risk in a value chain network associated with the item may include responding to a potential risk in a value chain network associated with the item. Data related to warehouse management, inventory management, order management, and analytics may be integrated to optimize omni-channel fulfillment. Taking action to mitigate a potential risk in a value chain network associated with the item may include resolving an out-of-stock situation. Taking action to mitigate a potential risk in a value chain network associated with the item may include predicting when to place an order based at least in part on upstream data. An alert may be provided describing an action that has been taken to mitigate a potential risk in a value chain network associated with the item. The information may include past behavior over time, historical data, and current data. The potential risk may be a potential disruption in a value chain network associated with the item. A visualization related to at least one of an inbound shipment or an outbound shipment associated with the item may be rendered.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0031] As an example of another embodiment, a computer program product may be located on a computer-readable storage medium having a plurality of instructions stored thereon, which instructions, when executed on one or more processors, cause at least some of the processors to perform operations including, but not limited to, receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a first set of artificial intelligence (AI) based learning models, and at least one member of the first set of AI-based learning models may be trained with a training dataset of the value chain network data to generate a prediction of future demand for items in the value chain network. The information may be provided to a second set of AI-based learning models, and at least one member of the second set of AI-based learning models may be trained on a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A potential risk in the value chain network associated with the at least one value chain network entity may be identified based, at least in part, on the output of the AI-based learning models. A recommendation for mitigating the potential risk in the value chain network may be output, or an action may be automatically taken to mitigate the potential risk in the value chain network.

[0032] One or more of the following example features may be included: Taking action to mitigate a potential risk in a value chain network associated with the item may include flagging a potential risk in a value chain network associated with the item. Taking action to mitigate a potential risk in a value chain network associated with the item may include responding to a potential risk in a value chain network associated with the item. Data related to warehouse management, inventory management, order management, and analytics may be integrated to optimize omni-channel fulfillment. Taking action to mitigate a potential risk in a value chain network associated with the item may include resolving an out-of-stock situation. Taking action to mitigate a potential risk in a value chain network associated with the item may include predicting when to place an order based, at least in part, on upstream data. An alert may be provided describing an action that has been taken to mitigate a potential risk in a value chain network associated with the item. The information may include past behavior over time, historical data, and current data. The potential risk may be a potential risk in a value chain network associated with the item. A visualization related to at least one of an inbound shipment or an outbound shipment associated with the item may be rendered.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, a business, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0033] In one exemplary implementation, a method performed by one or more computing devices may include, but is not limited to, receiving, by the computing devices, information related to a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, and at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. Tasks to be completed for the value chain network may be determined, at least in part, based on output of the set of AI-based learning models. The tasks may be performed to facilitate improvement of the value chain network.

[0034] One or more of the following example features may be included: Performing the task may include predicting future demand for the item in the value chain network. The information may include one or more of historical sales data and market trends related to the item. Performing the task may include detecting defects and quality issues for the item in the value chain network. The information may include one or more of video and photographs related to the item. Performing the task may include predicting when an item in the value chain network will fail. The information may include data from one or more sensors related to the item. Performing the task may include identifying a value chain process eligible for optimization based at least in part on analyzing information related to the value chain network, and optimizing the value chain process. The value chain process may include one or more of transportation routing, inventory management, or supplier selection. Performing the task may include analyzing user data of the user from at least one source, and identifying one or more attributes of the user based at least in part on the user data.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage point, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0035] In another exemplary embodiment, a computing system may include one or more processors and one or more memories configured to perform operations that may include, but are not limited to, receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, and at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. Tasks to be completed for the value chain network may be determined based at least in part on the output of the set of AI-based learning models. Tasks that facilitate improvement of the value chain network may be performed.

[0036] One or more of the following example features may be included: Performing the task may include predicting future demand for an item in the value chain network. The information may include one or more of historical sales data and market trends related to the item. Performing the task may include detecting defects and quality issues for an item in the value chain network. The information may include one or more of video and photographs related to the item. Performing the task may include predicting when an item in the value chain network will fail. The information may include data from one or more sensors related to the item. Performing the task may include identifying a value chain process capable of optimization based at least in part on analyzing information related to the value chain network, and optimizing the value chain process. The value chain process may include one or more of transportation routing, inventory management, or supplier selection. Performing the task may include analyzing user data of the user from at least one source and identifying one or more attributes of the user based at least in part on the user data. The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage point, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy, which may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0037] In another exemplary embodiment, a computer program product may be present on a computer-readable storage medium having stored thereon a plurality of instructions that, when executed on one or more processors, can cause at least some of the processors to perform operations including, but not limited to, receiving, by a computing device, information related to a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, and at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. Tasks to be completed for the value chain network may be determined based at least in part on an output of the set of AI-based learning models. Tasks that facilitate improvement of the value chain network may be performed.

[0038] One or more of the following example features may be included: Performing the task may include predicting future demand for an item in the value chain network. The information may include one or more of historical sales data and market trends related to the item. Performing the task may include detecting defects and quality issues for an item in the value chain network. The information may include one or more of video and photographs related to the item. Performing the task may include predicting when an item in the value chain network will fail. The information may include data from one or more sensors related to the item. Performing the task may include identifying a value chain process capable of optimization based at least in part on analyzing information related to the value chain network, and optimizing the value chain process. The value chain process may include one or more of transportation routing, inventory management, or supplier selection. Performing the task may include analyzing user data of the user from at least one source and identifying one or more attributes of the user based at least in part on the user data. The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage point, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0039] In one exemplary implementation, a method performed by one or more computing devices may include, but is not limited to, receiving, by the computing devices, information related to a value chain network, the information generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, at least one member of the set of AI-based learning models may be trained on a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of the value chain network, and upon receiving the classification, the at least one member of the set of AI-based learning models may be trained on the training dataset to determine a task to complete for the value chain network. A set of computer code instructions for performing the task may be provided to a machine to facilitate operational improvement of the value chain network.

[0040] One or more of the following example features may be included: Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to move items throughout a value chain network. The machine may include one or more of a robot, an automated guided vehicle (AGV), a smart container, a 3D printer, or a drone. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to detect defects and quality issues of items in the value chain network. The information related to the value chain network may include one or more of videos and photographs related to the items in the value chain network to detect defects and quality issues of the items. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to predict when an item in the value chain network will fail. The information related to the value chain network may include data from one or more sensors related to the items in the value chain network to predict when an item in the value chain network will fail. Providing a machine with a set of computer code instructions to perform a task may include identifying value chain processes that can be optimized based at least in part on analyzing information related to the value chain network, and instructing the machine to optimize the value chain processes. The value chain process may include one or more of transportation routing, inventory management, supplier selection, or warehouse management. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to transport items between locations.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0041] In another exemplary implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include, but are not limited to, receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, where at least one member of the set of AI-based learning models may be trained on a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of the value chain network, where, upon receiving the classification, the at least one member of the set of AI-based learning models is trained on the training dataset to determine a task to complete for the value chain network. A set of computer code instructions for performing the tasks may be provided to the machine to facilitate operational improvements of the value chain network.

[0042] One or more of the following example features may be included: Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to move items throughout a value chain network. The machine may include one or more of a robot, an automated guided vehicle (AGV), a smart container, a 3D printer, or a drone. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to detect defects and quality issues of items in the value chain network. The information related to the value chain network may include one or more of videos and photographs associated with the items to detect defects and quality issues of the items in the value chain network. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to predict when an item in the value chain network will fail. The information related to the value chain network may include data from one or more sensors associated with the items to predict when an item in the value chain network will fail. Providing a machine with a set of computer code instructions to perform a task may include identifying value chain processes that can be optimized based at least in part on analyzing information related to the value chain network, and instructing the machine to optimize the value chain processes. The value chain process may include one or more of transportation routing, inventory management, supplier selection, or warehouse management. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to transport items between locations.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage point, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0043] As an example of another embodiment, a computer program product may be resident on a computer-readable storage medium having a plurality of instructions stored thereon that can execute on one or more processors to cause at least some of the processors to perform operations including, but not limited to, receiving, by a computing device, information related to a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, at least one member of the set of AI-based learning models trained on a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of the value chain network, the at least one member of the set of AI-based learning models being trained on the training dataset upon receiving the classification to determine a task to complete for the value chain network. A set of computer code instructions for performing tasks may be provided to a machine to facilitate operational improvements of the value chain network.

[0044] One or more of the following example features may be included: Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to move items throughout a value chain network. The machine may include one or more of a robot, an automated guided vehicle (AGV), a smart container, a 3D printer, or a drone. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to detect defects and quality issues of items in the value chain network. The information related to the value chain network may include one or more of videos and photographs associated with the items to detect defects and quality issues of the items in the value chain network. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to predict when an item in the value chain network will fail. The information related to the value chain network may include data from one or more sensors associated with the items to predict when an item in the value chain network will fail. Providing a machine with a set of computer code instructions to perform a task may include identifying value chain processes that can be optimized based at least in part on analyzing information related to the value chain network, and instructing the machine to optimize the value chain processes. The value chain process may include one or more of transportation routing, inventory management, supplier selection, or warehouse management. Providing a machine with a set of computer code instructions to perform a task may include instructing the machine to transport items between locations.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0045] In one exemplary implementation, a method performed by one or more computing devices may include, but is not limited to, receiving, by the computing devices, information related to a value chain network, the information generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, where at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of the value chain network, where, upon receiving the classification, the at least one member of the set of AI-based learning models is trained with the training dataset to determine a task to be completed for the value chain network. The robotic process automation system may be configured to perform tasks to facilitate improvement of the value chain network.

[0046] One or more of the following example features may be included: A task may include automatically processing one or more orders for items in a value chain network based at least in part on the information; Automatically processing one or more orders for items in a value chain network may include extracting order data for the one or more orders from one or more sources and automatically entering the order data extracted from the one or more sources into an order management system; A task may include monitoring inventory levels for items in a value chain network and automatically generating one or more purchase orders for the items when inventory levels for the items in the value chain network fall below a threshold; Shipments of the items may be tracked in real time, and inventory levels for the items in the value chain network may be automatically updated based at least in part on tracking the shipments of the items in real time; A task may include extracting invoice data for one or more invoices from one or more sources and automatically entering the invoice data extracted from the one or more sources into an accounting system; A user interface may be rendered that allows a user to visually design an automation workflow; A user interface may be rendered that allows a user to manage an automation process. A user interface may be rendered that allows a user to create multiple AI-based learning models and select multiple application programming interfaces for integrating the multiple AI models into one or more automation workflows. The user interface may be rendered to allow a user to track automation performance and generate custom dashboards based at least in part on automation performance.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0047] In another exemplary implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include, but are not limited to, receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, where at least one member of the set of AI-based learning models may be trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of the value chain network, where, upon receiving the classification, the at least one member of the set of AI-based learning models is trained with the training dataset to determine a task to be completed for the value chain network. A robotic process automation system may be configured to perform tasks to facilitate improvement of the value chain network.

[0048] One or more of the following example features may be included: A task may include automatically processing one or more orders for items in a value chain network based at least in part on the information; Automatically processing one or more orders for items in a value chain network may include extracting order data for the one or more orders from one or more sources and automatically entering the order data extracted from the one or more sources into an order management system; A task may include monitoring inventory levels for items in a value chain network and automatically generating one or more purchase orders for the items when inventory levels for the items in the value chain network fall below a threshold; Shipments of the items may be tracked in real time, and inventory levels for the items in the value chain network may be automatically updated based at least in part on tracking the shipments of the items in real time; A task may include extracting invoice data for one or more invoices from one or more sources and automatically entering the invoice data extracted from the one or more sources into an accounting system; A user interface may be rendered that allows a user to visually design an automation workflow; A user interface may be rendered that allows a user to manage an automation process. A user interface may be rendered that allows a user to create multiple AI-based learning models and select multiple application programming interfaces for integrating the multiple AI models into one or more automation workflows. The user interface may be rendered to allow a user to track automation performance and generate custom dashboards based at least in part on automation performance.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0049] As an example of another embodiment, a computer program product may be disposed on a computer-readable storage medium having a plurality of instructions stored thereon that can execute on one or more processors to cause at least some of the processors to perform operations including, but not limited to, receiving, by a computing device, information related to a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities. The information may be provided to a set of artificial intelligence (AI)-based learning models, at least one member of the set of AI-based learning models may be trained on a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault condition, an operating flow, or a behavior of the value chain network, wherein, upon receiving the classification, the at least one member of the set of AI-based learning models is trained on the training dataset to determine a task to be completed for the value chain network. A robotic process automation system may be configured to perform tasks to facilitate improvement of a value chain network.

[0050] One or more of the following example features may be included: A task may include automatically processing one or more orders for items in a value chain network based at least in part on the information; Automatically processing one or more orders for items in a value chain network may include extracting order data for the one or more orders from one or more sources and automatically entering the order data extracted from the one or more sources into an order management system; A task may include monitoring inventory levels for items in a value chain network and automatically generating one or more purchase orders for the items when inventory levels for the items in the value chain network fall below a threshold; Shipments of the items may be tracked in real time, and inventory levels for the items in the value chain network may be automatically updated based at least in part on tracking the shipments of the items in real time; A task may include extracting invoice data for one or more invoices from one or more sources and automatically entering the invoice data extracted from the one or more sources into an accounting system; A user interface may be rendered that allows a user to visually design an automation workflow; A user interface may be rendered that allows a user to manage an automation process. A user interface may be rendered that allows a user to create multiple AI-based learning models and select multiple application programming interfaces for integrating the multiple AI models into one or more automation workflows. The user interface may be rendered to allow a user to track automation performance and generate custom dashboards based at least in part on automation performance.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0051] In one implementation, a method performed by one or more computing devices includes, but is not limited to, receiving information related to a value chain network via a value chain network digital twin, where the information may include a virtual representation of relationships between physical data items of the value chain network, and further, the information may be dynamic, real-time, and time-series. The information may be provided to a set of artificial intelligence (AI)-based learning models, where at least one member of the set of AI-based learning models may be trained with a training dataset of the set of operating data of the value chain network entities to classify at least one of an operating state, a fault state, an operating flow, or a behavior of the value chain network, and where, upon receiving the classification, the at least one member of the set of AI-based learning models may be trained with the training dataset to determine a task to be completed for the value chain network. At least one of instructions for performing the task on the value chain network digital twin and a recommendation for performing the task on the value chain network digital twin may be provided.

[0052] One or more of the following example features may be included: One of a virtual reality (VR) environment, an augmented reality (AR) environment, a mixed reality (MR) environment, or a faded reality (DR) environment may be rendered for a user to interact with a sensor-based virtual representation of multiple relationships between physical data items of the value chain network. The information may include real-time data regarding one of inbound prepaid shipments from suppliers linked to orders or inventory entering a network associated with the value chain network. Receiving information related to the value chain network may include receiving sensor data indicating inbound and outbound shipment conditions. A simulation of the value chain network digital twin may be generated, and the simulation of the value chain network digital twin may be generated using a graph neural network (GNN). An optimization of the value chain network digital twin may be generated, and the simulation of the value chain network digital twin may be generated using a graph neural network (GNN). A robot operating system may realize the value chain network digital twin. The value chain network digital twin may operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets may include a built-in marketplace for digital twin simulations.The value chain network digital twin may operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets may include a built-in marketplace for one of artificial intelligence-based learning models or artificial intelligence-based algorithms.A value chain network digital twin can operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets can include an embedded marketplace for data. The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0053] In another example, a computing system may include one or more processors and one or more memories configured to perform operations, including but not limited to, receiving information related to the value chain network through a digital twin of the value chain network, the information encompassing a virtual representation of relationships between physical data items of the value chain network and may be dynamic, real-time, and time-series. The information may be provided to a set of artificial intelligence (AI)-based learning models, at least one member of the set of AI-based learning models may be trained with a training dataset of the set of operating data of the value chain network entities to classify at least one of an operating state, a fault state, an operating flow, or a behavior of the value chain network, and upon receiving the classification, the at least one member of the set of AI-based learning models may be trained with the training dataset to determine a task to complete for the value chain network. At least one of instructions for performing the task on the value chain network digital twin and a recommendation for performing the task on the value chain network digital twin may be provided.

[0054] One or more of the following example features may be included: One of a virtual reality (VR) environment, an augmented reality (AR) environment, a mixed reality (MR) environment, or a disappearing reality (DR) environment may be rendered for a user to interact with a sensor-based virtual representation of multiple relationships between physical data items of the value chain network. The information may include real-time data regarding one of inbound prepaid transportation from a supplier linked to an order or inventory entering a network associated with the value chain network. Receiving information related to the value chain network may include receiving sensor data indicating inbound and outbound transportation status. A simulation of the value chain network digital twin may be generated, and the simulation of the value chain network digital twin may be generated using a graph neural network (GNN). An optimization of the value chain network digital twin may be generated, and the simulation of the value chain network digital twin may be generated using a graph neural network (GNN). A robot operating system may implement the value chain network digital twin. A value chain network digital twin may operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets may include an embedded marketplace for digital twin simulations. A value chain network digital twin can operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets may include an embedded marketplace for one of artificial intelligence-based learning models or artificial intelligence-based algorithms. A value chain network digital twin can operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets includes an embedded marketplace for data.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0055] In another exemplary embodiment, a computer program product may reside on a computer-readable storage medium having stored thereon a plurality of instructions that, when executed across one or more processors, cause at least a portion of the one or more processors to perform operations, including, but not limited to, receiving information related to the value chain network through a value chain network digital twin, where the information may include a virtual representation of associations between physical data items of the value chain network, and the information may be dynamic, real-time, and time-series. The information may be provided to a set of artificial intelligence (AI)-based learning models, where at least one member of the set of AI-based learning models may be trained with a training dataset of the set of operating data of the value chain network entities to classify at least one of an operating state, a fault state, an operating flow, or a behavior of the value chain network, where at least one member of the set of AI-based learning models may be trained with the training dataset to determine a task to be completed for the value chain network upon receiving the classification. At least one of instructions for performing the task on the value chain network digital twin and a recommendation for performing the task on the value chain network digital twin may be provided.

[0056] One or more of the following example features may be included: One of a virtual reality (VR) environment, an augmented reality (AR) environment, a mixed reality (MR) environment, or a disappearing reality (DR) environment may be rendered for a user to interact with a sensor-based virtual representation of multiple relationships between physical data items of the value chain network. The information may include real-time data regarding one of an inbound prepaid shipment from a supplier linked to an order or inventory entering a network associated with the value chain network. Receiving information related to the value chain network may include receiving sensor data indicative of inbound and outbound transportation status. A simulation of the value chain network digital twin may be generated, and the simulation of the value chain network digital twin may be generated using a graph neural network (GNN). An optimization of the value chain network digital twin may be generated, and the simulation of the value chain network digital twin may be generated using a graph neural network (GNN). A robot operating system may implement the value chain network digital twin. A value chain network digital twin may operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets may include an embedded marketplace for digital twin simulations. A value chain network digital twin can operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets may include an embedded marketplace for one of artificial intelligence-based learning models or artificial intelligence-based algorithms. A value chain network digital twin can operate within a digital twin system having one or more sets of one or more digital twins, where each digital twin in the one or more sets includes an embedded marketplace for data.The set of value chain network entities may include at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training dataset for the set of AI-based learning models may include one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of an operating state, a fault state, an operating flow, or a behavior.

[0057] Details of one or more exemplary implementations are set forth in the accompanying drawings and the following description. Other possible exemplary features and / or possible exemplary advantages will become apparent from the description, drawings, and claims. Some embodiments may not have those possible exemplary features and / or possible exemplary advantages, and such possible exemplary features and / or possible exemplary advantages may not necessarily be required for some embodiments. A more complete understanding of the present disclosure will be seen from the following description and accompanying drawings, and the claims. All documents referenced herein are incorporated herein by reference. [Brief explanation of the drawings]

[0058] The accompanying drawings are included to provide a better understanding of the disclosure, illustrate embodiments of the disclosure, and together with the description explain various aspects of the disclosure.

[0059] [Figure 1] FIG. 1 is a block diagram illustrating the prior art relationships of various entities and facilities within a supply chain.

[0060] [Figure 2] FIG. 2 is a block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0061] [Figure 3] FIG. 3 is another block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0062] [Figure 4] FIG. 4 is a block diagram illustrating the components and interrelationships of the systems and processes of the digital product network shown in FIGS.

[0063] [Figure 5]FIG. 5 is a block diagram illustrating the system and process components and interrelationships of a value chain network technology stack according to the present disclosure.

[0064] [Figure 6] FIG. 6 is a block diagram illustrating a platform and relationships for orchestrating the control of various entities in a value chain network according to the present disclosure.

[0065] [Figure 7] FIG. 7 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0066] [Figure 8] FIG. 8 is a block diagram illustrating components and relationships of value chain entities managed by an embodiment of a value chain network management platform according to the present disclosure.

[0067] [Figure 9] FIG. 9 is a block diagram illustrating the network relationships of entities in a value chain network according to the present disclosure.

[0068] [Figure 10] FIG. 10 is a block diagram illustrating a set of applications supported by a unified data processing layer in a value chain network management platform according to the present disclosure.

[0069] [Figure 11] FIG. 11 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0070] [Figure 12]FIG. 12 is a block diagram illustrating components and relationships of a data storage layer in an embodiment of a value chain network management platform according to the present disclosure.

[0071] [Figure 13] FIG. 13 is a block diagram illustrating components and relationships of an adaptive intelligent system layer in an embodiment of a value chain network management platform according to the present disclosure.

[0072] [Figure 14] FIG. 14 is a block diagram illustrating providing an adaptive intelligent system for collaborative intelligence for a set of commodity category demand and supply applications according to the present disclosure.

[0073] [Figure 15] FIG. 15 illustrates a block diagram for providing a hybrid adaptive intelligence system for providing collaborative intelligence for a set of supply and demand applications or a category of goods in accordance with the present invention.

[0074] [Figure 16] FIG. 16 shows a block diagram for providing an adaptive intelligence system for providing predictive intelligence for a set of supply and demand applications in a category of goods in accordance with the present invention.

[0075] [Figure 17] FIG. 17 is a block diagram illustrating an adaptive intelligence system that provides classification intelligence for a set of product category supply and demand applications in accordance with the present invention.

[0076] [Figure 18] FIG. 18 is a block diagram illustrating an adaptive intelligence system for generating automatic control signals for a set of supply and demand applications for a commodity category in accordance with the present invention.

[0077] [Figure 19] FIG. 19 shows a block diagram for training an artificial intelligence / machine learning system to generate intelligent routing recommendations for a selected value chain network in accordance with the present invention.

[0078] [Figure 20] FIG. 20 shows a block diagram illustrating a semi-autonomous problem recognition system for recognizing pain points / problem situations in a value chain network in accordance with the present invention.

[0079] [Figure 21] FIG. 21 is a block diagram illustrating a set of artificial intelligence systems operating on value chain information to enable automated coordination of value chain activities in an enterprise in accordance with the present disclosure.

[0080] [Figure 22] FIG. 22 is a block diagram illustrating components and relationships in integrating a set of digital twins in an example value chain network management platform according to the present disclosure.

[0081] [Figure 23] FIG. 23 is a block diagram illustrating a set of digital twins involved in an embodiment of a value chain network management platform according to the present disclosure.

[0082] [Figure 24] FIG. 24 is a block diagram illustrating 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.

[0083] [Figure 25] FIG. 25 is a block diagram illustrating the components and relationships of a robotic process automation system in an embodiment of a value chain network management platform according to the present disclosure.

[0084] [Figure 26] FIG. 26 is a block diagram illustrating components and relationships of a set of opportunity miners in an embodiment of a value chain network management platform according to the present disclosure.

[0085] [Figure 27] FIG. 27 is a block diagram illustrating components and relationships of a set of edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0086] [Figure 28] FIG. 28 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0087] [Figure 29] FIG. 29 is a block diagram illustrating details of components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0088] [Figure 30] FIG. 30 is a block diagram illustrating components and relationships that enable centralized orchestration of value chain network entities in an embodiment of a value chain network management platform according to the present disclosure.

[0089] [Figure 31] FIG. 31 is a block diagram illustrating components and relationships of an integrated database in an embodiment of a value chain network management platform according to the present disclosure.

[0090] [Figure 32] FIG. 32 is a block diagram illustrating components and relationships of integrated data collection systems in an embodiment of a value chain network management platform according to the present disclosure.

[0091] [Figure 33] FIG. 33 is a block diagram illustrating components and relationships of IoT monitoring systems in an embodiment of a value chain network management platform according to the present disclosure.

[0092] [Figure 34] FIG. 34 is a block diagram illustrating 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.

[0093] [Figure 35] FIG. 35 is a block diagram illustrating the 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.

[0094] [Figure 36] FIG. 36 is a block diagram illustrating additional details of the 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.

[0095] [Figure 37] FIG. 37 is a block diagram illustrating components and relationships of a set of integrated adaptive intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0096] [Figure 38] FIG. 38 is a schematic diagram illustrating a system configuration for training an artificial system utilized by a value chain system using real-world outcome data and a digital twin system, according to some embodiments of the present disclosure.

[0097] [Figure 39]FIG. 39 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a container fleet management system, according to one embodiment of the present disclosure.

[0098] [Figure 40] FIG. 40 shows a schematic diagram of a system configuration for training an artificial system utilized by a logistics design system using real-world outcome data and a digital twin system, according to one embodiment of the present disclosure.

[0099] [Figure 41] FIG. 41 is a schematic diagram of a system for using real-world outcome data and a digital twin system to train an artificial system utilized by a packaging design system, according to one embodiment of the present disclosure.

[0100] [Figure 42] FIG. 42 is a schematic diagram of a system for using real-world outcome data and a digital twin system to train an artificial system utilized by a waste reduction system, according to one embodiment of the present disclosure.

[0101] [Figure 43] FIG. 43 is a schematic diagram illustrating a portion of an example information technology system for value chain artificial intelligence utilizing digital twins, according to one embodiment of the present disclosure.

[0102] [Figure 44] FIG. 44 is a block diagram illustrating components and relationships of a series of intelligent project management facilities in one embodiment of a value chain network management platform according to the present disclosure.

[0103] [Figure 45] FIG. 45 is a block diagram illustrating components and relationships of an intelligent task recommendation system in an embodiment of a value chain network management platform according to the present disclosure.

[0104] [Figure 46] FIG. 46 is a block diagram illustrating components and relationships of a routing system between nodes of a value chain network in an embodiment of a value chain network management platform according to the present disclosure.

[0105] [Figure 47] FIG. 47 is a block diagram showing components and relationships of a dashboard for managing a set of digital twins in a value chain network management platform in an embodiment of the present disclosure.

[0106] [Figure 48] FIG. 48 is a block diagram showing the components and relationships of a value chain network management platform in an embodiment of the present disclosure that employs a microservices architecture.

[0107] [Figure 49] FIG. 49 is a block diagram illustrating the components and relationships of an IoT data collection architecture and sensor recommendation system in an embodiment of a value chain network management platform.

[0108] [Figure 50] FIG. 50 is a block diagram illustrating components and relationships of a social data collection architecture in an embodiment of a value chain network management platform.

[0109] [Figure 51] FIG. 51 is a block diagram illustrating the components and relationships of a crowdsourcing data collection architecture in an embodiment of a value chain network management platform.

[0110] [Figure 52]FIG. 52 is a schematic diagram illustrating an example of a set of value chain network digital twins representing virtual models of a set of value chain network entities in accordance with the present disclosure.

[0111] [Figure 53] FIG. 53 is a schematic diagram illustrating an example of a warehouse digital twin kit system according to the present disclosure.

[0112] [Figure 54] FIG. 54 is a schematic diagram illustrating an example of a stress test for a value chain network according to the present disclosure.

[0113] [Figure 55] FIG. 55 is a schematic diagram illustrating an embodiment of a method for detecting machine faults and predicting future faults in accordance with the present invention.

[0114] [Figure 56] FIG. 56 is a schematic diagram illustrating an example of deploying machine twins to perform predictive maintenance on a fleet of machines in accordance with the present invention.

[0115] [Figure 57] FIG. 57 is a schematic diagram illustrating a portion of a value chain customer digital twin and customer profile digital twin system according to one embodiment of the present disclosure.

[0116] [Figure 58] FIG. 58 is a schematic diagram illustrating an example of an advertising application interfacing with an adaptive intelligent systems layer according to the present disclosure.

[0117] [Figure 59] FIG. 59 is a schematic diagram illustrating an example of an EC application integrated with an adaptive intelligent systems layer in accordance with the present disclosure.

[0118] [Figure 60]FIG. 60 is a schematic diagram illustrating an example of a demand management application integrated with an adaptive intelligent systems layer in accordance with the present disclosure.

[0119] [Figure 61] FIG. 61 is a schematic diagram illustrating an example of a system showing a portion of a digital twin of a value chain smart supply component, according to one embodiment of the present disclosure.

[0120] [Figure 62] FIG. 62 is a schematic diagram illustrating an example of a risk management application interfacing with an adaptive intelligence system layer according to the present disclosure.

[0121] [Figure 63] FIG. 63 is a schematic diagram of maritime assets associated with a value chain network management platform including port infrastructure components in accordance with the present disclosure.

[0122] [Figure 64] 64 and 65 are schematic diagrams of maritime assets associated with a value chain network management platform including a vessel component in accordance with the present disclosure. [Figure 65] 64 and 65 are schematic diagrams of maritime assets associated with a value chain network management platform including a vessel component in accordance with the present disclosure.

[0123] [Figure 66] FIG. 66 is a schematic diagram of maritime assets associated with a value chain network management platform including a barge component in accordance with the present disclosure.

[0124] [Figure 67] FIG. 67 is a schematic diagram of maritime assets associated with a value chain network management platform including components involved in maritime events, legal proceedings, and utilizing geofence parameters in accordance with the present disclosure.

[0125] [Figure 68] FIG. 68 is a schematic diagram illustrating an exemplary environment of enterprise and executive control towers and management platforms, and data sources communicating therewith, according to one embodiment of the present disclosure.

[0126] [Figure 69] FIG. 69 is a schematic diagram illustrating an exemplary set of components of an enterprise control tower and management platform according to one embodiment of the present disclosure.

[0127] [Figure 70] FIG. 70 is a schematic diagram illustrating an example of an enterprise data model in one embodiment of the present disclosure.

[0128] [Figure 71] Figure 71 is a schematic diagram showing examples of different types of enterprise digital twins, including management layer digital twins, in relation to the data layer, processing layer, and application layer of an enterprise digital twin framework in one embodiment of the present disclosure.

[0129] [Figure 72] FIG. 72 is a schematic diagram illustrating an exemplary implementation of an enterprise and executive control tower and management platform in accordance with one embodiment of the present disclosure.

[0130] [Figure 73] Figure 73 is a flowchart illustrating an example set of operations for configuring and providing an enterprise digital twin.

[0131] [Figure 74] FIG. 74 shows a set of operational examples of a method for constructing an organizational digital twin.

[0132] [Figure 75] Figure 75 shows a set of operational examples of the method for creating an executive digital twin.

[0133] [Figure 76] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 77] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 78] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 79] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 80] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 81] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 82] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 83] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 84] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 85] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 86] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 87] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 88]76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 89] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 90] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 91] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 92]76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 93] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 94] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 95] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 96]76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 97] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 98] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 99] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 100]76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 101] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 102] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes. [Figure 103] 76-103 are schematic diagrams illustrating exemplary neural net systems in accordance with embodiments of the present invention that are connected, integrated, and accessible to platforms that enable intelligent transactions, including expert systems, self-organization, machine learning, and artificial intelligence. These systems include neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and other purposes.

[0134] [Figure 104] FIG. 104 is a schematic diagram illustrating an example of an intelligent service system according to one embodiment of the present disclosure.

[0135] [Figure 105] FIG. 105 is a schematic diagram illustrating an example of a neural network having multiple layers according to one embodiment of the present disclosure.

[0136] [Figure 106] FIG. 106 is a schematic diagram of an exemplary convolutional neural network (CNN) illustrating an example embodiment of the present invention.

[0137] [Figure 107] FIG. 107 is a schematic diagram of a neural network for implementing exemplary natural language processing according to an embodiment of the present invention.

[0138] [Figure 108] FIG. 108 is a schematic diagram illustrating an example of a reinforcement learning-based approach for a mobile system to perform one or more tasks according to one embodiment of the present disclosure.

[0139] [Figure 109] FIG. 109 is a schematic diagram illustrating an example of a physical orientation determination chip according to one embodiment of the present disclosure.

[0140] [Figure 110] FIG. 110 is a schematic diagram showing an example of a network reinforcement chip in one embodiment of the present invention.

[0141] [Figure 111] FIG. 111 is a schematic diagram showing an example of a diagnostic chip in one embodiment of the present invention.

[0142] [Figure 112] FIG. 112 is a schematic diagram showing an example of a governance chip in one embodiment of the present invention.

[0143] [Figure 113] FIG. 113 is a schematic diagram illustrating an example of prediction, classification, and recommended tips in one embodiment of the present disclosure.

[0144] [Figure 114] FIG. 114 is a diagram illustrating an example of an autonomous additive manufacturing platform in one embodiment of the present disclosure.

[0145] [Figure 115] FIG. 115 is a schematic diagram illustrating an exemplary implementation of an autonomous additive manufacturing platform for automating and optimizing digital production workflows for metal additive manufacturing in accordance with one embodiment of the present invention.

[0146] [Figure 116] FIG. 116 is a flow chart illustrating the optimization of different parameters of an additive manufacturing process, according to one embodiment of the present invention.

[0147] [Figure 117] FIG. 117 is a schematic diagram illustrating a system for training an artificial intelligence system for classification, prediction, and decision-making using a digital twin using data from an autonomous additive manufacturing platform, in accordance with one embodiment of the present invention.

[0148] [Figure 118] FIG. 118 is a schematic diagram illustrating an exemplary implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network, in accordance with one embodiment of the present invention.

[0149] [Figure 119] FIG. 119 is a schematic diagram illustrating an exemplary implementation of an autonomous additive manufacturing platform that automates and manages manufacturing functions and sub-processes (including process and material selection, hybrid part workflow, raw material formulation, part design optimization, risk prediction and management, marketing, and customer service) in accordance with one embodiment of the present invention.

[0150] [Figure 120] FIG. 120 illustrates a conceptual diagram of a distributed manufacturing network implemented by an autonomous additive manufacturing platform and built on a distributed ledger system, in accordance with one embodiment of the present invention.

[0151] [Figure 121] FIG. 121 is a schematic diagram illustrating an exemplary implementation of a distributed manufacturing network in which digital thread data in the distributed manufacturing network is tokenized and stored in a distributed ledger to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network, in accordance with one embodiment of the present invention.

[0152] [Figure 122] FIG. 122 is a diagrammatic view showing an exemplary embodiment of a conventional computer vision system for creating an image of an object of interest.

[0153] [Figure 123] FIG. 123 is a schematic diagram illustrating an exemplary embodiment of a dynamic vision system that dynamically learns object concepts related to objects of interest, in accordance with one embodiment of the present invention.

[0154] [Figure 124] FIG. 124 is a schematic diagram illustrating an example architecture of a dynamic vision system according to one embodiment of the present disclosure.

[0155] [Figure 125] FIG. 125 is a flow chart illustrating a method for object recognition by a dynamic vision system according to one embodiment of the present disclosure.

[0156] [Figure 126]FIG. 126 is a schematic diagram illustrating an exemplary implementation of a dynamic vision system for modeling, simulating, and optimizing various optical, mechanical, design, and lighting parameters of a dynamic vision system in accordance with one embodiment of the present invention.

[0157] [Figure 127] FIG. 127 is a schematic diagram illustrating an example implementation of a dynamic vision system in one embodiment of the present invention, showing a detailed view of the various components of the dynamic vision system as well as the integration of the dynamic vision system with one or more third party systems.

[0158] [Figure 128] FIG. 128 is a schematic diagram illustrating an exemplary environment for a fleet management platform in one embodiment of the present invention.

[0159] [Figure 129] FIG. 129 is a schematic diagram illustrating an exemplary configuration of a multi-purpose robot and a special-purpose robot in one embodiment of the present disclosure.

[0160] [Figure 130] FIG. 130 is a schematic diagram illustrating an exemplary configuration of a platform-level intelligence layer of a fleet management platform in one embodiment of the present disclosure.

[0161] [Figure 131] FIG. 131 is a schematic diagram illustrating an exemplary configuration of an intelligence layer in one embodiment of the present invention.

[0162] [Figure 132] FIG. 132 is a schematic diagram illustrating an exemplary configuration of a security framework in one embodiment of the present invention.

[0163] [Figure 133] FIG. 133 is a schematic diagram illustrating an exemplary environment for a fleet management platform in accordance with one embodiment of the present invention.

[0164] [Figure 134] FIG. 134 is a schematic diagram illustrating an example of a data flow in a job configuration system according to an embodiment of the present disclosure.

[0165] [Figure 135] FIG. 135 is a schematic diagram showing an example of data flow in a fleet operation system in one embodiment of the present disclosure.

[0166] [Figure 136] FIG. 136 is a schematic diagram showing an example of a job analysis system and a task definition system according to an embodiment of the present disclosure, and the data flow thereof.

[0167] [Figure 137] FIG. 137 is a schematic diagram showing an example of a fleet configuration system and its data flow in one embodiment of the present disclosure.

[0168] [Figure 138] FIG. 138 is a schematic diagram illustrating an exemplary workflow definition system and its exemplary data flow according to one embodiment of the present invention.

[0169] [Figure 139] FIG. 139 is a schematic diagram illustrating an example configuration of an exemplary multi-purpose robot and its components in accordance with one embodiment of the present invention.

[0170] [Figure 140] FIG. 140 is a schematic diagram illustrating an exemplary architecture of a robotic control system in one embodiment of the present invention.

[0171] [Figure 141] FIG. 141 is a schematic diagram illustrating an exemplary architecture in which a robotic control system 12150 utilizes data from multiple sensors from a vision and sensing system in one embodiment of the present invention.

[0172] [Figure 142] FIG. 142 is a schematic diagram illustrating an exemplary vision and sensing system of a robot in accordance with one embodiment of the present invention.

[0173] [Figure 143] FIG. 143 is a schematic diagram illustrating an exemplary process performed by a multipurpose robot to harvest crops in accordance with an embodiment of the present invention.

[0174] [Figure 144] FIG. 144 is a schematic diagram illustrating an example environment of an intermodal smart container system in one embodiment of the present disclosure.

[0175] [Figure 145] FIG. 145 is a schematic diagram showing an example configuration of a smart container in one embodiment of the present disclosure.

[0176] [Figure 146] Figure 146 is a schematic diagram showing an example configuration of an intelligence service that provides intelligence services to a smart intermodal container system according to one embodiment of the present invention.

[0177] [Figure 147] Figure 147 is a schematic diagram showing an example configuration of a digital twin module according to one embodiment of the present invention.

[0178] [Figure 148] FIG. 148 illustrates an exemplary embodiment of a method for receiving a request to update one or more properties of a digital twin of a transportation entity and / or an environment.

[0179] [Figure 149] FIG. 149 shows an illustrative example of a method for updating a set of downtime cost values ​​in a smart container's digital twin, according to one embodiment of the present invention.

[0180] [Figure 150] FIG. 150 is a schematic diagram illustrating an exemplary environment of a digital product network in one embodiment of the present disclosure.

[0181] [Figure 151] FIG. 151 is a schematic diagram illustrating an exemplary environment for connected products in one embodiment of the present disclosure.

[0182] [Figure 152] FIG. 152 is a schematic diagram illustrating an exemplary environment for a digital product network in accordance with one embodiment of the present invention.

[0183] [Figure 153] FIG. 153 is a schematic diagram illustrating an exemplary environment for a digital product network in accordance with one embodiment of the present invention.

[0184] [Fig. 154] Figure 154 is a flow chart illustrating how product level data is used in one embodiment of the present invention.

[0185] [Figure 155] FIG. 155 is a schematic diagram illustrating an exemplary environment of a digital product network in one embodiment of the present disclosure.

[0186] [Figure 156] FIG. 156 is a schematic diagram illustrating an example of a smart futures contract system in one embodiment of the present disclosure.

[0187] [Figure 157] FIG. 157 is a schematic diagram illustrating an exemplary environment of an edge network system in one embodiment of the present disclosure.

[0188] [Figure 158] FIG. 158 is a schematic diagram illustrating an exemplary environment of an edge network system including a VCN bus in one embodiment of the present disclosure.

[0189] [Figure 159] 159 is a schematic diagram showing an example environment of an edge network system in one embodiment of the present invention. This system includes a configured device EDNW system.

[0190] [Figure 160] FIG. 160 is a schematic diagram illustrating an exemplary embodiment of a quantum computing service in one embodiment of the present invention.

[0191] [Figure 161] Figure 161 shows the process of requesting a quantum computing service in one embodiment of the present invention.

[0192] [Figure 162] FIG. 162 is a schematic diagram illustrating an embodiment of a biology-based value chain network system according to the present invention.

[0193] [Figure 163] FIG. 163 is a schematic diagram showing the Taramis service according to the present invention and how it cooperates within modules.

[0194] [Fig. 164] FIG. 164 illustrates a block diagram of an energy system capable of communicating with similar systems, subsystems, components, and a value chain network management platform in accordance with one embodiment of the present invention.

[0195] [Figure 165] FIG. 165 shows a block diagram illustrating an overview of a dual-process artificial neural network system in accordance with one embodiment of the present invention.

[0196] [Figure 166A] FIG. 166A is a schematic diagram illustrating an exemplary environment of a distributed database system according to the present disclosure.

[0197] [Figure 166B] FIG. 166B is a schematic diagram illustrating an exemplary architecture of a distributed database system according to the present disclosure.

[0198] [Figure 167A] 167A-167B are illustrative diagrams illustrating the storage of data in a distributed database system according to the present disclosure. [Figure 167B] 167A-167B are illustrative diagrams illustrating the storage of data in a distributed database system according to the present disclosure.

[0199] [Figure 168A] 168A-168B are illustrative diagrams illustrating systems and modules for implementing a distributed database system according to the present disclosure. [Figure 168B] 168A-168B are illustrative diagrams illustrating systems and modules for implementing a distributed database system according to the present disclosure.

[0200] [Figure 169A] 169A-169B are process diagrams illustrating an example method by which a distributed database system responds to received queries according to this disclosure. [Figure 169B] 169A-169B are process diagrams illustrating an example method by which a distributed database system responds to received queries according to this disclosure.

[0201] [Figure 169C] 169C-169D are process diagrams illustrating an exemplary method for optimizing a dynamic ledger maintained by a distributed database system according to this disclosure. [Figure 169D] 169C-169D are process diagrams illustrating an exemplary method for optimizing a dynamic ledger maintained by a distributed database system according to this disclosure.

[0202] [Figure 170A] 170A-170B show data flow diagrams of exemplary data table creation queries processed by a distributed database system in accordance with this disclosure. [Figure 170B] 170A-170B show data flow diagrams of exemplary data table creation queries processed by a distributed database system in accordance with this disclosure.

[0203] [Figure 171A] 171A-171B show data flow diagrams of exemplary select queries processed by a distributed database system according to this disclosure. [Figure 171B] 171A-171B show data flow diagrams of exemplary select queries processed by a distributed database system according to this disclosure.

[0204] [Figure 172A] 172A-172C are data flow diagrams illustrating the operation of an exemplary distributed join query performed in a distributed database system in accordance with the present disclosure. [Figure 172B] 172A-172C are data flow diagrams illustrating the operation of an exemplary distributed join query performed in a distributed database system in accordance with the present disclosure. [Figure 172C] 172A-172C are data flow diagrams illustrating the operation of an exemplary distributed join query performed in a distributed database system in accordance with the present disclosure.

[0205] [Figure 173] FIG. 173 is a schematic diagram of an example control tower dashboard for one or more VCN processes that can be used in conjunction with one or more example implementations of the present disclosure.

[0206] [Fig. 174] FIG. 174 is an example flowchart of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure.

[0207] [Figure 175A] FIG. 175A is a schematic diagram of an exemplary control architecture for system support and / or management.

[0208] [Figure 175B] FIG. 175B is a schematic diagram of another exemplary control architecture for system support and / or management.

[0209] [Figure 176A] FIG. 176A is a schematic diagram of an example management stack including a control architecture similar to that of FIGS. 175A and 175B.

[0210] [Figure 176B] FIG. 176B is a schematic diagram of an exemplary management stack that can implement a control architecture similar to that of FIGS. 175A and 175B, designed for a value chain network.

[0211] [Figure 177A] FIG. 177A is a flow chart illustrating an example of a control architecture similar to FIGS. 175A and 175B.

[0212] [Figure 178] FIG. 178 is a flowchart illustrating one or more example VCN processes that may be used in conjunction with one or more of the example implementations of the present disclosure.

[0213] [Figure 175C] FIG. 175C is a schematic diagram of an exemplary control architecture for system support and / or management.

[0214] [Figure 175D] FIG. 175D is a schematic diagram of another exemplary control architecture for system support and / or management.

[0215] [Figure 176C] FIG. 176C is a schematic diagram of an exemplary management stack including a control architecture similar to that of FIGS. 175C and 175D.

[0216] [Figure 176D] FIG. 176D is a schematic diagram of an exemplary management stack capable of implementing a control architecture similar to that of FIGS. 175C and 175D, designed for a value chain network.

[0217] [Figure 177B] FIG. 177B is a flow chart of an exemplary arrangement having a control architecture similar to that of FIGS. 175C and 175D.

[0218] [Figure 179] 179-185 are example flowcharts of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure. [Figure 180] 179-185 are example flowcharts of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure. [Figure 181] 179-185 are example flowcharts of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure. [Figure 182] 179-185 are example flowcharts of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure. [Figure 183] 179-185 are example flowcharts of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure. [Figure 184] 179-185 are example flowcharts of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure. [Figure 185]179-185 are example flowcharts of one or more VCN processes that may be used in conjunction with one or more example implementations of the present disclosure.

[0219] Like reference symbols in the drawings indicate like elements. DETAILED DESCRIPTION OF THE INVENTION

[0220] In exemplary embodiments of the present disclosure, systems and processes may include information technology processes and systems for management of value chain network entities, including supply chain and demand management entities. In exemplary embodiments, an enterprise management platform may be used, particularly one that is used to store and retrieve value chain data using edge-distributed databases and query languages.

[0221] Product orders were fulfilled by manufacturers through the supply chain as shown in Figure 1. Suppliers 122 operated manufacturing facilities 134 in various supply environments 160 or acted as resellers or distributors on behalf of others, filling orders and providing products 130 at the point of origin 102. As the products 130 moved through the supply chain, they were transported and stored via various transportation facilities 138 and distribution facilities 134, including warehouses 132, fulfillment centers 112, and distribution systems 114 (such as trucks and other vehicles, trains, etc.). Often, maritime facilities and infrastructure, such as ships, barges, piers, and ports, provided transportation by water between the point of origin 102 and one or more destinations 104.

[0222] Organizations have access to nearly limitless amounts of data. The emergence of technologies such as smart connected devices, wearable technology, and the Internet of Things (IoT) has dramatically increased the amount of data available to organizations that plan, supervise, manage, and operate their value chain networks—and this growth is expected to continue. For example, manufacturing facilities, warehouses, campuses, and other operational environments may contain hundreds to thousands of IoT sensors, including vibration data measuring critical machinery vibration patterns, facility temperature and traffic tracking motion sensors, asset tracking sensors and beacons, cameras and optical sensors, and chemical and biological sensors. Furthermore, as wearable technology becomes more prevalent, wearable devices provide insights into employee behavior, health indicators, physiological status, activity status, and behavioral patterns. Furthermore, as organizations adopt systems that leverage information and information technology, such as CRM systems, ERP systems, operations systems, information technology systems, and advanced analytics systems, they gain access to increasingly diverse and large datasets, including marketing data, sales data, operations data, information technology data, performance data, customer data, financial data, market data, pricing data, and supply chain data—data generated within the organization and acquired from third parties.

[0223] The existence of more data and new types of data offers many opportunities for organizations to gain a competitive advantage, but it also creates challenges such as complexity and volume, which can overwhelm users and result in missed insights. Companies need methods and systems that can go beyond data acquisition to transform data into insights and connect those insights to the right execution of informed decisions and efficient operations.

[0224] It is becoming increasingly common to acquire large data sets from thousands, or potentially millions, of devices (containing numerous sensors) distributed across multiple organizations across value chain networks. For example, the widespread use of Radio Frequency Identification (RFID) tags on individual items in retail stores. In these and similar situations, the massive data streams can overwhelm the network's ability to transmit the data and the ability for effective, automated, centralized decision-making.

[0225] The proliferation of data-generating devices (e.g., sensors) creates opportunities to manage networks, such as value chain networks, leveraging input from many distributed, semi-intelligent control points. However, current approaches often rely on limited, centralized data collection due to bandwidth, storage, processing power, or other limitations.

[0226] Over time, companies have increasingly leveraged technology solutions to improve outcomes associated with traditional supply chains (such as those depicted in Figure 1). Examples include software systems for forecasting and managing customer demand, RFID and asset tracking systems for tracking goods through the supply chain, and navigation and routing systems for more efficient route selection. However, several major trends are putting increasing pressure on manufacturers, retailers, and other companies to improve supply chain performance. First, online and e-commerce retailers, particularly Amazon®, have become the largest retail channel in many product categories, establishing distribution and fulfillment centers112 in the United States and elsewhere that stock hundreds, and in some cases even more, of product categories (SKUs), allowing customers to receive their orders the next day, and in some cases, the same day (with delivery also possible in some regions via drones, robots, and / or autonomous vehicles). For retailers lacking a geographical presence of distribution centers and warehouses, customer expectations for delivery speed are increasing pressure to streamline and optimize their supply chains. Therefore, improvements to supply chain methods and systems are required.

[0227] Second, agile manufacturing capabilities (such as 3D printing and robotic assembly technologies), customer profiling techniques, and online ratings and reviews are driving customer expectations for product customization and personalization. Therefore, to stay competitive, manufacturers and retailers must improve their methods and systems for understanding, predicting, and satisfying customer demand.

[0228] Historically, supply chain management and demand planning and management have primarily been separate activities, integrating at the stage where demand is converted into orders and communicated to the supply side. Increasing expectations of speed and personalization require methods and systems that enable the integrated coordination of supply and demand.

[0229] Parallel to these other major trends is the rise of the Internet of Things (IoT). Some smart home products, particularly those (e.g., thermostats, lighting systems, and speakers), increasingly feature on-board network connectivity and processing power, incorporating voice-controlled intelligent agents like Alexa® or Siri® to control devices and trigger specific application functions (e.g., playing music or ordering products). Some smart products 650 may even issue orders themselves, such as a printer ordering refill cartridges. While intelligent products 650 may function as part of a collaborative system, such as an Amazon® Echo® product controlling a TV, or sensor-equipped thermostats and security cameras connecting with mobile devices, most intelligent products primarily engage in isolated, application-specific sets of interactions. As artificial intelligence capabilities improve and computational and networking functions increasingly move to the supply environment 670, the demand environment 672, and connected edge devices and systems located everywhere, including systems and facilities located along the flow of product from the manufacturing site's unloading area to customer or retailer delivery, there is a need and opportunity to dramatically improve the intelligence, control, and automation of all factors involved in supply and demand in the systems and facilities along the path of product 1510 from the manufacturer's unloading area to its final destination 612 at the customer 662 or retailer 664. Value Chain Network

[0230] Referring to FIG. 2, a block diagram illustrating the components and interrelationships of value chain network systems and processes is shown at 200. In this example, the term "value chain network" refers to the historically separate elements and interconnections of demand management systems and processes and supply chain management systems and processes, realized through the advancement and convergence of various technologies. In an exemplary embodiment, a value chain control tower 260 (sometimes referred to in some parts of this specification as a "value chain network management platform," "VCNP," or simply the "system" or "platform") is connected, communicatively, or otherwise functionally coupled to data processing facilities, including, but not limited to, data processing facilities (e.g., big data centers (big data processing 230)), and related processing functions. These functions receive data flows, data pools, data streams, or other data structures and transmission methods received from, for example, a digital product network 21002, directly connected customers (e.g., directly connected customers 250), or other third parties 220. Communications 210, analytics 232, or other types of input related to market orchestration activities may be utilized by a value chain control tower for collaborative and adaptive intelligence-based demand improvement 262, synchronized planning 234, intelligent sourcing 238, dynamic fulfillment 240, or other smart operations, as described herein.

[0231] Referring to FIG. 3, another block diagram illustrating the components and interrelationships of value chain network systems and processes, associated use cases, data processing, and related entities is shown. In an exemplary embodiment, a value chain control tower 360 coordinates market orchestration activities 310, including, but not limited to, demand curve management 352, ecosystem synchronization 348, intelligent sourcing 344, dynamic fulfillment 350, value chain analytics 340, and / or smart supply chain operations 342. In an exemplary embodiment, the value chain control tower 360 is connected to, in communication with, or otherwise operationally coupled to, adaptive data pipelines 302 and processing facilities, which may further be connected to, in communication with, or otherwise operationally coupled to external data sources 320 and data processing stacks 330 (e.g., value chain network technologies), which may include intelligent and user-adaptive interfaces, adaptive intelligence and control 332, and / or adaptive data monitoring and storage 334, as described herein. The value chain control tower 302 is further connected to, communicative with, or otherwise operationally coupled to additional value chain entities, including, but not limited to, the digital product network 21002, customers (e.g., direct connection customers 362), and / or other connected operations 364 or entities of the value chain network. Digital Product Network (“DPN”)

[0232] Referring to Figure 4, a block diagram illustrating the components and interrelationships of systems and processes in a digital product network is shown. In an exemplary embodiment, products (including goods and services) create and transmit data, such as product-level data, to communication layers and / or edge data processing facilities in the value chain network technology stack. This data may be combined with third-party data for further processing, modeling, or adaptive or collaborative intelligence activities, as described herein. This may include generating and / or simulating product and value chain use cases, and the data may be utilized in products, product development processes, product designs, etc. Stack View Example

[0233] As shown in FIG. 5, a block diagram illustrating the system and process components and interrelationships of a value chain network technology stack is shown at 500. This includes, but is not limited to, a presentation layer, an intelligence layer, and serverless functions (e.g., development and hosting platforms, data facilities related to IoT and big data, data aggregation facilities, etc.). In an exemplary embodiment, the presentation layer includes, but is not limited to, a user interface, user experience and engagement research, discovery, and tracking modules, etc. In an exemplary embodiment, the intelligence layer includes, but is not limited to, statistical and computational techniques, semantic models, analytics libraries, analytics development environments, algorithms, logic and rules, machine learning, etc. In an exemplary embodiment, the platform or value chain network technology stack includes, but is not limited to, a development environment, connectivity APIs, cloud and / or hosting applications, device detection, etc. In an exemplary embodiment, the data aggregation facility or layer includes, but is not limited to, a data normalization module for common transmission and heterogeneous data collection from various devices. In an exemplary embodiment, the data facilities or layers include, but are not limited to, IoT and big data access, control, collection, and interoperability. In an exemplary embodiment, the value chain network technology stack may be further associated with additional data sources and / or technology enablers. From Value Chain Orchestration to Command Platform

[0234] Figure 6 illustrates a value chain network management platform 604 (sometimes referred to herein as a "value chain control tower," "VCNP," or simply the "system" or "platform") that coordinates the planning, monitoring, control, and optimization of various factors (e.g., supply and production factors, demand factors, logistics and delivery factors) in a value chain network 668. The unified platform 604 enables the monitoring and management of supply and demand factors, as well as the sharing of status information (e.g., quality and status, planning, ordering and confirmation, tracking and tracing, etc.) among various entities in the supply chain, such as customers / consumers, suppliers, and distributors. As demand factors are understood and considered, orders are generated and fulfilled, and products are manufactured and moved through the supply chain, the value chain network 668 may include not only the intelligent product 1510 but also all the facilities, infrastructure, personnel, and other entities involved in planning and satisfying that demand. Value Chain Network and Value Chain Network Management Platform

[0235] Referring to FIG. 7, a value chain network 668 managed by the value chain management platform 604 may include the following set of value chain network entities 652: products 1510 (including intelligent products 1510); a set of production facilities 674 that manufacture finished products, components, systems, subsystems, materials used in the products, or the like; various entities, activities, and other supply factors 648 (e.g., suppliers 642, places of origin 610, etc.) involved in the supply environment 670; various entities, activities, and other demand factors 644 involved in the demand environment 672 (e.g., customers 662 (including consumers, businesses, and intermediate customers such as value-added resellers and distributors), retailers 664 (online retailers, etc.)); and located and / or operating in various destinations 612, such as line retailers, mobile retailers, traditional brick-and-mortar retailers, pop-up shops, etc.; including various distribution environments 678 and distribution facilities 658 (e.g., warehousing facilities 654, fulfillment facilities 628, delivery systems 632, etc.) and offshore facilities 622 (e.g., port infrastructure facilities 660, floating assets 620, shipyards 638, etc.). In an example, the value chain network management platform 604 enables the monitoring, control, and management (sometimes including autonomous or semi-autonomous operation) of the various processes, workflows, activities, events, and applications 630 (sometimes collectively referred to simply as “applications 630”) of the value chain network 668.

[0236] Referring 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 is comprised of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements that work in concert to enable the intelligent management of value chain entities 652. These value chain entities may occur, operate, transact, or otherwise participate within a value chain network 668. or owns, operates, supports, or enables one or more value chain network processes, workflows, activities, events, and / or applications 630, or in connection with a VCNP 604, a product 1510 (including a finished product, software product, hardware product, component product, material, piece of equipment, consumer packaged goods, consumer product, food product, beverage product, household product, commercial supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or set of other types of products and / or related services) operated by, or ... or operated by VCNP604, or if operated by VCNP604, or if operated by VCNP604, or if operated by VCNP604, or if operated by VCNP604, or if operated by VCNP604, or if operated by VCNP604, or if operated by VCNP604, or may include an intelligent product 1510 with capabilities including, but not limited to, natural language processing, voice recognition, 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 IoT capabilities.

[0237] In an example, the management platform 604 includes a set of data processing layers 608 that provide a range of capabilities to facilitate the development and deployment of intelligence. These capabilities include automation, machine learning, the application of artificial intelligence, intelligent transactions, state management, event management, process management, and the like, addressing a variety of value chain network applications and end uses. In an embodiment, the data processing layers 608 are configured in a topology that facilitates the collection and distribution of data among multiple applications and uses within the platform 604 by a value chain monitoring system layer 614. The value chain monitoring system layer 614 may include, integrate with, and / or interface with various data collection and management systems 640, conveniently referred to as data collection systems 640, to collect and organize data collected from or about value chain entities 652 and from or about various data layers 624 or their services or components. In an embodiment, the data processing layer 608 is configured in a topology that facilitates shared or common data storage among multiple applications and uses of the platform 604 through a value chain network-oriented data storage system layer 624 (for convenience, referred to herein simply as data storage layer 624 or storage layer 624). As shown in FIG. 7, the data processing layer 608 may also include an adaptive intelligent systems layer 614. The adaptive intelligent systems layer 614 includes a set of data processing, artificial intelligence, and computation systems 634, described later in this specification. The data processing, artificial intelligence, and computation systems 634 relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural networks, supervised learning, machine learning, deep learning, model-based systems, etc.).Specifically, the data processing, artificial intelligence, and computation system 634, in some embodiments, may be an adaptive intelligence system operating on a transactional blockchain within a supply chain using recurrent networks to determine patterns, collaboration with biological systems, opportunity mining (e.g., an artificial intelligence system monitoring new data sources for opportunities to automatically deploy intelligence), robotic process automation (e.g., edge and network intelligence (e.g., monitoring systems using adaptively available RF spectrum, monitoring systems using adaptively available fixed network spectrum, systems that adaptively store data based on available storage conditions, systems that adaptively sense based on context-sensitive sensing, etc.)), etc.

[0238] In examples, the data processing layer 608 is depicted in the diagram as a vertical stack or ribbon, representing many of the capabilities available to the platform 604 (e.g., storage, monitoring, processing applications and resources, combinations thereof, etc.). In examples, the feature set of the data processing layer 608 may include a shared microservices architecture. Throughout these examples, the feature set may be deployed to provide multiple independent services or applications that can be configured as one or more services, workflows, or combinations thereof. In some examples, the feature set may be deployed within or inherent to a particular application or process. In some examples, the feature set may include one or more activities marshaled for the benefit of the platform. In some examples, the feature set may include one or more events orchestrated for the benefit of the platform. In examples, one of the platform's feature sets may be deployed within at least a portion of a common architecture that supports a common data schema. In examples, one of the platform's feature sets may be deployed within at least a portion of a common architecture that supports common storage. In examples, one of the platform's feature sets may be deployed within at least a portion of a common architecture that supports a common monitoring system. In embodiments, one or more sets of platform functionality may be deployed at least in part to a common architecture that supports one or more common processing frameworks. In embodiments, data processing layer 608 functionality sets include examples where storage functionality supports scalable processing functionality, a scalable monitoring system, a digital twin system, a payment interface system, etc. Through these examples, the platform may provide one or more software development kits (SDKs) and deployment interfaces to facilitate connection and utilization of the data processing layer 608 functionality.Additionally, the adaptive intelligence system may analyze, learn, configure, and reconfigure one or more functions of the data processing layer 608. In an embodiment, the platform 604 may include a common data storage schema for providing, for example, shipyard-related services and warehouse management services. Many applicable examples and combinations exist within the above examples, including many of the value chain entities disclosed herein. Through these examples, the platform 604 is shown to establish connectivity (e.g., functionality and information provision) between many value chain entities. In many examples, there are pairings (e.g., double, triple, quad, etc.) of similar types of value chain entities that utilize (interact, depend, etc.) a common data schema, common architecture, common interfaces, etc., using one or more smaller sets of functions in the data processing layer 608. While services and functions may be provided to a single value chain entity, the platform is shown to provide diverse benefits to the value chain and consumers by facilitating connectivity between the value chain entities and the applications used by them. Platform-managed value chain network entities

[0239] Referring to FIG. 8 , the value chain network management platform 604 is shown in relation to a set of value chain entities 652 that may be managed by the platform 604, integrate with, incorporate into, provide input to, or receive output from the platform 604. These entities are shown in relation to value chain entities 652 involved in supply chain activities, logistics activities, demand management and planning activities, distribution activities, transportation activities, warehousing activities, delivery and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many other activities. These activities are incorporated into various value chain network processes, workflows, activities, events, and applications 630 (collectively referred to as “applications 630” or simply “activities”). Connectivity with the value chain entities 652 is facilitated by a set of connectivity capabilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in more detail below. This includes connectivity and interface capabilities between the platform's individual services, data processing layers, the platform as a whole, and / or the value chain entities 652.

[0240] These value chain entities 652 include, but are not limited to, a wide range of assets, systems, devices, machines, components, equipment, facilities, individuals, or other entities described herein or in the documents incorporated by reference herein. For example, machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, material handling machines, cranes, lifts, carriers, trucks, material handling machines, unloading machines, packing machines, picking machines, and many others, such as robotic systems (e.g., physical robots, collaborative robots (e.g., "cobots"), drones, autonomous vehicles, software bots, etc.)); products 650 (which may include finished products, software products, hardware products, component products, materials, equipment, consumer packaged goods, consumer goods, food products, beverage products, household products, commercial consumables, pharmaceutical products, medical device products, technology products, entertainment products, or any other type of product and / or set of related services); value chain processes 722 (e.g., transportation process, shipping process, ocean process, inspection process, transport process, loading / unloading process, packing / unpacking process, configuration process, assembly process, installation process, quality control process, environmental control process (e.g., temperature control, humidity control, etc.)); control, pressure control, vibration control, etc.), border control processes, port-related processes, software processes (including applications, programs, services, etc.), packing and loading processes, financial processes (e.g., insurance processes, reporting processes, trading processes, etc.), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many other processes; wearable and portable devices 720 (e.g., smartphones, tablets, portable devices dedicated to value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-worn devices, clothing-integrated devices, armbands, bracelets, neck-worn devices, AR / VR devices, headphones, and many other devices); employees 718 (e.g., delivery personnel, transportation workers, barge workers, port workers, dock workers, rail workers, ship workers,fulfillment center workers, warehouse workers, vehicle drivers, operations managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, cargo handling workers, inspectors, delivery personnel, environmental managers, financial asset managers, process supervisors and workers (those engaged in any of the processes mentioned herein), security personnel, safety personnel and many others; Suppliers 642 (e.g., suppliers of all kinds of goods and related services, parts suppliers, raw material suppliers, manufacturers and many others); Customers 662 (e.g., consumers, licensees, companies, entities, value-added distributors and other resellers, retailers, end users, wholesalers and those who purchase, license or otherwise use categories of goods and / or related services); Various Operational Facilities 712 (e.g., cargo handling facilities, warehouse facilities 654, safes, distribution facilities 658, fulfillment centers 628, aviation facilities 740 (e.g., aircraft, airports, hangars, runways, fueling bases), marine facilities 622 (e.g., port infrastructure facilities 622 (piers, yards, cranes, roll-on / roll-off facilities, ramps, containers, container handling systems, waterways 732, locks, and many others), shipbuilding facilities 638, floating assets 620 (vessels, barges, boats, etc.), facilities and other items at origin 610 and / or destination 628, transportation facilities 710 (container ships, barges, other floating assets 620), and land vehicles and other delivery systems 632 (trucks, trains, etc.) used to transport cargo), items or elements that are drivers of demand (i.e., demand factors 644) (including market factors, events, and many others); supply factors ( That is, supply factors 648) (including market factors, weather, availability of parts and materials, and many other factors); logistics factors 750 (availability of transportation routes, weather, fuel prices, regulatory factors, availability of space (in vehicles, in containers, in packages, in warehouses, fulfillment centers, on shelves, etc.), and many other factors); retailers 664 (including online retailers 730, e-commerce sites 730, etc.); transportation routes (waterways 732, roads 734, airways, railroads 738, etc.); robotic systems 744 (mobile robots, cobots, robotic systems for human work,Robotic delivery systems, etc.), drones 748 (e.g., package delivery, site mapping, monitoring or inspection), autonomous vehicles 742 (e.g., package delivery), software platforms 752 (e.g., enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, IoT platforms, supply chain management platforms, platform-as-a-service platforms, infrastructure-as-a-service platforms, software-based data storage platforms, analytics platforms, artificial intelligence platforms, etc.), and many other products. In some embodiments, product 1510 is encompassed as intelligent product 1510, or VCNP 604 includes intelligent product 1510. Intelligent product 1510 can have a range of capabilities, including, but 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 other IoT capabilities. Intelligent product 1510 may include any type of information technology. The intelligent product 1510 may comprise a processor, computer random access memory, and a communication module. The intelligent product 1510 may be a passive intelligent product, similar to RFID-type data structures that the intelligent product can ping or read. The product 1510 may be considered a value chain network entity (e.g., under the control of the platform) that is made intelligent by adding surrounding infrastructure and RFID, allowing data to be read from the intelligent product 1510. The intelligent product 1510 may also be connected to sensors, IoT devices, tags,Or it may fit into a value chain network, with connectivity built around the intelligent product 1510 through other components.

[0241] In an embodiment, the monitoring system layer 614 has functions such as monitoring all or some of the value chain entities 652 in the value chain network 668, exchanging data with the value chain entities 652, providing control commands to any of the value chain entities 652, and receiving control commands, which are achieved through the various functions of the data processing layer 608 described herein. Network characteristics of value chain network entities

[0242] Referring to FIG. 9 , the orchestration of a set of deeply interconnected value chain network entities 652 within a value chain network 668 by a value chain network management platform 604 is illustrated. Each value chain network entity 652 may have connections to a VCNP 604, to a set of other value chain network entities 652 (local network connections, peer-to-peer connections, mobile network connections, connections via the cloud, or other connections), and / or to other value chain network entities 652 via the VCNP 604. The value chain network management platform 604 manages the connections, configures or provisions resources to enable the connections, and / or manages applications 630 that leverage the connections (e.g., by providing information from one set of entities 652 to applications 630 related to another set of entities 652, by coordinating the activities of the entity sets 652, by providing input to artificial intelligence systems related to the VCNP 604 or the entity sets 652, by interacting with edge computing systems deployed in the entities 652 and their environments, etc.).

[0243] Entities 652 are external entities, and VCNP 604 may interact with these entities 652. VCNP 604 may function as a control tower and establish oversight (e.g., establishing common oversight across multiple entities 652). A unified platform may have an interface where users can view various items, such as user destinations, ports, air and rail assets, and orders. A next step may be to establish a common data schema that enables services to operate within any of these applications. This involves extracting data that flows through or is related to these entities 652 and integrating it into a framework that allows supply chain and demand-side applications to interact with entities 652. This involves IoT systems and other external data sources feeding into the monitoring layer via a shared data pipeline and storing it in a common data schema in the storage layer, which then trains intelligence to identify impacts across these entities 652. For example, if a supplier goes bankrupt or is determined to be bankrupt, VCNP 604 may trigger an automatic transmission of a replacement smart contract to a secondary supplier. Management of different aspects of the supply chain may take place. For example, if a supplier is identified as bankrupt (e.g., from a bankruptcy announcement), a response such as an immediate and automatic change in price can be taken on the demand side. Other similar examples may apply depending on events occurring in the automation layer enabled by VCNP604. At this VCNP604 interface layer, users can then use the digital twin to view all entities 652 that are not normally viewed together and monitor the status of each entity 652, including identifying problem states. For example, if a supplier's financial reports have three negative quarters, the reports can be flagged to monitor for future potential bankruptcy.

[0244] For example, an IoT system deployed at a fulfillment center 628 may collaborate with intelligent product 1510 to collect customer feedback about product 1510, and if application 630 for fulfillment center 628 receives feedback about an issue with product 1510 through connectivity, it may initiate a workflow to implement corrective action before similar products 650 of product 1510 are shipped from fulfillment center 628. Similarly, port infrastructure facilities 660 (e.g., yards storing shipping containers, etc.) through connectivity with floating assets 620 (e.g., ships, barges, etc.) may notify floating assets 620 that the port is approaching capacity, thereby triggering a negotiation process (which may include automated negotiations based on a set of rules and governed by smart contracts) that allows some assets 620 to be reallocated to alternative ports or storage facilities. These and other connections between value chain network entities 652 (such as one-to-one connections, one-to-many connections, many-to-many connections, or connections between defined groups of entities 652 managed by the same owner or operator) are included herein as applications 630 managed by VCNP 604. Value chain network activities and platform-managed applications

[0245] Referring to FIG. 10, the set of applications 614 provided on, integrated with, or managed by, for, or associated with the VCNP 604, including the set of value chain network entities 652, may include one or more applications, including, but not limited to, the following types of applications: supply chain management applications 21004 (e.g., those used to manage the timing, quantity, logistics, transportation, delivery, and other details regarding orders for goods, parts, and other items); asset management applications 814 (e.g., those used to manage floating assets (ships, boats, etc.)); assets); financial applications 822 (e.g., processing financial matters relating to value chain entities and assets, including, but not limited to, payments, collateral, bonds, duties, customs fees, taxes, and other matters);6 (e.g., shipments, goods, assets, individuals, 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, personal injury, property damage, business loss, breach of contract, etc.), demand management applications 824 (e.g., including, but not limited to, applications that analyze, plan, or drive demand for value chain products or services), applications that analyze, plan, or drive customer demand for a facility that provides value chain products or services or for product categories that can be supplied using that facility (e.g., demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, marketing analysis applications, customer relationship management applications, search engine optimization applications). 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 other applications (including applications that use or enable one or more features of the intelligent product 1510 or that run using the intelligent features of the intelligent product 1510); trading applications 858 (such as, without limitation, buying applications, selling applications, bidding applications, auction applications, reverse auction applications, bid / offer matching applications, analytics applications that analyze value chain performance, yield, return on investment, or other metrics, or other applications);tax applications 850 (e.g., for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, or other factors related to taxes, duties, levies, levies, tariffs, customs duties, credits, fees, or other government-imposed charges (e.g., customs duties, value-added taxes, sales taxes, income taxes, property taxes, local taxes, pollution taxes, renewable energy credits, pollution control credits, import duties, export duties, and the like); identity management applications 830 (e.g., for managing one or more identities of entities 652 involved in the value chain, including, but not limited to, identity verification applications, biometric 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, or other fraud detection applications);Inventory management applications 820 (e.g., inventory management at a fulfillment center, distribution center, warehouse, storage facility, store, port, ship, other floating asset, or other location), security applications, solutions, or services 834 (collectively referred to herein as security applications, and may include, for example, any of the identity management applications 830 listed above), and physical security systems (e.g., access control systems (using biometric access control, fingerprint authentication, retinal scans, passwords, or other access controls), safes, vaults, cages, secure rooms, secure storage facilities, or the like), surveillance systems (using cameras, motion sensors, infrared sensors, or other sensors), perimeter security systems, floating security systems for floating assets, cybersecurity systems (e.g., virus detection and response, intrusion detection and response, spam detection and response, phishing detection, other security applications, such as detection and response, social engineering detection and response, cyber-attack detection and response, packet inspection, traffic inspection, DNS attack response and detection); safety applications 840 (including, for example, applications for detecting, characterizing, or predicting the probability and / or extent of an accident or other damage, including improving employee safety, reducing the probability of property damage, reducing accident risk, reducing the probability of cargo (e.g., freight) damage, and managing risk related to insured goods, loan collateral, or similar items); including safety management based on the data sources, events, or entities described in this disclosure or the documents incorporated herein; blockchain applications 844 (e.g., a distributed ledger or other blockchain-based application that records a series of transactions (e.g., debits or credits, purchases or sales, physical exchanges, smart contract events, etc.);Facility Management Applications 850 (e.g., the management of infrastructure, buildings, systems, real estate, personal property, or other property that supports the value chain (e.g., shipyards, ports, distribution centers, warehouses, piers, stores, fulfillment centers, storage facilities, etc.), or the design, management, or control of systems or facilities in or around the property (e.g., information technology systems, robotic / autonomous vehicle systems, packaging systems, packing systems, picking systems, inventory tracking systems, inspection systems, routing systems for mobile robots, workflow systems for human assets, etc.); Regulatory Filings 852 (e.g., applications to regulate any of the applications, services, transactions, activities, workflows, events, entities, or other items described in this Agreement and the documents incorporated herein, for example, regulations regarding permitted routes, permitted cargo and goods, authorized parties to transactions, required disclosures, privacy, value commercial applications, solutions, or services 854 (including, for example, but not limited to, e-commerce site marketplaces, online sites, auction sites or marketplaces, physical goods marketplaces, advertising marketplaces, reverse auction marketplaces, advertising networks, or other marketplaces); vendor management applications 832 (including, for example, but not limited to, applications that manage vendors or potential vendors, or manage the procurement of goods, components, or materials supplied in the value chain, including, for example, vendor qualification, vendor evaluation, requests for proposals, requests for information, bonds or other performance guarantees, contract management, etc.);analytics applications 838 (e.g., including, but not limited to, analytics applications relating to data types, applications, events, workflows, or entities mentioned in this disclosure or the documents incorporated herein, including, but not limited to, big data applications, user behavior analysis applications, forecasting applications, classification applications, dashboards, pattern recognition applications, econometrics applications, financial revenue applications, return on investment applications, scenario planning applications, decision support applications, demand forecasting applications, demand planning applications, route planning applications, weather forecasting applications, and other applications);Pricing applications 842 (e.g., pricing of goods, services (including those described in this disclosure or the documents incorporated herein) and smart contract applications, solutions, or services (collectively referred to herein as "smart contract applications 848"), including, for example, any of the types of smart contracts mentioned herein or in the documents incorporated herein, such as smart contracts for the sale of goods, smart contracts for ordering goods, smart contracts for transportation resources, smart contracts for labor, etc.) Smart contracts include, but are not limited to, smart contracts for the delivery of goods, smart contracts for the placement of goods, smart contracts for tokens or crypto assets, smart contracts that grant rights, options, futures, or benefits based on future conditions, smart contracts covering securities, commodities, futures, options, derivatives, or the like, smart contracts covering current or future resources, smart contracts configured to handle or address tax, regulatory, or compliance parameters, smart contracts configured to execute arbitrage trades, and many others. Thus, the value chain management platform 604 may host a variety of applications 630 (e.g., other value chain applications, services, solutions, etc., including those mentioned above) to enable interactions between them, with shared microservices, shared data infrastructure, and shared intelligence enabling improved performance of any pair or larger combination or permutation of such services compared to isolated implementations of the same type of application.

[0246] Referring to FIG. 10 , the set of applications 614 including the set of value chain network entities 652 provided on, integrated with, or managed by, or for, or associated with the VCNP 604 may further include, but are not limited to, the following: a payment application 860 (e.g., for managing situational factors (applicable taxes, duties, etc.), transferring funds, settling payments to parties, etc.), corresponding to any of the applications 630 described herein); a process management application 862 (e.g., for managing processes or workflows described herein (e.g., supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, ordering processes, navigation processes, and other processes); a compatibility testing application 864 (e.g., for assessing compatibility between value chain network entities 652 or activities participating in processes, workflows, activities, or other applications 630 described herein).For example, compatibility of a product 1510 with a container or package, compatibility of a product 1510 with a customer requirement set, compatibility of a product 1510 with another product 1510 (e.g., when one is a replenishment, resupply, replacement part, etc., of the other), compatibility between infrastructure and equipment entities 652 (e.g., between a container ship or barge and a port or waterway, between a container and a storage facility, between a truck and a road, between a drone or robot and a package, between a drone, AV, or robot and a delivery destination, and many other cases); infrastructure test application 802 (e.g., compatibility of a product 1510 or an application 630 (e.g., storage capabilities, lifting capabilities, movement capabilities, storage capacity, network capabilities, environmental control capabilities, software capabilities, security capabilities, and other cases) and / or an incident management application 910 (for managing events, accidents, and other incidents that occur in one or more environments, including the value chain network entities 652, such as vehicle accidents, worker injuries, outages, property damage incidents, product damage incidents, product liability incidents, regulatory non-compliance incidents, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicular traffic, maritime traffic, human worker traffic, and combinations thereof), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, abuse incidents, and many others).

[0247] 10 , the set of applications 614 comprising the set of value chain network entities 652 provided on, integrated with, or managed by, or for, or associated with the VCNP 604 further includes, but is not limited to, the following: predictive maintenance applications 910 (e.g., for taking action to predict and manage breakdowns, failures, outages, damage, required maintenance, required repairs, required service, required support, or the like, of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, etc.); logistics applications 912 (e.g., for picking, delivering, transferring loads to transportation facilities, loading, unloading, packing, picking, shipping, driving, and transporting products 650 or other items to various intermediate locations between origins and destinations); activities including scheduling and managing movement through points; reverse logistics applications 914 (e.g., managing the logistics of product 650 returns, waste, damaged goods, or other items transferable via return logistics routes); waste reduction applications 920 (e.g., reducing packaging waste, solid waste, energy waste, liquid waste, pollution, contaminants, computational resource waste, human resource waste, or other waste associated with a value chain network entity 652 or activity); augmented reality, mixed reality, and / or virtual reality applications 930 (e.g., applications to visualize one or more value chain network entities 652 or activities involved in any one or more of the applications 630, such as the movement of a product 1510, the interior of a facility, the condition or status of goods, one or more environmental conditions, weather conditions, the packaging configuration of a container or set of containers, or other);a demand forecasting application 940 (e.g., an application that forecasts demand for a product 1510, a product category, a potential product, and / or factors related to demand (e.g., market factors, asset factors, demographic factors, weather factors, economic factors, etc.)) that forecasts demand for a product 1510, a product category, a potential product, and / or factors related to demand (e.g., market factors, asset factors, demographic factors, weather factors, economic factors, etc.); a demand aggregation application 942 (e.g., an application that collects and aggregates orders and / or commitments (optionally embodied in one or more contracts (including smart contracts)) regarding a product 1510, a category, or otherwise); applications integrating smart contracts (e.g., embodied in smart products 1510 and including smart contracts) (e.g., including current demand for existing products and future demand for products that are not yet available); customer profiling applications 944 (e.g., applications for profiling one or more customers' demographic, psychographic, behavioral, economic, geographic, or other attributes, including based on past purchase data, loyalty program data, behavior tracking data (including data collected as customers interact with smart products 1510), online clickstream data, interactions with intelligent agents, and other data sources); and / or component supply applications 948 (e.g., applications for managing the supply chain of components for a set of products 650);

[0248] 10 , the set of applications 614 provided on, integrated with, or managed by, or for, the VCNP 604, or associated with the VCNP 604, including the set of value chain network entities 652, may further include, but are not limited to: policy management applications 868 (e.g., for deploying one or more policies, rules, or the like for governance of one or more value chain network entities 652 or applications 630, such as for managing workflow execution (including configuring policies for each workflow on the platform 604), for managing regulatory compliance (including maritime, food and drug, healthcare, environmental, health, safety, tax, financial reporting, commercial, and other regulations), resources (connectivity, compute, human resources, energy, and other resource management), and the like. the allocation of resources, managing compliance with corporate policies, managing compliance with contracts (including smart contracts); the platform 604 may automatically deploy governance functions for associated entities 652 and applications 630 via connectivity functionality 642; managing interactions with other entities (including policies regarding information sharing and access to resources); managing data access (including privacy data, operational data, status data, and other data types); managing security access to infrastructure, products, facilities, locations, or similar assets, and many other functions; a product configuration application 870 (for product managers and / or automated product configuration processes (optionally using robotic process automation) to determine the configuration of a product 1510).This includes the ability to change configurations in real time during agile manufacturing, or the ability to configure or customize while in transit (e.g., 3D printing one or more features or elements), or the ability to configure or customize remotely (e.g., firmware download), configuration of field programmable gate arrays, software installation, etc.), warehouse management and fulfillment applications 872 (applications that enable the management of warehouses, distribution centers, fulfillment centers, etc., including product selection, configuration of product storage locations, determining movement routes for personnel, mobile robots, etc. within the facility, determining picking and packing schedules, routes, and workflows, and operations management for robots, drones, conveyors, etc., and other products within the facility). move schedules, including many functions such as determining schedules for moving products to loading docks, etc.); kit configuration and deployment applications 874 (applications that allow users of the VCNP to configure kits, boxes, or pre-integrated, pre-provisioned, and / or pre-configured systems and enable customers or workers to quickly deploy portions of the VCNP 604's functionality for specific value chain network entities 652 and / or applications 630); and / or product testing applications 878 (applications that perform testing of products 1510 (including performance testing, feature activation, safety, policy or regulatory compliance, quality, quality of service, probability of failure, and many other factors).

[0249] Referring to FIG. 10 , the set of applications 614 provided on, integrated with, or managed by or for the VCNP 604, or comprising 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, e.g., container ships, barges, boats, etc., and related infrastructure facilities (e.g., docks, cranes, port facilities, etc.), determining optimal routes for fleet assets based on weather, market, traffic, etc. conditions, ensuring compliance with policies and regulations, ensuring safety, improving environmental factors, and enhancing financial metrics, among other purposes); a transportation management application 882 (for managing transportation assets (e.g., trucks, trains, aircraft, etc.), including for optimizing financial returns, improving safety, reducing energy consumption, mitigating delays, mitigating environmental impacts, among other purposes); and an opportunity matching application 884 (for matching demand and supply factors, matching the needs and capabilities of value chain network entities 652, and vice versa). identifying logistics opportunities, identifying input opportunities for enhanced analytics, artificial intelligence, and / or automation, identifying cost reduction opportunities, identifying profit and / or arbitrage opportunities, and many other purposes; workforce management applications 888 (including fulfillment centers, ships, ports, warehouses, distribution centers, enterprise management locations, retail stores, online / ecommerce site management facilities, ports, ships, boats, barges, trains, depots, and other facilities referenced in this disclosure), shipping and distribution applications 890 (for planning, scheduling, routing, and otherwise managing the shipping and distribution of products 650 and other items), and / or enterprise resource planning (ERP) applications 892 (for planning enterprise resource utilization, including human resources, financial resources, energy resources, physical assets, digital assets, and other resources). Core functions and interactions of the data processing layer (adaptive intelligence, monitoring, data storage, and applications)

[0250] Referring to FIG. 11 , a high-level schematic diagram of an example implementation of a value chain network management platform 604 illustrates a configuration of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements working in concert to enable the intelligent management of a set of value chain entities 652. These value chain entities occur, operate, transact within the platform 604, or enable one or more value chain network processes, workflows, activities, events, and / or applications 630 that occur, operate, transact within, or that own, operate, support, or enable, or that execute one or more value chain network processes, workflows, activities, events, and / or applications 630 that are executed by the platform 604 in connection with products 1510 (including finished goods, software products, hardware products, component products, materials, equipment, consumer packaged goods, consumer products, food products, beverage products, household products, business supply products, consumables, pharmaceutical products, medical device products, technology products, entertainment products, or any other type of product or related service) in connection with the platform 604. Value chain entities 652 are entities that participate in a broad range of value chain activities that participate in or are related to value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, transportation activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, etc.), and include systems, devices, machines, components, equipment, facilities, individuals, or other entities that participate in the value chain network processes, workflows, activities, events, applications 630 described in this disclosure or the documents incorporated by reference herein.

[0251] In an embodiment, the value chain network management platform 604 can include a set of data processing layers 608, each configured to provide automation, machine learning, artificial intelligence applications, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, process management, and many other functions. These functions correspond to various value chain network applications and end uses. In an embodiment, the data processing layers 608 can include a value chain network monitoring system layer 614, a value chain network entity-oriented data storage system layer 624 (referred to in some parts of this specification simply as the data storage layer 624 for convenience), an adaptive intelligent system layer 614, and the value chain network management platform 604. The value chain network management platform 604 is configured to include a data processing layer 608, which can provide management of the value chain network management platform 604 and / or other layers (e.g., a value chain network monitoring system layer 614, a value chain network entity-oriented data storage system layer 624 (e.g., data storage layer 624), and an adaptive intelligent system layer 614). Each data processing layer 608 can include a variety of elements, including services, programs, applications, workflows, systems, components, and modules, as described in further detail herein and in documents incorporated herein. In embodiments, each data processing layer 608 (and optionally the platform 604 as a whole) is configured such that one or more of its elements are accessible as services by other layers 624 or other systems (e.g., configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture).For example, the platform 604 may configure (or configure and / or provision) the data processing layer 608 to have network connections (including various configurations, types, and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, microservices, SaaS interfaces, PaaS interfaces, IaaS interfaces, cloud functions, or the like, that allow data or information to flow between the data processing layer 608 and other layers, systems, subsystems, or other systems of the platform 604 (e.g., value chain entities 652 or cloud-based or on-premise enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems, etc. Each data processing layer 608 can include a set of services (e.g., microservices) for data processing, including the following functions: data extraction, transformation, and loading functions; data cleaning and deduplication functions; data normalization functions; data synchronization functions; data security functions; computational functions (e.g., performing predefined computational operations on data streams and providing output streams); compression and decompression functions; analytical functions (e.g., providing automatic generation of data visualizations), etc.

[0252] In an embodiment, each data processing layer 608 has a set of application programming connectivity functions 642 that automate data exchange with other data processing layers 608. This includes data integration functions (e.g., functions that extract, transform, load, normalize, compress, decompress, encode, decode, or otherwise process data packets, signals, or other information) to process data packets, signals, or other information exchanged between layers and / or applications 630. This also includes functions to convert data from one format or protocol to another format or protocol, as needed, so that one layer can consume the output of another layer. In an embodiment, the data processing layers 608 are configured by the value chain monitoring system layer 614 in a topology that facilitates the collection and distribution of data among multiple applications and uses within the platform 604. The value chain monitoring system layer 614 may include, integrate with, and / or interface with various data collection and management systems 640 (sometimes referred to as data collection systems 640 for convenience) for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about various data layers 624 or their services or components. For example, streams of physiological data from wearable devices worn by workers performing tasks or consumers engaging in activities may be distributed via the monitoring system layer 614 to multiple different applications within the value chain management platform 604, such as applications that monitor the worker's physiological, psychological, performance level, attention, or other state, and applications that promote operational efficiency and / or effectiveness. In an embodiment, the monitoring system layer 614 facilitates synchronization (time synchronization), normalization, or similar adjustment of data collected regarding one or more value chain network entities 652.For example, one or more video streams or other sensor data collected from a set of camera-equipped IoT devices, such as from workers 718 or other entities within a value chain network facility or environment, can be synchronized to a common clock. This allows the relative timing of the video or other data to be understood by a video processing system (e.g., a machine learning system that processes images within a video, changes in images between different frames, or similar data). In such an example, the monitoring system layer 614 can further synchronize the video, camera images, sensor data, or similar data with data streams from wearable devices, data streams generated by value chain network systems (such as ships, elevators, vehicles, containers, cargo handling systems, packaging systems, delivery systems, drones / robots), data streams collected by mobile data collectors, or similar data. Configuring the monitoring system layer 614 as a common platform or set of microservices accessible across multiple applications can significantly reduce the number of interconnections required as an owner or other operator within a value chain network expands the set of applications they monitor to accommodate the growing number of IoT devices and other systems and devices under their control.

[0253] In an embodiment, the data processing layer 608 is configured in a topology that enables shared or common data storage among multiple applications and uses of the platform 604 through a value chain network-oriented data storage system layer 624 (hereinafter referred to as data storage layer 624 or storage layer 624 for convenience). For example, various data related to the value chain entities 652 and data generated by other data processing layer 608 services are stored in the data storage layer 624. This allows various services, applications, programs, etc. in the data processing layer 608 to access a common data source, which may include a single logical data source distributed across multiple physical and / or virtual storage locations. This dramatically reduces the amount of data storage required to process the vast amounts of data generated by the value chain network entities 652 as the use of applications 630 and the value chain network expands and becomes more widespread. For example, a supply chain management or inventory management application within the value chain management platform 604 (e.g., one that orders replacement parts for machinery or equipment) may have access to the same data set (information about parts that have been replaced on a particular fleet of machinery) as a predictive maintenance application that forecasts the likelihood that parts on a ship or parts on a port facility will need to be replaced. Similarly, predictions may be used regarding the restocking of items.

[0254] In an embodiment, the value chain network data objects 1004 may be provided according to an object-oriented data model that defines the classes, objects, attributes, parameters, and other characteristics of a set of data objects (e.g., those associated with the value chain network entities 652 and applications 630) processed by the platform 604.

[0255] In some embodiments, the data storage system layer 624 provides an extremely rich environment for collecting data usable as feature extraction or input for intelligent systems, such as expert systems, analytics systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems expressly disclosed in this disclosure and the documents incorporated herein. As a result, each application 630 in the platform 604 and each adaptive intelligent system in the adaptive intelligent systems layer 614 can benefit from data collected or generated by each of the other systems. In some embodiments, the data storage system layer 624 may facilitate the collection of data usable as feature extraction or input for intelligent systems, such as development frameworks from artificial intelligence. In some examples, data collection may involve collecting and / or storing natural archives or ad-hoc event logs as needed, conducting periodic checks of on-board diagnostic data, etc. In some examples, feature precomputation is deployed using cloud-based, on-demand compute capabilities (e.g., precomputation, signal multiplexing), such as AWS Lambda. In many examples, pairings of similar types of value chain entities (e.g., double, triple, quad, etc.) use one or more feature sets in the data processing layer 608 to deploy connectivity and services between the value chain entities and the applications they use. This applies even when collecting hundreds of types of data from relatively diverse entities. In these examples, pairings of similar types of value chain entities may leverage some or all of the connectivity and services between the value chain entities and applications and send information resulting from the connected data pairings to artificial intelligence services, including various neural networks as disclosed herein, or hybrid combinations thereof. In these examples, genetic programming techniques may be deployed to prune some of the input features of the information resulting from the connected data pairings.In these examples, genetic programming techniques may also be deployed to add and extend input features of the information derived from the connected data pairing. These genetic programming techniques may be shown to improve the effectiveness of decisions established by artificial intelligence services. In these examples, the information derived from the connected data pairing may be transitioned to other layers of the platform (including robotic process automation, forecasting, prediction, and other resource assistance or deployment) to enable shared data schemas to function as features and resources of platform 604.

[0256] The storage layer 624 stores a wide range of data types using a variety of storage media, data storage types, data architectures 1002, and formats.This includes asset and facility data 1030, state data 1140 (which indicates the state, condition status, or other indicators of any of the value chain network entities 652, applications 630 or their components or workflows, or 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 (e.g., data relating to a wide range of events, including operational data, transactional data, workflow data, maintenance data, or data involving or relating to events occurring within the value chain network 668 or in one or more of the applications 630, process events, financial events, transactional events, output events, input events, state change events, operational events, etc.) , workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, recharging events, shipping events, warehousing events, goods movements, border crossings, freight movements, inspection events, supply events, and many other events; claims data 664 (e.g., business interruption insurance, product liability insurance, goods, facilities, or equipment insurance, flood insurance, insurance for contract-related risks, etc.), product liability, general liability, workers' compensation, casualty and other liability claims data, and contract claims data (e.g., supply contract performance claims, product delivery requirements, warranty claims, indemnity claims, delivery requirements, timing requirements, milestones, key performance indicators, etc.), accounting data 730 (e.g., data related to completion of contract requirements, warranty fulfillment, payment of duties and taxes, etc.), and risk management data 732 (including data related to supplied items, quantities, prices, deliveries, supplier, transportation routes, tariff information, etc.).

[0257] In an embodiment, data processing tier 608 is configured in a topology that facilitates shared adaptive functions, which may be provided, managed, or mediated by a set of services, components, programs, systems, or functions in adaptive intelligent systems tier 614 (referred to in some parts of this specification as adaptive intelligence tier 614 for convenience). Adaptive intelligent systems tier 614 includes a set of data processing, artificial intelligence, and computation systems 634, which are described in detail herein. Thus, computational resources (e.g., available processing cores, available servers, available edge computing resources, available in-device resources for a single device or peered network, available cloud infrastructure, etc.), data storage resources (e.g., local storage within a device, storage resources within a value chain entity or environment (e.g., in-device storage, storage on an asset tag, local area network storage, etc.), network storage resources, cloud-based storage resources, database resources, etc.), network resources (e.g., cellular network spectrum, wireless network resources, fixed network resources, etc.), and energy resources (e.g., available battery power, available renewable energy, fuel, grid-based power, etc.) may be optimized in a collaborative or shared manner beside an operator, enterprise, or similar entity (e.g., for the benefit of multiple applications, programs, workflows, etc.). For example, the adaptive intelligence layer 614 manages and provisions available network resources for both supply chain management applications and demand planning applications (among many other possibilities). This allows low-latency resources to be used for supply chain management applications (where fast decision-making is important) and high-latency resources to be used for demand planning applications.As described in detail in this disclosure and in documents incorporated herein, various services and functions across each tier 624 are provided with a variety of adaptations, such as application requirements, quality of service, on-time delivery, service goals, budgets, costs, prices, risk factors, operational goals, efficiency goals, optimization parameters, return on investment, profit margins, uptime / downtime rates, and worker utilization rates.

[0258] The value chain management platform 604, referred to herein for convenience as platform 604, encompasses, integrates, and enables the value chain network processes, workflows, activities, events, and applications 630 described throughout this specification, allowing operators to manage multiple aspects of a value chain network environment or entity 652 in a common application environment (e.g., shared, pooled, similar licenses apply whether shared data is single-person, multi-person, or anonymized), leveraging common data storage in data storage layer 624, common data collection or monitoring in monitoring system layer 614, and / or common adaptive intelligence in adaptive intelligence layer 614. Output from applications 630 within platform 604 is provided to other data handling layers 624. This includes, but is not limited to, state and status information for various objects, entities, processes, flows, etc.; object information (identifier, attribute, and parameter information for various classes of objects of various data types); event and change information (including information about workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information); outcome information (indications of success or failure, indications of completion of a process or milestone, indications of correct or incorrect predictions, information indicating correct or incorrect labeling or classification, and success metrics (including those related to yield, engagement, return on investment, profit margins, efficiency, timing, service quality, product quality, customer satisfaction, etc.). Output from each application 630 is stored in the data storage layer 624, distributed for processing by the data collection layer 614, and consumed by the adaptive intelligence layer 614. The cross-application nature of the platform 604 enables the convenient organization of the necessary infrastructure elements to add intelligence to any application.For example, it may provide machine learning based on results between applications, and based on results from other applications or other elements of the platform 604 to enhance automation of a particular application, allowing application developers to focus on application-specific processes while leveraging other capabilities of the platform 604. As an example, there may be systems, components, services, or other functionality that control, automate, or optimize one or more performance characteristics of a value chain network entity 652, or generally improve the outputs and results 1040 of processes and applications pursued through use of the platform 604. In some examples, the outputs and results 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or similar functions aimed at automating some steps in a process. Data Storage Layer Deep Dive - Alternative Data Architectures

[0259] 12 , additional details, components, subsystems, and other elements of optional embodiment data storage layer 624 of platform 604 are shown. A variety of data architectures are available, including traditional relational and object-oriented data architectures, blockchain architectures 1180, asset tag data storage architectures 1178, local storage architectures 1190, network storage architectures 1174, multi-tenant architectures 1132, distributed data architectures 1002, value chain network (VCN) data object architectures 1004, cluster-based architectures 1128, event database architectures 1034, state database architectures 1140, graph database architectures 1124, self-organizing architectures 1134, and other data architectures 1002.

[0260] The adaptive intelligent systems layer 614 of the platform 604 enables data storage, retrieval access, query management, loading, extraction, normalization, and / or transformation, enabling the use of a variety of other data storage architectures 1002 (e.g., extracting from one format of database and loading into a data system that uses a different protocol or data structure).

[0261] In an embodiment, the value chain network-oriented data storage systems layer 624 includes, 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), and many other systems.

[0262] In an embodiment, the storage layer 624 can store data in one or more knowledge graphs (e.g., directed acyclic graphs, data maps, data hierarchies, data clusters with links and nodes, self-organizing maps, or the like) in a graph database architecture 1124. In an exemplary embodiment, a knowledge graph is a common example of how graph databases and graph database architectures are used. In some examples, a knowledge graph is used to graph a workflow. For linear workflows, a directed acyclic graph is used. For conditional workflows, a cyclic graph is used. A graph database (e.g., graph database architecture 1124) includes a knowledge graph, or a knowledge graph is an example of a graph database. In an exemplary embodiment, a knowledge graph includes ontologies and connections (e.g., relationships) between the ontologies in the knowledge graph. For example, a knowledge graph can be used to capture a representation of a human expert's knowledge domain to identify opportunities for designing and building robotic process automation or other intelligence that can replicate this knowledge set. The platform may be used to recognize expert types that combine the fact-based knowledge base obtained from the knowledge graph with skills that can be replicated by artificial intelligence, which may vary depending on the area of ​​expertise. For example, artificial intelligence such as convolutional neural networks may be used to combine spatial and temporal components to diagnose problems or pack boxes in a warehouse. Meanwhile, a platform may use a different type of knowledge graph for a self-organizing map of experts whose primary task is to categorize customers into customer segmentation groups. In some examples, the knowledge graph is built from diverse data, such as job qualifications, job postings, and analysis of output deliverables. In some examples, the data storage layer 624 stores data in a digital thread, ledger, or similar format to maintain a serial or other record of entities 652 over time, which may include any of the entities described herein. In some examples, the data storage layer 624 uses or enables the use of asset tags 1178.Asset tags 1178 include data structures associated with assets and accessible and manageable through access controls, etc., allowing data storage and retrieval to be linked to local processes while also being open to remote retrieval and storage options. In embodiments, storage layer 624 can include one or more blockchains 1180 containing identity data, transaction data, historical interaction data, etc. These blockchains can have access control based on credentials associated with value chain entities 652, services, or one or more applications 630. Data stored in data storage system 624 can include accounting and other financial data 730, access data 734, asset and facility data 1030 (including related to assets and facilities in the value chain described herein), asset tag data 1178, worker data 1032, event data 1034, risk management data 732, pricing data 738, safety data 664, and other types of data associated with, generated by, or generated about value chain entities and activities described herein and in the documents incorporated by reference. Adaptive Intelligent Systems and Monitoring Layer

[0263] 13 , which illustrates additional details, components, subsystems, and other elements of optional implementations of platform 604. Management platform 604, in various optional embodiments, may include a set of applications 614 that enable an operator or owner of a value chain network entity, or other user, to manage, monitor, control, analyze, or otherwise interact with one or more elements of value chain network entity 652 (including the elements mentioned above and throughout this specification).

[0264] In an embodiment, the adaptive intelligent systems layer 614 includes a collection of systems, components, services, and other capabilities that facilitate the collaborative development and deployment of intelligent systems. This includes systems that enhance one or more functions of an application 630 in the application platform 604; systems that improve the performance (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors) of one or more components of the connectivity facility 642; systems that improve other functions within the adaptive intelligent systems layer 614; systems that improve other functions within the adaptive intelligent systems layer 614; systems that improve other functions within the adaptive intelligent systems layer 614; systems that improve other functions within the adaptive intelligent systems layer 614; systems that improve other functions within the adaptive intelligent systems layer 614 (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors); systems that improve other functions within the adaptive intelligent systems layer 614; or systems that optimize one or more components of the value chain network-oriented data storage system 624 or improve its overall performance (e.g., speed / latency, energy utilization efficiency, storage capacity, storage efficiency, reliability, security, or similar characteristics); or systems that generally improve the output and results 1040 of processes and applications pursued through use of the platform 604.

[0265] These adaptive intelligent systems 614 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 self-adaptive system), an adaptive network system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, an entity interaction system 1920 (e.g., for establishing, provisioning, configuring, and managing a set of interactions between value chain network entities 652 within a value chain network 668), and other systems.

[0266] In an embodiment, the value chain monitoring system layer 614 and its data collection systems 640 can include a variety of systems for collecting data, including, but not limited to, real-time monitoring systems 1520 (e.g., on-board monitoring systems such as ships and other floating assets, delivery vehicles, trucks and other transportation assets, event and status reporting systems in shipyards, ports, warehouses, distribution centers, and other locations; on-board diagnostic (OBD) and telematics systems in floating assets, vehicles, and equipment; systems that provide diagnostic codes and events via event buses, communication ports, or other communication systems; monitoring infrastructure (e.g., cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, people tracking systems, and other systems installed within the various environments where value chain activities and other events occur); tracking systems, environmental sensing systems, etc.), and removable and replaceable monitoring systems (such as portable data collectors, RFID readers, other tag readers, smartphones, tablets, and other mobile devices with data collection capabilities); software interaction observation systems 1500 (systems that observe a variety of software interactions, including recording and tracking events related to user interactions with software user interfaces (e.g., mouse actions, touchpad actions, mouse clicks, cursor movements, keyboard actions, navigation actions, eye movements, finger movements, gestures, menu selections, etc.), as well as interactions with other programs via APIs);Mobile data collectors 1170 (including those described in detail herein and in documents incorporated by reference), visual monitoring systems 1930 (including systems using video and still imaging systems, LIDAR, IR, and other systems to visualize items, people, materials, components, machinery, equipment, personnel, gestures, facial expressions, position, location, configuration, and other factors or parameters of entities 652, and inspection systems to monitor process, worker activity, etc.), interaction point systems 1530 (dashboards, user interfaces, control systems for value chain entities, etc.), physical process monitoring systems 1510 (to track the physical activity of operators, workers, customers, or others, and to monitor the physical activity of individuals (e.g., shippers, delivery workers, packers, etc.), physical interactions between workers, workers, and physical entities such as machines and equipment, and interactions between physical entities (e.g., packagers, pickers, assemblers, customers, merchants, vendors, distributors, etc.); video and still cameras, motion sensing systems (including optical, LIDAR, IR, and other sensor suites), robotic motion tracking systems (e.g., those that track the motion of systems worn by humans or physical entities), and other systems; machine condition monitoring systems 1940 (including on-board and external monitors that monitor the condition, state, operating parameters, or other status indicators of machines or their parts (e.g., machines, clients, servers, cloud resources, control systems, display screens, sensors, cameras, vehicles, robots, and other machines);Sensors and Cameras 1950 and Other IoT Data Collection Systems 1172 (including sensors, sensors or other data collection devices (including click-tracking sensors) installed in or around a value chain environment, such as where goods are shipped from, loading and unloading docks, vehicles or floating assets transporting goods, containers, ports, distribution centers, storage facilities, warehouses, delivery vehicles, and where goods are arriving) including cameras that monitor the entire environment, cameras dedicated to a specific machine, process, worker, or similar subject, wearable cameras, portable cameras, cameras mounted on mobile robots, cameras mounted on portable devices such as smartphones and tablets, and many other cameras (including many sensor types disclosed herein or in documents incorporated by reference herein); Indoor Location Monitoring Systems 1532 (including cameras, infrared systems, motion detection systems, beacons, RFID readers, smart lighting systems, and three-dimensional (3D) cameras) user feedback systems 1534 (including survey systems, touchpads, voice-based feedback systems, rating systems, facial expression monitoring systems, emotion monitoring systems, gesture monitoring systems, and other systems); behavior monitoring systems 1538 (including systems that monitor movement, shopping behavior, purchasing behavior, clicking behavior, behavior indicative of fraud or deception, user interface interaction, product return behavior, behavior indicative of interest, attention, boredom, or similar behavior, behavior indicative of mood (e.g., fidgeting, not moving, moving closer, or changing posture), and other behaviors); and various Internet of Things (IoT) data collectors 1172, including those described herein and the references incorporated herein;

[0267] In an embodiment, the value chain monitoring system layer 614 and its data collection system 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652, including any of the entities described throughout this specification. This includes components or subsystems for searching for entities within the value chain network 668, including by device identifier, network location, geolocation (e.g., by geofencing), indoor location (e.g., by proximity to known resources (e.g., IoT-enabled devices and infrastructure, Wi-Fi routers, switches, etc.)), cellular location (e.g., by proximity to cellular towers), identity management system (e.g., where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise, by an identifier assigned and / or managed by the platform 604), etc. The entity discovery 1900 initiates handshakes between devices and initiates interactions that enable applications 630 and other functionality of the platform 604.

[0268] Referring to FIG. 14, a management platform for an information technology system (e.g., a product and / or service value chain management platform) is illustrated as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that includes a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide collaborative intelligence (e.g., artificial intelligence 1160, expert systems 3002, machine learning 3004, etc.) for a set of demand management applications 824 and a set of supply chain applications 812 for a category of goods 3010 (manufactured and sold through the value chain). The adaptive intelligence systems 614 can provide the artificial intelligence 1160 through a set of data processing, artificial intelligence, and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020, and one or more adaptive intelligence systems 614 can operate on or in concert with a set of value chain applications (e.g., demand management applications 824 and supply chain applications 812). The adaptive intelligence systems 614 can include artificial intelligence, including various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described in this disclosure and in the documents incorporated by reference.

[0269] In an embodiment, the user interface includes an interface for receiving inputs from and providing selected data sources of the value chain (e.g., data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) to the artificial intelligence system 1160, such as neural networks, artificial intelligence system 1160, or other adaptive intelligence systems 614 described herein and in documents incorporated by reference, to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of the commodity category 3010. In an example, the selected data sources of the value chain may be applied as inputs for classification or prediction, or as results related to the value chain, the commodity category 3010, etc.

[0270] In an example, providing collaborative intelligence may include providing artificial intelligence capabilities, such as artificial intelligence system 1160. The artificial intelligence system facilitates collaborative intelligence for the set of demand management applications 824 or the set of supply chain applications 812, or both, such as for a product category, by processing data present in any of the value chain data sources, such as value chain processes, bills of materials, shipping statements, delivery schedules, weather data, traffic data, product 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, product lifecycle management (PLM) systems, etc.

[0271] In an embodiment, user interface 3020 can provide access to artificial intelligence functions, applications, systems, etc. for the coordination of intelligence in value chain applications, particularly value chain applications for commodity category 3010. User interface 3020 is adaptable to obtain information about commodity category 3010 and configure user access to artificial intelligence functions responsive to that information, such that a user can navigate through the user interface to value chain applications (e.g., set of demand management applications 824 and supply chain applications 812) that contribute to goods / services in commodity category 3010. These functions contribute to goods / services belonging to commodity category 3010. User interface 3020 facilitates the provision of coordinated intelligence, including artificial intelligence functions that provide coordinated intelligence for specific operators and / or enterprises participating in the supply chain.

[0272] In an embodiment, the user interface 3020 can be configured to facilitate a user's selection and / or configuration of multiple artificial intelligence systems 1160 for use in the value chain. The user interface displays a set of demand management applications 824 and supply chain applications 812 as interconnected entities, each of which receives, processes, and outputs data and allows these outputs to be shared among the applications. The type of artificial intelligence system 1160 is indicated in the user interface 3020 in response to the set of connected applications or their data elements being displayed in the user interface, such as when a user places a pointer near the set of connected applications. In an embodiment, the user interface 3020 provides a set of functionality that facilitates access to the set of adaptive intelligence systems and facilitates the development and deployment of intelligence for at least one function selected from the list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

[0273] The adaptive intelligence system 614 can be configured with data processing, artificial intelligence, and computation systems 634 that can work in concert to provide collaborative intelligence, such as when the artificial intelligence systems 1160 act on or respond to data collected or generated by other systems in the adaptive intelligence system 614, such as data processing systems. In embodiments, providing collaborative intelligence can include operating a portion of a set of artificial intelligence systems 1160 that employ one or more types of neural networks as described herein and in documents incorporated by reference herein, to process any of the demand management application outputs and the supply chain application outputs to provide collaborative intelligence.

[0274] In an embodiment, providing collaborative intelligence for the set of demand management applications 824 includes causing at least one of the adaptive intelligence systems 614 (e.g., through user interface 3020, etc.) to provide collaborative intelligence for at least one demand management application selected from the list including 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, a product or service recommendation engine, and the like.

[0275] Similarly, providing collaborative intelligence for the set of supply chain applications 812 includes configuring at least one of the adaptive intelligence systems 614 for one or more supply chain applications selected from a list of supply chain applications (e.g., a product timing management application, a product quantity management application, a logistics management application, a shipping application, a delivery application, a product order management application, a parts order management application, etc.).

[0276] In an embodiment, the management platform 102 provides access through the user interface 3020 to a set of adaptive intelligence systems 614 that apply artificial intelligence to provide collaborative intelligence to a set of demand management applications 824 and supply chain applications 812. In such an embodiment, a user may seek to match supply and demand, such as while securing the profits of a value chain for a commodity category 3010. By providing access to artificial intelligence capabilities 1160, the management platform allows a user to focus on supply and demand applications while leveraging technological advantages such as expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, and deep learning systems.

[0277] In an embodiment, the management platform 102 provides, e.g., through a user interface 3020, a set of adaptive intelligence systems 614 that provide coordinated artificial intelligence 1160 to a set of demand management applications 824 and a set of supply chain applications 812 for a product category 3020, for example, by automatically determining relationships between the demand management applications and the supply chain applications based on the inputs the applications use, the results they produce, and the value chain outcomes. The artificial intelligence 1160 is coordinated by, e.g., a set of data processing, artificial intelligence, and computation systems 634 available through the adaptive intelligence systems 614.

[0278] In an embodiment, management platform 102 is configured with a set of artificial intelligence systems 1160 as part of a set of adaptive intelligence systems 614 that provides collaborative intelligence for a set of demand management applications 824 and supply chain applications 812 in product category 3010. Artificial intelligence systems 1160 provide collaborative intelligence for at least one supply chain application in supply chain applications 812 to generate a result corresponding to at least one aspect of supply for at least one product in product category 3010 as determined by at least one demand management application in demand management applications 824. As an example, a behavior-tracking demand management application generates a result regarding usage behavior for products in product category 3010. Artificial intelligence systems 1160 process the behavioral data and conclude that a need for increased consumer access to products in product category 3010 has been identified. This collaborative intelligence is then applied, optionally automatically, to the set of supply chain applications 812 to, for example, allocate production resources or other resources in the product category's value chain to a second product. As an example, a distributor responsible for stocking retail store shelves receives a new inventory plan that increases retail shelf space for a secondary product, perhaps by reducing space from a lower-margin product.

[0279] In some embodiments, artificial intelligence systems 1160 or the like may provide collaborative intelligence to supply chain applications and demand management applications, such as determining a time-of-day prioritization for the output of a demand management application that influences control of the supply chain applications to satisfy demand for at least one product in product category 3010 at any given time. Seasonal adjustment of the prioritization of the demand application results is an example of a temporal change. Priority adjustments may also be localized, such as when a large college football team plays at its home stadium, temporarily adjusting the local supply of tailgating equipment even though the demand management application results indicate that there is currently no demand for small propane stoves in the broader region.

[0280] The adaptive intelligence systems 614 (including, e.g., artificial intelligence capabilities 1160) providing collaborative intelligence may facilitate the development and deployment of intelligence for at least one capability selected from the list of capabilities consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, process management, etc. The adaptive intelligence systems 614 may be configured as a layer of a platform in which AI systems can act on or respond to data collected and / or generated by other systems (e.g., data processing systems, expert systems, machine learning systems, etc.).

[0281] In addition to providing collaborative intelligence tailored to a particular commodity category, collaborative intelligence may be provided for specific value chain entities 652, such as supply chain operators, businesses, or organizations participating in the supply chain for that commodity category.

[0282] Providing collaborative intelligence involves employing neural networks to process at least one of the inputs and outputs of a set of demand management and supply chain applications. Neural networks may be used in conjunction with demand applications (e.g., 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, search engine optimization applications, sales management applications, advertising network applications, behavior tracking applications, marketing analytics applications, location-based product or service targeting applications), collaborative filtering applications, product or service recommendation engines, etc. Neural networks can also be used in conjunction with supply chain applications (e.g., product timing management applications, product quantity management applications, logistics management applications, shipping applications, delivery applications, product order management applications, parts order management applications, etc.). Neural networks can provide collaborative intelligence by processing data available from multiple value chain data sources, including product categories. These data sources may provide collaborative intelligence by processing data available from processes, bills of materials, weather, traffic conditions, 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, product lifecycle management (PLM) systems, etc. Neural networks configured to provide collaborative intelligence may share adaptive capabilities with other adaptive intelligence systems 614, for example, when these systems are configured in a topology that facilitates such shared adaptation.In some embodiments, the neural network may facilitate allocation of available value chain / supply chain network resources to both a set of demand management applications and a set of supply chain applications. In some embodiments, the neural network may provide collaborative intelligence to improve at least one of the following list: process outputs, application outputs, process results, application results, and the like.

[0283] Referring to FIG. 15, an information technology system management platform, including a management platform for a goods and / or service value chain, is illustrated as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020, which is provided with a hybrid set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide collaborative intelligence through the application of a hybrid artificial intelligence system 3060, optionally including one or more expert systems, machine learning systems, etc., for collaboration with a demand management application set 824 and a supply chain application set 812 for goods category 3010 (those manufactured and sold through the value chain). The hybrid adaptive intelligence system 614 provides two types of artificial intelligence systems, Type A 3052 and Type B 3054, through a data processing, artificial intelligence, and computation system set 634. In an embodiment, the hybrid adaptive intelligence system 614 is selectable and / or configurable through the user interface 3020, and one or more hybrid adaptive intelligence systems 614 can operate on or in concert with a set of supply chain applications (e.g., demand management applications 824 and supply chain applications 812). The hybrid adaptive intelligence system 614 can include a hybrid artificial intelligence system 3060 that includes at least two types of artificial intelligence capabilities, including various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described in this disclosure and in documents incorporated by reference. The hybrid adaptive intelligence system 614 facilitates applying a first type of artificial intelligence system 1160 to the set of demand management applications 824 and a second type of artificial intelligence system 1160 to the set of supply chain applications 812.Here, the first and second types of artificial intelligence systems 1160 can operate independently or optionally in concert to provide collaborative intelligence for the operation of a value chain that produces at least one commodity belonging to the category of commodity 3010.

[0284] In an embodiment, user interface 3020 includes functionality for hybrid artificial intelligence system 3060 to receive inputs from selected data sources of the value chain (such as data sources used by demand management application set 824 and / or supply chain application set 812) and feed them to one of two types of artificial intelligence systems within hybrid artificial intelligence system 3060. These types are described herein and in documents incorporated by reference herein to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of commodity category 3010. In an example, the selected data sources of the value chain may be applied as inputs for classification or prediction, or as results related to the value chain, commodity category 3010, etc.

[0285] In some embodiments, the hybrid adaptive intelligence system 614 may provide a plurality of different artificial intelligence systems 1160, a hybrid artificial intelligence system 3060, or a combination thereof. In some embodiments, any of the plurality of different artificial intelligence systems 1160 and the hybrid artificial intelligence system 3060 may be configured as a neural network-based system, such as a classification adaptive neural network or a predictive adaptive neural network. An example of the hybrid adaptive intelligence system 614 may be a machine learning-based artificial intelligence system for the set of demand management applications 824 and a neural network-based artificial intelligence system for the set of supply chain applications 812. As an example of the hybrid artificial intelligence system 3060, the hybrid adaptive intelligence system 614 may provide a hybrid artificial intelligence system 3060 in which a first type of artificial intelligence applied to the demand management applications 824 is different from a second type of artificial intelligence applied to the supply chain applications 812. The hybrid artificial intelligence system 3060 may include any combination of artificial intelligence systems, including a plurality of first type artificial intelligence systems (e.g., neural networks) and at least one second type artificial intelligence system (e.g., an expert system). In an embodiment, the hybrid artificial intelligence system may include a hybrid neural network that applies a first type of artificial intelligence network to demand management applications 824 and a second type of artificial intelligence network to supply chain applications 812. Additionally, the hybrid artificial intelligence system 3060 may provide two types of artificial intelligence for different applications (e.g., different demand management applications 824 (e.g., a sales management application and a demand forecasting application) or different supply chain applications 812 (e.g., a logistics control application and a production quality control application)).

[0286] In an embodiment, the hybrid adaptive intelligence system 614 is applied as independent artificial intelligence functions to different demand management applications 824. For example, collaborative intelligence through hybrid artificial intelligence functions may be provided for demand planning applications with feedforward neural networks, demand forecasting applications with machine learning systems, sales applications with self-organizing neural networks, future demand aggregation applications with radial basis function neural networks, marketing applications with convolutional neural networks, advertising applications with recurrent neural networks, e-commerce applications with hierarchical neural networks, probabilistic neural networks for marketing analytics applications, association neural networks for customer relationship management applications, etc.

[0287] Referring to FIG. 16 , a management platform for information technology systems, including a management platform for a goods and / or services value chain, is illustrated as a block diagram of functional elements and representative interconnections for providing a set of forecasts 3070. The management platform includes a user interface 3020 that includes adaptive intelligence systems 614. The adaptive intelligence systems 614 provide the set of forecasts 3070 through artificial intelligence (e.g., application of artificial intelligence system 1160), optionally applying one or more expert systems, machine learning systems, and the like, in combination with a coordinated demand management application 824 and a supply chain application 812 for a category of goods 3010 (manufactured and sold through the value chain). The adaptive intelligence system 614 can provide the set of forecasts 3070 through a set of data processing, artificial intelligence, and computational systems 634. In embodiments, the adaptive intelligence system 614 is selectable and / or configurable through the user interface 3020, allowing one or more adaptive intelligence systems 614 to operate on or in concert with the coordinated set of value chain applications. The adaptive intelligence system 614 can include artificial intelligence systems providing artificial intelligence capabilities known to be associated with artificial intelligence, such as artificial intelligence, various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described in this disclosure and in documents incorporated by reference. The adaptive intelligence system 614 facilitates applying adaptive intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812, such as by generating a set of predictions 3070 that facilitates the coordination of the two sets of value chain applications, or at least one demand management application and at least one supply chain application from each set.

[0288] In some embodiments, the set of predictions 3070 includes at least one prediction that predicts an impact on a supply chain application based on the current state of the coordinated demand management applications. For example, a prediction that demand for a product will decrease more quickly than previously expected may be included. Conversely, the set of predictions 3070 includes at least one prediction that predicts an impact on a demand management application based on the current state of the coordinated supply chain applications. For example, a prediction that predicts the likelihood that a supply shortage of a product will affect a demand metric for the associated product. In some embodiments, the set of predictions 3070 includes at least one prediction that predicts an adjustment in supply necessary to meet demand. Other predictions include at least one prediction that predicts a change in demand that will affect supply. Another prediction in the set of predictions predicts a change in supply that will affect at least one of the set of demand management applications (e.g., a promotion application for at least one product in a product category). A prediction in the set of predictions may be as simple as a prediction of the likelihood that the supply of a product in a product category will not meet the demand set by a demand setting application.

[0289] In an embodiment, the adaptive intelligence system 614 may provide a set of artificial intelligence functions to provide a set of predictions for coordinated demand management and supply chain applications. As an example, the set of artificial intelligence functions may include a probabilistic neural network used to predict failure or problem conditions in demand management applications (e.g., lack of sufficient validated feedback). The probabilistic neural network may be used to predict problem conditions in machines performing value chain operations (e.g., manufacturing machines, automated handling machines, packaging machines, shipping machines, etc.) based on a collection of machine operational information and preventive maintenance information.

[0290] In an embodiment, the prediction set 3070 is provided directly from the management platform 102 through the adaptive artificial intelligence system set.

[0291] In an embodiment, the forecast set 3070 is provided for a collaborative set of demand management applications and supply chain applications for a product category, utilizing artificial intelligence capabilities to coordinate the collaboration of the demand management applications and supply chain applications.

[0292] In an embodiment, the prediction set 3070 is a prediction of the results when a collaborative set of demand management applications and supply chain applications operates a value chain for a product category, and enables a user to run test cases of the collaborative set of demand management applications and supply chain applications to identify sets that produce desirable results (candidate collaborative application sets) and sets that produce undesirable results.

[0293] Referring to FIG. 17 , a management platform for information technology systems, including a management platform for a goods and / or services value chain, is illustrated as a block diagram of functional elements and representative interconnections for providing a classification set 3080. The management platform includes a user interface 3020 that includes adaptive intelligence systems 614. The adaptive intelligence systems 614 are used in conjunction with a set of coordinated demand management applications 824 and supply chain applications 812, for example, through the application of an artificial intelligence (AI) system 1160, or optionally one or more expert systems, machine learning systems, etc. These applications help manage goods categories 3010 as they are manufactured, sold, resold, rented, leased, given away, serviced, recycled, refurbished, improved, etc., throughout the value chain. The adaptive intelligence system 614 can provide the classification set 3080 through a set of data processing, artificial intelligence, and computational systems 634. In embodiments, the adaptive intelligence system 614 is selectable and / or configurable through the user interface 3020, and one or more adaptive intelligence systems 614 can operate on or in concert with the coordinated set of value chain applications. The adaptive intelligence system 614 can include artificial intelligence systems that provide classification capabilities through various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems, among others, as described in this disclosure and the documents incorporated by reference. The adaptive intelligence system 614 facilitates applying adaptive intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812, such as by generating a set of classifications 3080 that facilitates the coordination of the two sets of value chain applications, or at least the coordination of at least one demand management application and at least one supply chain application from each set.

[0294] In an embodiment, the set of classifications 3080 includes at least one classification of the current state of a supply chain application used by the coordinated demand management application. For example, a classification of a problem condition that may affect the operation of the demand management application (e.g., a marketing application) may be included. Such classifications may be useful in determining how to adjust market expectations for a product that is experiencing lower-than-expected yields. Conversely, the set of classifications 3080 may include at least one classification indicating the relationship between the current state of the demand management application and the coordinated supply chain application. In an embodiment, the set of classifications 3080 is a set of classifications for supply adjustments to meet demand, e.g., adjusting the needs of production workers is classified differently from adjusting third-party logistics providers. Other classifications may include at least one classification classifying a perceived change in demand and the resulting potential impact on supply management. Other classifications in the set of classifications may include classifications of the impact of a supply chain application on at least one application in the set of demand management applications (e.g., a promotion application for at least one product in a product category). A classification in the set of classifications may be as simple as classifying the likelihood that the supply of a product in a product category will not meet the demand set by a demand setting application.

[0295] In some embodiments, the adaptive intelligence system 614 may provide a set of artificial intelligence capabilities intended to assist in providing the set of classifications 3080 for a coordinated set of demand management and supply chain applications. As an example, the set of artificial intelligence capabilities may include a probabilistic neural network used to classify fault or problem conditions in a demand management application (e.g., classifying conditions for lack of validated feedback). The probabilistic neural network may be used to classify problem conditions in machines performing value chain operations (e.g., manufacturing machines, automated handling machines, packaging machines, shipping machines, etc.) as applicable to at least one of machine operation information and preventive maintenance information for the machines.

[0296] In an embodiment, the classification set 3080 is provided directly from the management platform 102 through a set of adaptive artificial intelligence systems. Additionally, the classification set 3080 is provided to a set of collaborative demand management applications and supply chain applications for a product category, utilizing artificial intelligence capabilities to coordinate the collaboration between the demand management applications and the supply chain applications.

[0297] In an embodiment, classification set 308...

Claims

1. [Control tower for computational tasks of value chain network entities] 1. A computer-implemented method comprising: configuring a set of secondary computing devices of a set of value chain network entities to communicate with a primary computing device of an enterprise operator, the primary computing device managing the set of secondary computing devices; receiving, by at least one member of the set of secondary computing devices, a set of primary commands from the primary computing device, each of the set of primary commands being at least one of a task or a request; allocating at least a portion of one or more computing devices capable of executing the set of primary commands as a set of one or more computing devices managed by the set of secondary computing devices; configuring the set of one or more computing devices to execute, by the set of secondary computing devices, one or more secondary commands based at least in part on the set of primary commands; executing, by the set of secondary computing devices, the set of primary commands based at least in part on the set of one or more computing devices executing the one or more secondary commands; and sending to the primary computing device a generated system output responsive to the set of primary commands based at least in part on the set of one or more computing devices executing the one or more secondary commands.

2. 2. The computer-implemented method of claim 1, wherein configuring the set of one or more computing devices to execute the one or more secondary commands includes creating a set of at least one configured system service (CSS).

3. 3. The computer-implemented method of claim 2, wherein generating the at least one set of CSSs includes utilizing an output of the primary computing device as an input for generating one or more control parameters.

4. 3. The computer-implemented method of claim 2, wherein configuring the set of one or more computing devices to execute the one or more secondary commands includes providing intelligence received from the at least one CSS to the set of one or more computing devices.

5. 2. The computer-implemented method of claim 1, wherein allocating at least a portion of the one or more computing devices capable of executing the set of primary commands as the set of one or more computing devices managed by the secondary computing device comprises performing a registration process between the set of one or more computing devices and the secondary computing device.

6. 6. The computer-implemented method of claim 5, wherein performing the registration process includes performing an inventory of communication protocols and data formats used by the set of one or more computing devices.

7. 7. The computer-implemented method of claim 6, further comprising: obtaining, by the secondary computing device, one or more application programming interfaces (APIs) that enable communication and data format conversion between each computing device in the set of one or more computing devices and the secondary computing device.

8. 3. The computer-implemented method of claim 2, further comprising: bypassing the primary computing device to receive the external data at the secondary computing device if the external data is unused by the primary computing device.

9. 3. The computer-implemented method of claim 2, wherein generating at least one set of CSS includes generating at least one CSS for each interface layer in a management stack.

10. 10. The computer-implemented method of claim 1, further comprising: performing intelligent decision-making regarding a strategy for the set of primary commands based at least in part on a configured intelligence service (CIS).

11. The set of value chain network entities comprises:

10. The computer-implemented method of claim 1, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

12. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: configuring a set of secondary computing devices of a set of value chain network entities to communicate with a primary computing device of an enterprise operator, the primary computing device managing the set of secondary computing devices; receiving, by at least one member of the set of secondary computing devices, a set of primary commands from the primary computing device, each of the set of primary commands being at least one of a task or a request; allocating at least a portion of one or more computing devices capable of executing the set of primary commands as a set of one or more computing devices managed by the set of secondary computing devices; configuring the set of one or more computing devices to execute, by the set of secondary computing devices, one or more secondary commands based at least in part on the set of primary commands; executing, by the set of secondary computing devices, the set of primary commands based at least in part on the set of one or more computing devices executing the one or more secondary commands; and transmitting to the primary computing device a generated system output responsive to the set of primary commands based at least in part on the set of one or more computing devices executing the one or more secondary commands; 1. A computing system configured to perform operations including:

13. 13. The computing system of claim 12, wherein configuring the set of one or more computing devices to execute the one or more secondary commands includes creating a set of at least one configured system service (CSS).

14. 14. The computing system of claim 13, wherein generating the at least one set of CSS includes utilizing an output of the primary computing device as an input for generating one or more control parameters.

15. 14. The computing system of claim 13, wherein configuring the set of one or more computing devices to execute the one or more secondary commands includes providing intelligence received from the at least one CSS to the set of one or more computing devices.

16. 13. The computing system of claim 12, wherein allocating at least a portion of the one or more computing devices capable of executing the set of primary commands as the set of one or more computing devices managed by the secondary computing device comprises performing a registration process between the set of one or more computing devices and the secondary computing device.

17. 17. The computing system of claim 16, wherein performing the registration process includes performing an inventory of communication protocols and data formats used by the set of one or more computing devices.

18. 20. The computing system of claim 17, wherein the operations further include obtaining, by the secondary computing device, one or more application programming interfaces (APIs) that enable communication and data format conversion between each computing device in the set of one or more computing devices and the secondary computing device.

19. 14. The computing system of claim 13, wherein the operations further comprise bypassing the primary computing device to receive the external data at the secondary computing device if the external data is unused by the primary computing device.

20. 14. The computing system of claim 13, wherein generating at least one set of CSS includes generating at least one CSS for each interface layer in a management stack.

21. 13. The computing system of claim 12, wherein the operations further comprise: performing intelligent decision-making regarding a strategy for the set of primary commands based at least in part on a configured intelligence service (CIS).

22. The set of value chain network entities comprises:

13. The computing system of claim 12, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

23. [Mini Control Tower for Value Chain Network Entities] 1. A computer-implemented method comprising: configuring a set of sub-level computing devices for communication with a primary computing device, said primary computing device managing said set of sub-level computing devices to coordinate performance of a set of entities in a value chain network; receiving, by the set of sub-level computing devices, a primary command from the primary computing device, the primary command being one of a task or a request related to the value chain network; allocating at least a portion of one or more operating devices capable of executing said primary commands as a set of one or more computing devices managed by said set of sub-level computing devices, said sub-level computing devices being computing devices that manage or execute performance of a particular entity or relationship of said value chain network; configuring, by the set of sub-level computing devices, the one or more computing devices to execute one or more secondary commands based at least in part on the primary command; and performing, by the set of sub-level computing devices, the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands.

24. 24. The computer-implemented method of claim 23, wherein configuring the set of one or more computing devices to execute the one or more secondary commands includes creating a set of at least one configured system service (CSS).

25. 25. The computer-implemented method of claim 24, wherein generating the at least one set of CSS includes utilizing outputs of multiple sources as inputs to generate one or more control parameters.

26. 25. The computer-implemented method of claim 24, wherein configuring the set of one or more computing devices to execute the one or more secondary commands includes providing intelligence received from the at least one CSS to the set of one or more computing devices.

27. 24. The computer-implemented method of claim 23, wherein allocating at least the portion of one or more computing devices capable of executing the primary command as the set of one or more computing devices managed by the set of sub-level computing devices includes performing a registration process between the set of one or more computing devices and the set of sub-level computing devices.

28. 30. The computer-implemented method of claim 27, wherein performing the registration process includes performing an inventory of communication protocols and data formats used by the set of one or more computing devices.

29. 30. The computer-implemented method of claim 28, further comprising obtaining, by the set of sub-level computing devices, one or more application programming interfaces (APIs) that enable communication and data format conversion between each computing device of the set of one or more computing devices and the set of sub-level computing devices.

30. 25. The computer-implemented method of claim 24, further comprising: if external data is unused by the primary computing device, bypassing the primary computing device to receive the external data at the set of sub-level computing devices.

31. 25. The computer-implemented method of claim 24, wherein generating at least one set of CSS includes generating at least one CSS for each interface layer in a management stack.

32. 24. The computer-implemented method of claim 23, further comprising: performing intelligent decision-making regarding strategy to the primary command based at least in part on configured intelligence services (CIS).

33. The set of entities in the value chain network comprises:

24. The computer-implemented method of claim 23, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

34. 24. The computer-implemented method of claim 23, further comprising transmitting to the primary computing device a generated system output responsive to the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands.

35. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: configuring a set of sub-level computing devices for communication with a primary computing device, said primary computing device managing said set of sub-level computing devices to coordinate performance of a set of entities in a value chain network; receiving, by the set of sub-level computing devices, a primary command from the primary computing device, the primary command being one of a task or a request related to the value chain network; allocating at least a portion of one or more operating devices capable of executing said primary commands as a set of one or more computing devices managed by said set of sub-level computing devices, said sub-level computing devices being computing devices that manage or execute performance of a particular entity or relationship of said value chain network; configuring, by the set of sub-level computing devices, the one or more computing devices to execute one or more secondary commands based at least in part on the primary command; and performing, by the set of sub-level computing devices, the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands; 1. A computing system configured to perform operations including:

36. 36. The computing system of claim 35, wherein configuring the set of one or more computing devices to execute the one or more secondary commands includes creating a set of at least one configured system service (CSS).

37. 37. The computing system of claim 36, wherein generating the at least one set of CSS includes utilizing outputs of multiple sources as inputs to generate one or more control parameters.

38. 37. The computing system of claim 36, wherein configuring the set of one or more computing devices to execute the one or more secondary commands comprises providing intelligence received from the at least one CSS to the set of one or more computing devices.

39. 36. The computing system of claim 35, wherein allocating at least the portion of one or more computing devices capable of executing the primary command as the set of one or more computing devices managed by the set of sub-level computing devices comprises performing a registration process between the set of one or more computing devices and the set of sub-level computing devices.

40. 40. The computing system of claim 39, wherein performing the registration process includes performing an inventory of communication protocols and data formats used by the set of one or more computing devices.

41. 41. The computing system of claim 40, wherein the operations further include obtaining, by the set of sub-level computing devices, one or more application programming interfaces (APIs) that enable communication and data format conversion between each computing device of the set of one or more computing devices and the set of sub-level computing devices.

42. 37. The computing system of claim 36, further comprising: if external data is unused by the primary computing device, bypassing the primary computing device to receive the external data at the set of sub-level computing devices.

43. 37. The computing system of claim 36, wherein generating at least one set of CSS includes generating at least one CSS for each interface layer in a management stack.

44. 36. The computing system of claim 35, wherein the operations further comprise: performing intelligent decision making regarding strategy to the primary command based at least in part on a configured intelligence service (CIS).

45. The set of entities in the value chain network comprises:

36. The computing system of claim 35, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

46. 36. The computing system of claim 35, wherein the operations further include transmitting to the primary computing device a generated system output responsive to the primary command based at least in part on the set of one or more computing devices executing the one or more secondary commands.

47. [ML / AI to flag and respond to potential bottlenecks in the value chain] 1. A computer-implemented method comprising: receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information being generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; identifying potential risks in the value chain based at least in part on an output of the AI-based learning classification regarding at least one of the operating state, the fault state, the operating flow, or the behavior; and taking action to mitigate the potential risk in the value chain network.

48. 48. The computer-implemented method of claim 47, wherein taking action to mitigate the potential risk in the value chain network comprises flagging the potential risk in the value chain network.

49. 48. The computer-implemented method of claim 47, wherein taking action to mitigate the potential risk in the value chain network comprises responding to the potential risk in the value chain network.

50. 48. The computer-implemented method of claim 47, further comprising integrating data related to warehouse management, inventory management, order management, and analytics to optimize omni-channel fulfillment.

51. 48. The computer-implemented method of claim 47, wherein taking action to mitigate the potential risk in the value chain network comprises resolving an out-of-stock situation.

52. 48. The computer-implemented method of claim 47, wherein taking action to mitigate the potential risk in the value chain network includes predicting timing of placing an order based at least in part on upstream data.

53. 48. The computer-implemented method of claim 47, wherein taking action to mitigate the potential risk in the value chain network comprises planning supply.

54. 48. The computer-implemented method of claim 47, wherein taking action to mitigate the potential risk in the value chain network comprises optimizing inventory mix.

55. receiving external data; and 48. The computer-implemented method of claim 47, further comprising determining a strategy to reduce transportation costs based at least in part on the external data.

56. 48. The computer-implemented method of claim 47, further comprising: providing a platform with a set of AI-based learning models for download.

57. The set of value chain network entities comprises:

48. The computer-implemented method of claim 47, comprising at least one of: a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

58. 57. The computer-implemented method of claim 56, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

59. 57. The computer-implemented method of claim 56, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

60. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information being generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; identifying potential risks in the value chain based at least in part on an output of the AI-based learning classification regarding at least one of the operating state, the fault state, the operating flow, or the behavior; and taking action to mitigate the potential risks in the value chain network; 1. A computing system configured to perform operations including:

61. 61. The computing system of claim 60, wherein taking action to mitigate the potential risk in the value chain network comprises flagging the potential risk in the value chain network.

62. 61. The computing system of claim 60, wherein the taking action to mitigate the potential risk in the value chain network comprises responding to the potential risk in the value chain network.

63. 61. The computing system of claim 60, wherein the operations further include integrating data related to warehouse management, inventory management, order management, and analytics to optimize omni-channel fulfillment.

64. 61. The computing system of claim 60, wherein the taking action to mitigate the potential risk in the value chain network comprises resolving an out-of-stock situation.

65. 61. The computing system of claim 60, wherein taking action to mitigate the potential risk in the value chain network includes predicting timing of placing an order based at least in part on upstream data.

66. 61. The computing system of claim 60, wherein the taking action to mitigate the potential risk in the value chain network comprises planning supply.

67. 61. The computing system of claim 60, wherein the taking action to mitigate the potential risk in the value chain network comprises optimizing inventory mix.

68. The operation receiving external data; and 61. The computing system of claim 60, further comprising determining a strategy to reduce transportation costs based at least in part on the external data.

69. 61. The computing system of claim 60, wherein the operations further comprise providing a platform with a set of AI-based learning models for download.

70. The set of value chain network entities comprises:

61. The computing system of claim 60, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

71. 70. The computing system of claim 69, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of value chain network entity outcomes, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

72. 70. The computing system of claim 69, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

73. [Automated and intelligent procurement system for value chain network entities] 1. A computer-implemented method comprising: receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information being generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; determining procurement actions to be performed in the value chain network based at least in part on outputs of the set of AI-based learning models; and performing the procurement action to facilitate improvement of at least one of the operating state, the fault state, the operating flow, or the behavior of the at least one entity of the set of value chain network entities.

74. 74. The computer-implemented method of claim 73, further comprising: providing an alert describing the procurement action that was performed.

75. 74. The computer-implemented method of claim 73, wherein the information includes past behavior over time, historical data, and current data.

76. 74. The computer-implemented method of claim 73, further comprising providing real-time information regarding supplier performance.

77. 74. The computer-implemented method of claim 73, further comprising monitoring supplier and procurement team compliance.

78. 74. The computer-implemented method of claim 73, further comprising automatically generating a purchase order associated with the procurement action.

79. 74. The computer-implemented method of claim 73, further comprising automatically processing invoice processing associated with the procurement action.

80. 74. The computer-implemented method of claim 73, further comprising unifying data related to warehouse management, inventory management, order management, and analytics to optimize omni-channel fulfillment.

81. 74. The computer-implemented method of claim 73, wherein performing the procurement action includes resolving an out-of-stock situation.

82. 74. The computer-implemented method of claim 73, wherein performing the procurement action includes predicting timing of placing an order based at least in part on upstream data.

83. The set of value chain network entities comprises:

74. The computer-implemented method of claim 73, comprising at least one of: a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

84. 74. The computer-implemented method of claim 73, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

85. 74. The computer-implemented method of claim 73, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that includes at least one of the operating state, the fault state, the operating flow, or the behavior.

86. 74. The computer-implemented method of claim 73, wherein the procurement action is performed by a value chain network digital twin.

87. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information being generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; determining procurement actions to be performed in the value chain network based at least in part on outputs of the set of AI-based learning models; and performing the procurement action to facilitate an improvement of at least one of the operating state, the fault state, the operating flow, or the behavior of the at least one entity of the set of value chain network entities; 1. A computing system configured to perform operations including:

88. 88. The computing system of claim 87, wherein the operations further comprise: providing an alert describing the procurement action that was performed.

89. 88. The computing system of claim 87, wherein the information includes past behavior over time, historical data, and current data.

90. 88. The computing system of claim 87, wherein the operations further comprise providing real-time information regarding supplier performance.

91. 88. The computing system of claim 87, wherein the operations further include monitoring supplier and procurement team compliance.

92. 88. The computing system of claim 87, wherein the operations further comprise automatically generating a purchase order associated with the procurement action.

93. 88. The computing system of claim 87, wherein the operations further comprise automatically processing invoice processing associated with the procurement action.

94. 90. The computing system of claim 87, wherein the operations further comprise unifying data related to warehouse management, inventory management, order management, and analytics to optimize omni-channel fulfillment.

95. 88. The computing system of claim 87, wherein performing the procurement action includes resolving an out-of-stock situation.

96. 88. The computing system of claim 87, wherein performing the procurement action includes predicting timing of placing an order based at least in part on upstream data.

97. The set of value chain network entities comprises:

90. The computing system of claim 87, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

98. 88. The computing system of claim 87, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of value chain network entity outcomes, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

99. 88. The computing system of claim 87, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that includes at least one of the operating state, the fault state, the operating flow, or the behavior.

100. 88. The computing system of claim 87, wherein the procurement action is performed by a value chain network digital twin.

101. [Predictive Procurement System] 1. A computer-implemented method comprising: receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information being generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a first set of artificial intelligence (AI) based learning models, wherein at least one member of the first set of AI based learning models is trained with a training dataset of value chain network data to generate a prediction of future demand for items in the value chain network; providing the information to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; identifying a potential risk in the value chain network associated with the at least one value chain network entity based at least in part on an output of the AI-based learning model; and and at least one of outputting recommendations for mitigating the potential risks in the value chain network and automatically performing actions to mitigate the potential risks in the value chain network.

102. 102. The computer-implemented method of claim 101, wherein taking action to mitigate the potential risk in the value chain network includes flagging the potential risk in the value chain network associated with the at least one value chain network entity.

103. 102. The computer-implemented method of claim 101, wherein taking action to mitigate the potential risk in the value chain network includes responding to the potential risk in the value chain network associated with the at least one value chain network entity.

104. 102. The computer-implemented method of claim 101, further comprising integrating data related to warehouse management, inventory management, order management, and analytics to optimize omni-channel fulfillment.

105. 102. The computer-implemented method of claim 101, wherein taking action to mitigate the potential risk in the value chain network includes resolving an out-of-stock situation.

106. 102. The computer-implemented method of claim 101, wherein taking action to mitigate the potential risk in the value chain network includes predicting timing for placing an order based at least in part on upstream data.

107. 102. The computer-implemented method of claim 101, further comprising: providing an alert describing the actions taken to mitigate the potential risk in the value chain network.

108. 102. The computer-implemented method of claim 101, wherein the information includes past behavior over time, historical data, and current data.

109. 102. The computer-implemented method of claim 101, wherein the potential risk is a potential disruption in the value chain network.

110. 102. The computer-implemented method of claim 101, further comprising rendering a visualization related to at least one of an inbound shipment or an outbound shipment associated with the item.

111. The set of value chain network entities comprises:

102. The computer-implemented method of claim 101, comprising at least one of: a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

112. 102. The computer-implemented method of claim 101, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

113. 102. The computer-implemented method of claim 101, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy that includes at least one of the operating state, the fault state, the operating flow, or the behavior.

114. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: receiving, by a computing device, information related to a set of value chain network entities of a value chain network, the information being generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a first set of artificial intelligence (AI) based learning models, wherein at least one member of the first set of AI based learning models is trained with a training dataset of value chain network data to generate a prediction of future demand for items in the value chain network; providing the information to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; identifying a potential risk in the value chain network associated with the at least one value chain network entity based at least in part on an output of the AI-based learning model; and at least one of outputting recommendations for mitigating the potential risks in the value chain network and automatically taking actions to mitigate the potential risks in the value chain network; 1. A computing system configured to perform operations including:

115. 115. The computing system of claim 114, wherein performing the action to mitigate the potential risk in the value chain network includes flagging the potential risk in the value chain network associated with the at least one value chain network entity.

116. 115. The computing system of claim 114, wherein performing the action to mitigate the potential risk in the value chain network includes responding to the potential risk in the value chain network associated with the at least one value chain network entity.

117. 115. The computing system of claim 114, wherein the operations further include integrating data related to warehouse management, inventory management, order management, and analytics to optimize omni-channel fulfillment.

118. 115. The computing system of claim 114, wherein taking the action to mitigate the potential risk in the value chain network associated with the item includes resolving an out-of-stock situation.

119. 115. The computing system of claim 114, wherein taking action to mitigate the potential risk in the value chain network includes predicting the timing of placing an order based at least in part on upstream data.

120. 115. The computing system of claim 114, wherein the operations further comprise providing an alert describing the action taken to mitigate the potential risk in the value chain network associated with the at least one value chain network entity.

121. 115. The computing system of claim 114, wherein the information includes past behavior over time, historical data, and current data.

122. 115. The computing system of claim 114, wherein the potential risk is a potential disruption in the value chain network associated with the at least one value chain network entity.

123. 115. The computing system of claim 114, wherein the operations further comprise rendering a visualization related to at least one of inbound transportation or outbound transportation related to the at least one value chain network entity.

124. The set of value chain network entities comprises:

115. The computing system of claim 114, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

125. 115. The computing system of claim 114, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of results of the value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

126. 115. The computing system of claim 114, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

127. ML / AI for automation of a set of value chain network entities 1. A computer-implemented method comprising: receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; determining tasks to be completed for the value chain network based at least in part on outputs of the set of AI-based learning models; and performing the tasks to drive improvement in the value chain network.

128. 128. The computer-implemented method of claim 127, wherein performing the tasks includes forecasting future demand for items in the value chain network.

129. 129. The computer-implemented method of claim 128, wherein the information includes one or more of historical sales data and market trends related to the item.

130. 129. The computer-implemented method of claim 128, wherein performing the tasks includes detecting defects and quality issues in items in the value chain network.

131. 131. A computer-implemented method as recited in claim 130, wherein the information includes one or more of a video and a photograph associated with the item.

132. 128. The computer-implemented method of claim 127, wherein performing the tasks includes predicting when an item in the value chain network will fail.

133. 133. The computer-implemented method of claim 132, wherein the information includes data from one or more sensors associated with the item.

134. Executing the task includes: identifying value chain processes that can be optimized based at least in part on analyzing the information related to the value chain network; and 128. The computer-implemented method of claim 127, further comprising optimizing the value chain process.

135. 135. The computer-implemented method of claim 134, wherein the value chain processes include one or more of transportation routing, inventory management, or supplier selection.

136. Executing the task includes: analyzing user data of users from at least one source; and 128. The computer-implemented method of claim 127, further comprising determining one or more attributes of the user based at least in part on the user data.

137. The set of value chain network entities comprises:

128. The computer-implemented method of claim 127, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

138. 128. The computer-implemented method of claim 127, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

139. 128. The computer-implemented method of claim 127, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

140. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of the set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities; determining tasks to be completed for the value chain network based at least in part on outputs of the set of AI-based learning models; and performing said tasks to promote improvements in said value chain network; 1. A computing system configured to perform operations including:

141. 141. The computing system of claim 140, wherein performing the tasks includes forecasting future demand for items in the value chain network.

142. 142. The computing system of claim 141, wherein the information includes one or more of historical sales data and market trends related to the item.

143. 141. The computing system of claim 140, wherein performing the tasks includes detecting defects and quality issues in items in the value chain network.

144. 144. The computing system of claim 143, wherein the information includes one or more of a video and a photograph associated with the item.

145. 141. The computing system of claim 140, wherein performing the tasks includes predicting when an item in the value chain network will fail.

146. 146. The computing system of claim 145, wherein the information includes data from one or more sensors associated with the item.

147. Executing the task includes: identifying value chain processes that can be optimized based at least in part on analyzing the information related to the value chain network; and 141. The computing system of claim 140, further comprising optimizing the value chain process.

148. 148. The computing system of claim 147, wherein the value chain processes include one or more of transportation routing, inventory management, or supplier selection.

149. Executing the task includes: analyzing user data of users from at least one source; and 141. The computing system of claim 140, further comprising determining one or more attributes of the user based at least in part on the user data.

150. The set of value chain network entities comprises:

141. The computing system of claim 140, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

151. 141. The computing system of claim 140, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of results of the value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

152. 141. The computing system of claim 140, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

153. [ML Directing Smart Machines for Value Chain Networks] 1. A computer-implemented method comprising: receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of a set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of the value chain network, and wherein at least one member of the set of AI based learning models is trained with the training dataset to determine a task to be completed for the value chain network upon receiving the classification of the at least one of the operating state, the fault state, the operating flow, or the behavior; and providing a set of computer code instructions to a machine to perform the task to facilitate improved operation of the value chain network.

154. 154. The computer-implemented method of claim 153, wherein providing the machine with the set of computer code instructions to perform the task comprises instructing the machine to move items throughout the value chain network.

155. 155. The computer-implemented method of claim 154, wherein the machine comprises one or more of a robot, an automated guided vehicle (AGV), a smart container, a 3D printer, or a drone.

156. 154. The computer-implemented method of claim 153, wherein providing the machine with the set of computer code instructions to perform the task includes instructing the machine to detect defects and quality issues in items within the value chain network.

157. 157. The computer-implemented method of claim 156, wherein the information related to the value chain network includes one or more of videos and photographs related to the items in the value chain network for detecting the defects and quality issues of the items.

158. 154. The computer-implemented method of claim 153, wherein providing the machine with the set of computer code instructions to perform the task includes instructing the machine to predict when an item in the value chain network will fail.

159. 159. The computer-implemented method of claim 158, wherein the information related to the value chain network includes data from one or more sensors associated with the items in the value chain network to predict when the items will fail.

160. Providing the set of computer code instructions to the machine for performing the task comprises: identifying value chain processes that can be optimized based at least in part on analyzing the information related to the value chain network; and optimizing said value chain processes; 154. The computer-implemented method of claim 153, further comprising instructing the machine:

161. 161. The computer-implemented method of claim 160, wherein the value chain processes include one or more of transportation routing, inventory management, supplier selection, or warehouse management.

162. 154. The computer-implemented method of claim 153, wherein providing the machine with the set of computer code instructions to perform the task includes instructing the machine to transport the item between locations.

163. The set of value chain network entities comprises:

154. The computer-implemented method of claim 153, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

164. 154. The computer-implemented method of claim 153, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

165. 154. The computer-implemented method of claim 153, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

166. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of a set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of the value chain network, and wherein at least one member of the set of AI based learning models is trained with the training dataset to determine a task to be completed for the value chain network upon receiving the classification of the at least one of the operating state, the fault state, the operating flow, or the behavior; and providing a machine with a set of computer code instructions for performing said tasks to facilitate improved operation of said value chain network; 1. A computing system configured to perform operations including:

167. 167. The computing system of claim 166, wherein providing the machine with the set of computer code instructions to perform the task comprises instructing the machine to move items throughout the value chain network.

168. 168. The computing system of claim 167, wherein the machine comprises one or more of a robot, an automated guided vehicle (AGV), a smart container, a 3D printer, or a drone.

169. 167. The computing system of claim 166, wherein providing the machine with the set of computer code instructions to perform the task includes instructing the machine to detect defects and quality issues in items within the value chain network.

170. 170. The computing system of claim 169, wherein the information related to the value chain network includes one or more of videos and photographs related to the items in the value chain network for detecting the defects and quality issues of the items.

171. 167. The computing system of claim 166, wherein providing the machine with the set of computer code instructions to perform the task includes instructing the machine to predict when an item in the value chain network will fail.

172. 172. The computing system of claim 171, wherein the information related to the value chain network includes data from one or more sensors associated with the items in the value chain network to predict when the items will fail.

173. Providing the set of computer code instructions to the machine for performing the task comprises: identifying value chain processes that can be optimized based at least in part on analyzing the information related to the value chain network; and optimizing said value chain processes; 167. The computing system of claim 166, further comprising instructing the machine to:

174. 174. The computing system of claim 173, wherein the value chain processes include one or more of transportation routing, inventory management, supplier selection, or warehouse management.

175. 167. The computing system of claim 166, wherein providing the machine with the set of computer code instructions to perform the task comprises instructing the machine to transport the item between locations.

176. The set of value chain network entities comprises:

167. The computing system of claim 166, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

177. 167. The computing system of claim 166, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of results of the value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

178. 167. The computing system of claim 166, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

179. [Robotic Process Automation] 1. A computer-implemented method comprising: receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of a set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of the value chain network, and wherein at least one member of the set of AI based learning models is trained with the training dataset to determine a task to be completed for the value chain network upon receiving the classification of the at least one of the operating state, the fault state, the operating flow, or the behavior; and configuring a robotic process automation system to perform the task to facilitate improvement of the value chain network.

180. 180. The computer-implemented method of claim 179, wherein the task includes automatically processing one or more orders for items in the value chain network based at least in part on the information.

181. automatically processing the one or more orders for the items in the value chain network, extracting order data for the one or more orders from one or more sources; and 181. The computer-implemented method of claim 180, further comprising automatically inputting the order data extracted from the one or more sources into an order management system.

182. The task is: monitoring inventory levels of items within the value chain network; and 180. The computer-implemented method of claim 179, further comprising automatically generating one or more purchase orders for the item when the inventory level for the item in the value chain network falls below a threshold.

183. Tracking the shipment of said items in real time; and 183. The computer-implemented method of claim 182, further comprising automatically updating the inventory levels of the items in the value chain network based at least in part on tracking the transportation of the items in real time.

184. The task is: extracting billing data for one or more bills from one or more sources; and 180. The computer-implemented method of claim 179, further comprising automatically inputting the invoice data extracted from the one or more sources into an accounting system.

185. 180. The computer-implemented method of claim 179, further comprising rendering a user interface that enables a user to visually design an automation workflow.

186. 180. The computer-implemented method of claim 179, further comprising rendering a user interface that allows a user to manage the automation process.

187. Creating a plurality of AI-based learning models; and selecting a plurality of application programming interfaces for integrating the plurality of AI models into one or more automation workflows; 180. The computer-implemented method of claim 179, further comprising rendering a user interface that enables a user to:

188. Tracking automation performance; and generating a custom dashboard based at least in part on said automation performance; 180. The computer-implemented method of claim 179, further comprising rendering a user interface that enables a user to:

189. The set of value chain network entities comprises:

180. The computer-implemented method of claim 179, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

190. 180. The computer-implemented method of claim 179, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

191. 180. The computer-implemented method of claim 179, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

192. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: receiving, by a computing device, information related to a value chain network, the information being generated by at least one of a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data related to the set of value chain network entities, or a set of APIs configured to expose data related to the set of value chain network entities; providing the information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of a set of value chain network entity operating data to classify at least one of an operating state, a fault state, an operating flow, or a behavior of the value chain network, and wherein at least one member of the set of AI based learning models is trained with the training dataset to determine a task to be completed for the value chain network upon receiving the classification of the at least one of the operating state, the fault state, the operating flow, or the behavior; and configuring a robotic process automation system to perform the tasks to facilitate improvement of the value chain network; 1. A computing system configured to perform operations including:

193. 200. The computing system of claim 192, wherein the task includes automatically processing one or more orders for items in the value chain network based at least in part on the information.

194. automatically processing the one or more orders for the items in the value chain network, extracting order data for the one or more orders from one or more sources; and 202. The computing system of claim 193, further comprising automatically inputting the order data extracted from the one or more sources into an order management system.

195. The task is: monitoring inventory levels of items within the value chain network; and 193. The computing system of claim 192, further comprising automatically generating one or more purchase orders for the item when the inventory level of the item in the value chain network falls below a threshold.

196. Tracking the shipment of said items in real time; and 200. The computing system of claim 195, further comprising automatically updating the inventory levels of the items in the value chain network based at least in part on tracking the transportation of the items in real time.

197. The task is: extracting billing data for one or more bills from one or more sources; and 200. The computing system of claim 192, further comprising automatically inputting the billing data extracted from the one or more sources into an accounting system.

198. 200. The computing system of claim 192, wherein the operations further comprise rendering a user interface that enables a user to visually design an automation workflow.

199. 200. The computing system of claim 192, wherein the operations further comprise rendering a user interface that allows a user to manage an automation process.

200. The operation Creating a plurality of AI-based learning models; and selecting a plurality of application programming interfaces for integrating the plurality of AI models into one or more automation workflows; 200. The computing system of claim 192, further comprising: rendering a user interface that enables a user to:

201. The operation Tracking automation performance; and generating a custom dashboard based at least in part on said automation performance; 200. The computing system of claim 192, further comprising: rendering a user interface that enables a user to:

202. The set of value chain network entities comprises:

200. The computing system of claim 192, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

203. 200. The computing system of claim 192, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the set of results of the value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

204. 193. The computing system of claim 192, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

205. [ML-driven Digital Twin for Value Chain Network Control Tower] 1. A computer-implemented method comprising: receiving, via a value chain network digital twin, information related to a value chain network, the information including a virtual representation of multiple relationships between physical data items of the value chain network, and the information being dynamic, real-time, and time-series; Providing information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of a set of operating data of value chain network entities to classify at least one of an operating state, a fault state, an operating flow, or a behavior of a value chain network, and wherein at least one member of the set of AI based learning models is trained with the training dataset to determine a task to be completed for the value chain network upon receiving the classification of the at least one of the operating state, the fault state, the operating flow, or the behavior; and providing at least one of instructions for performing the task in the value chain network digital twin and recommendations for performing the task in the value chain network digital twin.

206. 206. The computer-implemented method of claim 205, further comprising rendering one of a virtual reality (VR), an augmented reality (AR), a mixed reality (MR), or a disappearing reality (DR) environment for a user to interact with a sensor-based virtual representation of the plurality of relationships between the physical data items of the value chain network.

207. The information is Inbound prepaid transportation from a supplier linked to the order, or Inventory entering networks related to said value chain network; 206. The computer-implemented method of claim 205, wherein the method includes real-time data relating to one of:

208. 206. The computer-implemented method of claim 205, wherein receiving the information related to the value chain network includes receiving sensor data indicative of inbound and outbound transportation status.

209. 206. The computer-implemented method of claim 205, further comprising generating a simulation for the value chain network digital twin, wherein the simulation for the value chain network digital twin is generated using a graph neural network (GNN).

210. 206. The computer-implemented method of claim 205, further comprising generating an optimization for the value chain network digital twin, wherein the simulation for the value chain network digital twin is generated using a graph neural network (GNN).

211. 206. The computer-implemented method of claim 205, wherein a robot operating system implements the value chain network digital twin.

212. 206. The computer-implemented method of claim 205, wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, each digital twin in the one or more sets including an embedded marketplace for digital twin simulations.

213. 206. The computer-implemented method of claim 205, wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, each digital twin in the one or more sets including an embedded marketplace for one of an artificial intelligence-based learning model or an artificial intelligence-based algorithm.

214. 206. The computer-implemented method of claim 205, wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, each digital twin in the one or more sets including an embedded marketplace for data.

215. The set of value chain network entities comprises:

206. The computer-implemented method of claim 205, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

216. 206. The computer-implemented method of claim 205, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

217. 206. The computer-implemented method of claim 205, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

218. 1. A computing system including one or more processors and one or more memories, wherein the one or more processors and the one or more memories: receiving, via a value chain network digital twin, information related to a value chain network, the information including a virtual representation of multiple relationships between physical data items of the value chain network, and the information being dynamic, real-time, and time-series; Providing information to a set of artificial intelligence (AI) based learning models, wherein at least one member of the set of AI based learning models is trained with a training dataset of a set of operating data of value chain network entities to classify at least one of an operating state, a fault state, an operating flow, or a behavior of a value chain network, and wherein at least one member of the set of AI based learning models is trained with the training dataset to determine a task to be completed for the value chain network upon receiving the classification of the at least one of the operating state, the fault state, the operating flow, or the behavior; and providing at least one of instructions for performing the tasks in the value chain network digital twin and recommendations for performing the tasks in the value chain network digital twin; 1. A computing system configured to perform operations including:

219. 219. The computing system of claim 218, wherein the operations further include rendering one of a virtual reality (VR) environment, an augmented reality (AR) environment, a mixed reality (MR) environment, or a disappearing reality (DR) environment for a user to interact with a sensor-based virtual representation of the plurality of relationships between the physical data items of the value chain network.

220. The information is Inbound prepaid transportation from a supplier linked to the order, or Inventory entering networks related to said value chain network; 219. The computing system of claim 218, further comprising real-time data relating to one of:

221. 219. The computing system of claim 218, wherein receiving the information related to the value chain network includes receiving sensor data indicative of inbound and outbound transportation status.

222. 219. The computing system of claim 218, wherein the operations further include generating a simulation for the value chain network digital twin, wherein the simulation for the value chain network digital twin is generated using a graph neural network (GNN).

223. 219. The computing system of claim 218, wherein the operations further include generating an optimization for the value chain network digital twin, wherein the simulation for the value chain network digital twin is generated using a graph neural network (GNN).

224. 219. The computing system of claim 218, wherein a robot operating system implements the value chain network digital twin.

225. 219. The computing system of claim 218, wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, each digital twin in the one or more sets including an embedded marketplace for digital twin simulations.

226. 219. The computing system of claim 218, wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, each digital twin in the one or more sets including an embedded marketplace for one of an artificial intelligence-based learning model or an artificial intelligence-based algorithm.

227. 219. The computing system of claim 218, wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, each digital twin in the one or more sets including an embedded marketplace for data.

228. The set of value chain network entities comprises:

219. The computing system of claim 218, comprising at least one of a product, a supplier, a producer, a manufacturer, a retailer, a business, an owner, an operator, an operating facility, a customer, a consumer, a worker, a mobile device, a wearable device, a distributor, a reseller, a supply chain infrastructure facility, a supply chain process, a logistics process, a reverse logistics process, a demand forecasting process, a demand management process, a demand aggregation process, a machine, a ship, a barge, a warehouse, a seaport, an airport, an airway, a waterway, a road, a railway, a bridge, a tunnel, an online retailer, an e-commerce site, a demand factor, a supply factor, a distribution system, a floating asset, an origin, a destination, a storage location, a point of use, a network, an information technology system, a software platform, a distribution center, a fulfillment center, a container, a container handling facility, customs, export control, border control, a drone, a robot, a robotic handling system, a 3D printer, a vehicle, an autonomous vehicle, a material handling facility, a waterway, or a port infrastructure facility.

229. 219. The computing system of claim 218, wherein the set of AI-based learning models includes at least one of a transformer model, a convolutional neural network, a deep learning model trained on the resulting set of the value chain network entities, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

230. 219. The computing system of claim 218, wherein the training data set for the set of AI-based learning models includes one of the set of objects or events labeled to classify the set of objects or events according to a classification taxonomy including at least one of the operating state, the fault state, the operating flow, or the behavior.

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