US20210133669A1 - Control tower and enterprise management platform with robotic process automation layer to automate actions for subset of applications benefitting value chain network entities - Google Patents

Control tower and enterprise management platform with robotic process automation layer to automate actions for subset of applications benefitting value chain network entities Download PDF

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US20210133669A1
US20210133669A1 US17/112,459 US202017112459A US2021133669A1 US 20210133669 A1 US20210133669 A1 US 20210133669A1 US 202017112459 A US202017112459 A US 202017112459A US 2021133669 A1 US2021133669 A1 US 2021133669A1
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management
role
data
facilities
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Charles Howard Cella
Richard Spitz
Teymour S. EL-TAHRY
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Strong Force VCN Portfolio 2019 LLC
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Strong Force VCN Portfolio 2019 LLC
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Priority claimed from PCT/US2020/059227 external-priority patent/WO2021092263A1/en
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Publication of US20210133669A1 publication Critical patent/US20210133669A1/en
Assigned to STRONG FORCE VCN PORTFOLIO 2019, LLC reassignment STRONG FORCE VCN PORTFOLIO 2019, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CARDNO, ANDREW, BLIVEN, Brent, DOBROWITSKY, Joshua, PARENTI, Jenna, SPITZ, RICHARD, CELLA, Charles Howard, EL-TAHRY, Teymour S.
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Definitions

  • the present disclosure relates to information technology methods and systems for management of value chain network entities, including supply chain and demand management entities.
  • the present disclosure also relates to the field of enterprise management platforms, more particularly involving data management, artificial intelligence, network connectivity and digital twins.
  • Orders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1 , where suppliers 122 in various supply environments 160 , operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order.
  • the product 130 was passed through the supply chain, being conveyed and stored via various hauling facilities 138 and distribution facilities 134 , such as warehouses 132 , fulfillment centers 112 and delivery systems 114 , such as trucks and other vehicles, trains, and the like.
  • various hauling facilities 138 and distribution facilities 134 such as warehouses 132 , fulfillment centers 112 and delivery systems 114 , such as trucks and other vehicles, trains, and the like.
  • maritime facilities and infrastructure such as ships, barges, docks and ports provided transport over waterways between the points of origin 102 and one or more destinations 104 .
  • IoT Internet of Things
  • wearable technologies that provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors that can track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and many others.
  • wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers.
  • organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology
  • organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets.
  • an information technology system may include a cloud-based management platform with a micro-services architecture; a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities; and a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use.
  • an information technology system may include a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces for accessing and configuring features of the platform; a set of network connectivity facilities for enabling a set of value chain network entities to connect to the platform; a set of adaptive intelligence facilities for automating a set of capabilities of the platform; a set of data storage facilities for storing data collected and handled by the platform; and a set of monitoring facilities for monitoring the value chain network entities; wherein the platform hosts a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin of a product of the enterprise to a point of customer use.
  • an information technology system includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform; a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform; a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform; a set of data storage facilities that are configured to store data collected and handled by the platform, wherein the data is related to at least one of the value chain network entities and the features of the platform; and a set of monitoring facilities that are configured to monitor the value chain network entities; wherein the platform is configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.
  • the set of interfaces includes at least one of a demand management interface and a supply chain management interface.
  • the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence facilities includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of at least one value chain network entity of the value chain network entities controlled by the enterprise. In embodiments, the set of adaptive intelligence includes a smart contract system that is configured to automate a set of interactions among the value chain network entities.
  • the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses a graph database representing a set of hierarchical relationships of the value chain network entities. In embodiments, the set of monitoring facilities includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by the enterprise. In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications, and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.
  • the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.
  • the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.
  • the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.
  • the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.
  • the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search,
  • an information technology system includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of
  • the unified set of robotic process automation systems automate a process selected from the group consisting of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a set of products for compatibility, configuration of a request
  • one of the processes automated by the robotic process automation system involves selection of a quantity of product for an order. In embodiments, one of the processes automated by the robotic process automation system involves selection of a carrier for a shipment. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a vendor for a component. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a vendor for a finished goods order. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a variation of a product for marketing. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of an assortment of goods for a shelf. In embodiments, wherein one of the processes automated by the robotic process automation system involves determination of a price for a finished good.
  • one of the processes automated by the robotic process automation system involves configuration of a service offer related to a product. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product bundle. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product kit. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product package. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product display. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product image. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a product description. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a website navigation path related to a product.
  • one of the processes automated by the robotic process automation system involves determination of an inventory level for a product. In embodiments, wherein one of the processes automated by the robotic process automation system involves selection of a logistics type. In embodiments, wherein one of the processes automated by the robotic process automation system involves configuration of a schedule for product delivery. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a logistics schedule. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a set of inputs for machine learning. In embodiments, one of the processes automated by the robotic process automation system involves preparation of product documentation. In embodiments, one of the processes automated by the robotic process automation system involves preparation of disclosures about a product.
  • one of the processes automated by the robotic process automation system involves configuration of a product for a set of local requirements. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a set of products for compatibility. In embodiments, one of the processes automated by the robotic process automation system involves configuration of a request for proposals. In embodiments, one of the processes automated by the robotic process automation system involves ordering of equipment for a warehouse. In embodiments, one of the processes automated by the robotic process automation system involves ordering of equipment for a fulfillment center. In embodiments, one of the processes automated by the robotic process automation system involves classification of a product defect in an image. In embodiments, one of the processes automated by the robotic process automation system involves inspection of a product in an image. In embodiments, one of the processes automated by the robotic process automation system involves inspection of product quality data from a set of sensors. In embodiments, one of the processes automated by the robotic process automation system involves inspection of data from a set of onboard diagnostics on a. product.
  • one of the processes automated by the robotic process automation system involves inspection of diagnostic data from an Internet of Things system. In embodiments, one of the processes automated by the robotic process automation system involves review of sensor data from environmental sensors in a set of supply chain environments. In embodiments, one of the processes automated by the robotic process automation system involves selection of inputs for a digital twin. In embodiments, one of the processes automated by the robotic process automation system involves selection of outputs from a digital twin. In embodiments, one of the processes automated by the robotic process automation system involves selection of visual elements for presentation in a digital twin. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of delay in a supply chain.
  • one of the processes automated by the robotic process automation system involves diagnosis of sources of scarcity in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of congestion in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of cost overruns in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves diagnosis of sources of product defects in a supply chain. In embodiments, one of the processes automated by the robotic process automation system involves prediction of maintenance requirements in supply chain infrastructure.
  • the set of demand management applications, supply chain applications, intelligent product applications and enterprise resource management applications are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management,
  • the set of interfaces includes at least one of a demand management interface and a supply chain management interface.
  • the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities includes a robotic process automation system. In embodiments, the set of adaptive intelligence facilities includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a smart contract system that is configured to automate a set of interactions among a set of value chain network entities.
  • the set of data storage facilities uses a distributed data architecture. In embodiments, the set of data storage facilities uses a blockchain. In embodiments, the set of data storage facilities uses a distributed ledger. In embodiments, the set of data storage facilities uses graph database representing a set of hierarchical relationships of the value chain network entities. In embodiments, the set of monitoring facilities includes an Internet of Things monitoring system. In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise. In embodiments, the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.
  • the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.
  • the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.
  • the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.
  • the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.
  • the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search,
  • an information technology system includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application,
  • the set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities collects information from entities selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the set of Internet of Things resources is selected from the group consisting of camera systems, lighting systems, motion sensing systems, weighing systems, inspection systems, machine vision systems, environmental sensor systems, onboard sensor systems, onboard diagnostic systems, environmental control systems, sensor-enabled network switching and routing systems, RF sensing systems, magnetic sensing systems, pressure monitoring systems, vibration monitoring systems, temperature monitoring systems, heat flow monitoring systems, biological measurement systems, chemical measurement systems, ultrasonic monitoring systems, radiography systems, LIDAR-based monitoring systems, access control systems, penetrating wave sensing systems, SONAR-based monitoring systems, radar-based monitoring systems, computed tomography systems, magnetic resonance imaging systems, and network monitoring systems.
  • the set of Internet of Things resources includes a set of camera systems. In embodiments, the set of Internet of Things resources includes a set of lighting systems. In embodiments, the set of Internet of Things resources includes a set of machine vision systems. In embodiments, the set of Internet of Things resources includes a set of motion sensing systems.
  • the set of Internet of Things resources includes a set of weighing systems. In embodiments, the set of Internet of Things resources includes a set of inspection systems. In embodiments, the set of Internet of Things resources includes a set of environmental sensor systems. In embodiments, the set of Internet of Things resources includes a set of onboard sensor systems. In embodiments, the set of Internet of Things resources includes a set of onboard diagnostic systems. In embodiments, the set of Internet of Things resources includes a set of environmental control systems. In embodiments, the set of Internet of Things resources includes a set of sensor-enabled network switching and routing systems. In embodiments, the set of Internet of Things resources includes a set of RF sensing systems.
  • the set of Internet of Things resources includes a set of magnetic sensing systems. In embodiments, the set of Internet of Things resources includes a set of pressure monitoring systems. In embodiments, the set of Internet of Things resources includes a set of vibration monitoring systems. In embodiments, the set of Internet of Things resources includes a set of temperature monitoring systems. In embodiments, the set of Internet of Things resources includes a set of heat flow monitoring systems. In embodiments, the set of Internet of Things resources includes a set of biological measurement systems. In embodiments, the set of Internet of Things resources includes a set of chemical measurement systems. In embodiments, the set of Internet of Things resources includes a set of ultrasonic monitoring systems. In embodiments, the set of Internet of Things resources includes a set of radiography systems.
  • the set of Internet of Things resources includes a set of LIDAR-based monitoring systems. In embodiments, the set of Internet of Things resources includes a set of access control systems. In embodiments, the set of Internet of Things resources includes a set of penetrating wave sensing systems. In embodiments, the set of Internet of Things resources includes a set of SONAR-based monitoring systems. In embodiments, the set of Internet of Things resources includes a set of radar-based monitoring systems. In embodiments, the set of Internet of Things resources includes a set of computed tomography systems. In embodiments, the set of Internet of Things resources includes a set of magnetic resonance imaging systems. In embodiments, the set of Internet of Things resources includes a set of network monitoring systems. In embodiments, the set of interfaces includes at least one of a demand management interface and a supply chain management interface.
  • the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management
  • the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, wherein the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system.
  • the set of adaptive intelligence includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise.
  • the set of adaptive intelligence facilities includes a smart contract system that is configured to automate a set of interactions among a set of value chain network entities.
  • the set of data storage facilities uses a distributed data architecture.
  • the set of data storage facilities uses a blockchain.
  • the set of data storage facilities uses a distributed ledger.
  • the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities.
  • the set of monitoring includes an Internet of Things monitoring system.
  • the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.
  • the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications.
  • the set of applications includes an asset management application.
  • the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the platform manages a set of demand factors, a set of supply factors and a set of supply chain infrastructure facilities.
  • the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.
  • the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.
  • the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.
  • the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search,
  • an information technology system includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application,
  • the robotic process automation layer automates a process selected from the group consisting of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a set of products for compatibility, configuration of a request for proposals,
  • one of the actions automated by the robotic process automation layer involves selection of a quantity of product for an order. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a carrier for a shipment. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a vendor for a component. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a vendor for a finished goods order. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a variation of a product for marketing. In embodiments, one of the actions automated by the robotic process automation layer involves selection of an assortment of goods for a shelf. In embodiments, one of the actions automated by the robotic process automation layer involves determination of a price for a finished good.
  • one of the actions automated by the robotic process automation layer involves configuration of a service offer related to a product. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of product bundle. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product kit. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product package. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product display. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product image. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product description.
  • one of the actions automated by the robotic process automation layer involves configuration of a website navigation path related to a product. In embodiments, one of the actions automated by the robotic process automation layer involves determination of an inventory level for a product. In embodiments, one of the actions automated by the robotic process automation layer involves selection of a logistics type. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a schedule for product delivery. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a logistics schedule. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a set of inputs for machine learning. In embodiments, one of the actions automated by the robotic process automation layer involves preparation of product documentation.
  • one of the actions automated by the robotic process automation layer involves preparation of disclosures about a product. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a product for a set of local requirements. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a set of products for compatibility. In embodiments, one of the actions automated by the robotic process automation layer involves configuration of a request for proposals. In embodiments, one of the actions automated by the robotic process automation layer involves ordering of equipment for a warehouse. In embodiments, one of the actions automated by the robotic process automation layer involves ordering of equipment for a fulfillment center. In embodiments, one of the actions automated by the robotic process automation layer involves classification of a product defect in an image.
  • one of the actions automated by the robotic process automation layer involves inspection of a product in an image. In embodiments, one of the actions automated by the robotic process automation layer involves inspection of product quality data from a set of sensors. In embodiments, one of the actions automated by the robotic process automation layer involves inspection of data from a set of onboard diagnostics on a. product. In embodiments, one of the actions automated by the robotic process automation layer involves inspection of diagnostic data from an Internet of Things system. In embodiments, one of the actions automated by the robotic process automation layer involves review of sensor data from environmental sensors in a set of supply chain environments. In embodiments, one of the actions automated by the robotic process automation layer involves selection of inputs for a digital twin.
  • one of the actions automated by the robotic process automation layer involves selection of outputs from a digital twin. In embodiments, one of the actions automated by the robotic process automation layer involves selection of visual elements for presentation in a digital twin. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of delay in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of scarcity in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of congestion in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of cost overruns in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves diagnosis of sources of product defects in a supply chain. In embodiments, one of the actions automated by the robotic process automation layer involves prediction of maintenance requirements in supply chain infrastructure.
  • the set of interfaces includes at least one of a demand management interface and a supply chain management interface.
  • the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management
  • the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system.
  • the set of adaptive intelligence facilities includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise.
  • the set of adaptive intelligence facilities includes a smart contract system for automating a set of interactions among a set of value chain network entities.
  • the set of data storage facilities uses a distributed data architecture.
  • the set of data storage facilities uses a blockchain.
  • the set of data storage facilities uses a distributed ledger.
  • the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities.
  • the set of monitoring facilities includes an Internet of Things monitoring system.
  • the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.
  • the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications. In embodiments, the set of applications includes an asset management application.
  • the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.
  • the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.
  • the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.
  • the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.
  • the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search,
  • an information technology system includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera
  • the set of interfaces includes at least one of a demand management interface and a supply chain management interface.
  • the set of applications is at least one of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications that are selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit
  • the set of network connectivity facilities includes a 5G network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes an Internet of Things system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a cognitive networking system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of network connectivity facilities includes a peer-to-peer network system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes an edge intelligence system deployed in a supply chain infrastructure facility operated by the enterprise. In embodiments, the set of adaptive intelligence facilities includes a robotic process automation system.
  • the set of adaptive intelligence facilities includes a self-configuring data collection system deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities includes a digital twin system representing attributes of value chain network entity controlled by the enterprise.
  • the set of adaptive intelligence facilities includes a smart contract system for automating a set of interactions among a set of value chain network entities.
  • the set of data storage facilities uses a distributed data architecture.
  • the set of data storage facilities uses a blockchain.
  • the set of data storage facilities uses a distributed ledger.
  • the set of data storage facilities uses a graph database representing a set of hierarchical relationships of value chain network entities.
  • the set of monitoring facilities includes an Internet of Things monitoring system.
  • the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.
  • the set of applications includes a set of applications of at least two types from among a set of supply chain management applications, demand management applications, intelligent product applications and enterprise resource management applications.
  • the set of applications includes an asset management application.
  • the value chain network entities are selected from the group consisting of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the platform manages a set of demand factors, a set of supply factors, and a set of supply chain infrastructure facilities.
  • the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.
  • the demand factors are factors selected from the group consisting of product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, and inferred interest factors.
  • the supply chain infrastructure facilities are facilities selected from the group consisting of ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, and border control facilities.
  • the set of applications involves a set selected from the group consisting of supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search,
  • a value chain system that provides container fleet management decisions includes a machine learning system that trains a machine-learned model that outputs a container fleet management decision given a respective set of input features relating to a specific shipping event, wherein the machine learning system trains the machine-learned model based on training data sets that define features of previous shipping events and outcomes of the shipping events; an artificial intelligence system that receives a request for container fleet management and determines a container fleet management decision based on the machine-learned model and the request; and a digital twin system that generates an environment digital twin of an environment of a container fleet and one or more container digital twins of respective containers in the container fleet, wherein the digital twin system executes a container fleet simulation based on the environment digital twin and the one or more container digital twins, issues a container fleet management request from the artificial intelligence system based on a state of the container fleet simulation; and adjusts the state of the container fleet simulation based on the container fleet management decision output by the artificial intelligence system in response to the container fleet management request.
  • the digital twin system outputs a simulation outcome to the machine-learning system, and the machine learning system reinforces the machine-learned model used to determine the container fleet management decision based on the simulation outcome.
  • the artificial intelligence system receives the container fleet management request from the digital twin system and determines the container fleet management decision based on simulation features defined in the container fleet management request, wherein the simulation features are indicative of the state of the container fleet simulation.
  • the request for container fleet management includes one or more properties of a simulated shipping event.
  • the artificial intelligence system determines the container fleet management decision based on the one or more properties of the simulated shipping event and the machine-learned model.
  • the one or more properties include a type of good being shipped.
  • the one or more properties include a source and a destination of a container.
  • the digital twin system provides outcome data to the machine-learning system, wherein the outcome data defines a simulation outcome resulting from the container fleet management decision.
  • a value chain system that provides recommendations for designing a logistics system includes a machine learning system that trains a machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the machine-learned model based on training data sets that define features of logistics systems and outcomes of the logistics systems; an artificial intelligence system that receives a request for logistics system design and determines a logistics system design recommendation based on the machine-learned model and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation and one or more physical asset digital twins of physical assets, wherein the digital twin system: executes a logistics simulation based on the logistics environment digital twin and the one or more physical asset digital twins, issues a logistics system design request from the artificial intelligence system based on a state of the logistics simulation; and adjusts the state of the logistics simulation based on the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request.
  • the digital twin system outputs a graphical representation of the environment digital twin to a display, whereby a user views the simulation via the display.
  • the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the logistics system design recommendation based on the simulation outcome.
  • the artificial intelligence system receives the request from a logistics design system that designs logistics systems, wherein the request includes one or more logistics factors corresponding to a proposed logistics solution of an organization.
  • the logistics factors include one or more of: a type of product corresponding to the proposed logistics solution, one or more features of the type of product, a location of a manufacturing site, a location of a distribution facility, a location of a warehouse, a location of a customer base, proposed expansion areas of the organization, and supply chain features.
  • the logistics design system provides outcome data relating to the logistics system design recommendation to the machine learning system, and the machine learning system reinforces the machine-learned model that are used to determine the logistics system design recommendation based on the outcome data.
  • the artificial intelligence system determines the logistics system design recommendation to minimize delay times.
  • the artificial intelligence system determines the logistics system design recommendation to comply with regulatory requirements.
  • a value chain system that designs packaging includes a machine learning system that trains a machine-learned model that outputs a packaging design recommendation given a respective set of input features relating to a specific respective packaging design, wherein the machine learning system trains the machine-learned model based on training data sets that define features of packaging designs and outcomes of the packaging designs; an artificial intelligence system that receives a request for packaging design and determines a packaging design recommendation based on the machine-learned model and the request; and a digital twin system that generates a package digital twin of a package that incorporates the packaging design recommendation, wherein the digital twin system: executes a packaging simulation based on the package digital twin; issues a packaging design request from the artificial intelligence system based on a state of the logistics simulation; and adjusts the state of the logistics simulation based on the packaging design recommendation output by the artificial intelligence system in response to the packaging design request.
  • the digital twin system outputs a graphical representation of the package digital twin to a display, whereby a user views the simulation via the display. In embodiments, the digital twin system outputs a graphical representation of the package digital twin in a graphical user interface, whereby a user edits the packaging design via the graphical user interface. In embodiments, the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the packaging design recommendation based on the simulation outcome. In embodiments, the artificial intelligence system receives the request from a packaging design system that designs packaging for physical objects, wherein the request includes one or more packaging factors corresponding to a proposed packaging design for the physical objects.
  • the packaging factors include one or more of: a type of the physical objects, dimensions of the physical objects, masses of the physical objects, and shipping methods of the physical objects.
  • the packaging design system provides outcome data relating to the packaging design recommendation to the machine learning system, and the machine learning system reinforces the machine-learned model that are used to determine the packaging design recommendation based on the outcome data.
  • the artificial intelligence system determines the packaging design recommendation to minimize damage.
  • the artificial intelligence system determines the packaging design recommendation to minimize costs.
  • the artificial intelligence system determines the packaging design recommendation to mitigate environmental impact.
  • an information technology system for leveraging digital twins in a value chain having a plurality of value chain entities
  • the information technology system includes a plurality of sensors positioned at least one of in, on, and near a set of value chain entities of the value chain entities and configured to collect sensor data related to the set of value chain entities, the sensor data being substantially real-time sensor data; and an adaptive intelligence system connected to the plurality of sensors and configured to receive the sensor data from the plurality of sensors, the adaptive intelligence system including: an artificial intelligence system configured to input the sensor data into a machine learning model such that the sensor data is used as training data for the machine learning model, and the machine learning model is configured to transform the sensor data into simulation data; and a digital twin system configured to create a digital replica of the set of value chain entities based on the simulation data, wherein the digital replica of the value chain entities is configured to be used to provide a substantially real-time representation of the value chain entities and provide a simulation of a possible future state of the value chain entities via the simulation data.
  • the machine learning model is configured to learn which types of sensor data are relevant to dynamics of each value chain entity of the value chain entities and simulation thereof. In embodiments, the machine learning model is configured to make suggestions to a user of the information technology system via an interface regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system. In embodiments, the machine learning model is configured to prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof.
  • a value chain network management platform includes a machine learning system that trains one or more machine-learned models to output one or more e-commerce recommendations to a value chain network customer via an interface using training data that includes product features and outcomes; and an artificial intelligence system that receives a request for e-commerce from an e-commerce system, wherein the artificial intelligence is configured to determine and generate an e-commerce recommendation based on the one or more machine-learned models and the request, and the artificial intelligence is configured to leverage one or more product digital twins and one or more customer digital twins to execute a simulation based on the one or more customer digital twins, the one or more product digital twins, and the e-commerce recommendation.
  • the machine learning system integrates with a model interpretability system, and wherein the model interpretability system is configured to implement Testing with Concept Activation Vectors (TCAV) functionality, whereby the model interpretability facilitates learning of human-interpretable concepts by the machine-learned model.
  • the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more customer profile digital twins.
  • a value chain network management platform includes a machine learning system that trains one or more machine-learned models to output one or more risk management decisions using training data that includes component features and outcomes; and an artificial intelligence system that receives a request for risk management from a risk management system, wherein the artificial intelligence system is configured to determine and generate a risk management decision based on the one or more machine-learned models and the request, and the artificial intelligence system is configured to leverage one or more component digital twins and one or more environment digital twins to execute a simulation based on the one or more component digital twins, the one or more environment digital twins, and the risk management decision.
  • the risk management decision relates to a condition of a component.
  • the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more components.
  • an information technology system includes a value chain network management platform having an asset management application associated with maritime assets, wherein the platform comprises a data handling layer including data sources containing information used to populate a training set based on a set of maritime activities of one or more of the maritime assets and at least one of design outcomes, parameters, and data associated with the one or more of the maritime assets; an artificial intelligence system that is configured to learn on the training set collected from the data sources, wherein the artificial intelligence system is configured to simulate one or more attributes of the one or more of the maritime assets, and the artificial intelligence system is configured to generate one or more sets of recommendations for a change in the one or more attributes based on the training set collected from the data sources; a digital twin system that is configured to provide for visualization of a digital twin of the one or more of the maritime assets including detail generated by the artificial intelligence system of the one or more attributes in combination with the one or more generated sets of recommendations.
  • the maritime assets include one or more container ships, and wherein the digital twin system further provides for visualization of the digital twin of the one or more container ships including the one or more attributes in combination with one or more of the sets of recommendations associated with the container ships.
  • the maritime assets include one or more barges, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the barges including the one or more attributes in combination with one or more of the sets of recommendations associated with the barges.
  • the maritime assets include one or more components of a port infrastructure installed on or adjacent to land, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of port infrastructure including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of port infrastructure.
  • the maritime assets also include a container ship moored to a component of the port infrastructure.
  • the maritime assets include one or more moored navigation units deployed on water.
  • the maritime assets include one or more ships each connected to a barge.
  • the maritime assets are associated with a real-world maritime port, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of the real-world maritime port including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of the real-world maritime port.
  • the maritime assets are associated with a real-world shipyard, and wherein the digital twin system further provides for visualization of the digital twin of one or more of the components of the real-world shipyard including the one or more attributes in combination with one or more of the sets of recommendations associated with the components of the real-world shipyard.
  • the digital twin of one or more of the maritime assets is a floating asset twin associated with a ship.
  • the floating asset twin is configured to provide for visualization of a navigation course of the ship relative to a planned course of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the navigation course of the ship.
  • the floating asset twin is configured to provide for visualization of an engine performance of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the engine performance of the ship.
  • the visualization of the engine performance includes an emissions profile of the ship.
  • the floating asset twin is configured to provide for visualization of a hull integrity of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in maintenance of the hull of the ship. In embodiments, the floating asset twin is configured to provide for visualization of in-situ hydrodynamic changes to a portion of a hull disposed below a water line of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in a hydrodynamic surface to change performance of the ship. In embodiments, the floating asset twin is configured to determine a schedule for the change to the hydrodynamic surface of the hull disposed below the waterline of the ship to improve fuel efficiency based on known routes of travel and weather patterns.
  • the floating asset twin is configured to provide visualizations of in-situ aerodynamic changes to a portion of a hull disposed above a water line of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in an aerodynamic surface to change performance of the ship.
  • the floating asset twin is configured to determine a schedule for the change to the aerodynamic surface disposed above the waterline of the ship to improve fuel efficiency using known routes of travel and historical weather patterns.
  • the floating asset twin is configured to provide visualizations of extendable buoyant members from a hull of the ship to improve stability during certain maneuvers of the ship and one or more of the sets of recommendations from the artificial intelligence system for a change in the extendable buoyant members to change performance of the ship.
  • the floating asset twin is configured to provide visualizations of a plurality of inspection points on the ship and maintenance histories associated with those inspection points. In embodiments, the floating asset twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change in maintenance of the plurality of inspection points. In embodiments, the floating asset twin is further configured to provide for visualizations of the plurality of inspection points on the ship affected by travel within a geofenced area and maintenance histories associated with those inspection points. In embodiments, the floating asset twin is further configured to provide details of a ledger of activity associated with the visualization of the plurality of inspection points on the ship affected by travel within a geofenced area and maintenance histories associated with those inspection points.
  • the floating asset twin is configured to provide for visualization for a first user of one of a navigation course of the ship and an engine performance of the ship within a first geofenced area and for visualization for a second user of one of the navigation course of the ship and the engine performance of the ship within a second different geofenced area and where transit between the first and second geofenced areas motivates a handoff of the floating asset twin of the ship between the first user and the second user.
  • the digital twin is configured to at least partially represent one or more of the maritime assets associated with an event investigation and to at least partially detail a timeline of the event investigation and the associated maritime assets. In embodiments, the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets based on the event investigation and the timeline. In embodiments, the digital twin is configured to at least partially represent one or more of the maritime assets associated with a legal proceeding and to at least partially detail at least a portion of a timeline pertinent to the legal proceeding and the associated maritime assets.
  • the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets based on the legal proceeding and the timeline.
  • the digital twin is configured to at least partially represent one or more of the maritime assets associated with at least one of a casualty forecast and a casualty report, and to at least partially detail at least a portion of a timeline pertinent to the at least one of the casualty forecast, the casualty report, and the associated maritime assets.
  • the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the associated maritime assets to reduce exposure relative to a set of previous casualty forecasts based on at least one of the casualty forecast and the casualty report, and the timeline.
  • the maritime assets include a port infrastructure facility, wherein the data collected by a value chain network management platform facilitates identifying theft at or misuse of the port infrastructure facility by correlating data between a set of data collectors for one or more physical items in the port infrastructure facility and the digital twin detailing the one or more physical items of the port infrastructure facility for the at least one of the port infrastructure facility and a set of operators.
  • the digital twin details the one or more physical items of the port infrastructure facility for at least one operator that includes a view of expected states of at least a portion of the one or more physical items.
  • the maritime assets include a shipyard, wherein the data collected by a value chain network management platform facilitates identifying theft at or misuse of one or more physical items in the shipyard by correlating data between a set of data collectors for the one or more physical items and the digital twin detailing the one or more physical items of the shipyard for the at least one of the shipyard and a set of operators.
  • the digital twin details the one or more physical items of the shipyard for at least one operator that includes a view of expected states of at least a portion of the one or more physical items.
  • the artificial intelligence system determines a set of geofence parameters, and wherein the digital twin provides further visualization of at least one geofence that integrates representation of a set of the maritime assets with a representation of a maritime environment adjacent to the geofence.
  • the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the set of maritime assets based on the visualization of the at least one geofence.
  • the maritime assets are ships capable of carrying cargo
  • the artificial intelligence system determines a set of geofence parameters
  • the digital twin provides further visualization of at least one geofence that integrates representation of the ships capable of carrying cargo with a representation of a maritime environment.
  • the digital twin is further configured to provide one or more of the sets of recommendations from the artificial intelligence system for a change of one of the attributes of the ships capable of carrying cargo based on the visualization of the at least one geofence.
  • an information technology system having a management platform includes a user interface that provides a set of adaptive intelligence systems that provide coordinated artificial intelligence for a set of demand management applications and a set of supply chain applications for a category of goods by determining relationships among demand management and supply chain applications based on inputs used by the applications and results produced by the applications; and a set of artificial intelligence systems as part of the set of adaptive intelligence systems that provide coordinated intelligence for the set of demand management applications and the set of supply chain applications for the category of goods by determining a temporal prioritization of demand management application outputs that impact control of supply chain applications so as to meet a temporal demand for at least one of the goods in the category of goods.
  • the adaptive intelligence system facilitates coordinated artificial intelligence for the set of demand management applications or the set of supply chain applications, or both for a category of goods by processing data that is available in any of a plurality of data sources including processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System.
  • ERP Enterprise Resource Planning
  • CRM Customer Relationship Management
  • CEM Customer Experience Management
  • SLM Service Lifecycle Management
  • PLM Product Lifecycle Management
  • PLM Product Lifecycle Management
  • the set of adaptive intelligence systems provide user access to coordinated artificial intelligence capabilities for use with the sets of applications.
  • the user interface presents a set of coordinated artificial intelligence capabilities responsive to the category of goods.
  • the user interface facilitates configuring the set of adaptive intelligence systems with at least one artificial intelligence system.
  • the at least one artificial intelligence system is a hybrid artificial intelligence system.
  • the at least one artificial intelligence system comprises a hybrid neural network.
  • the set of adaptive intelligence systems that provide coordinated artificial intelligence operates on or responsive to data collected by or produced by other systems of an adaptive intelligence systems layer.
  • the set of adaptive intelligence systems that provide coordinated artificial intelligence provides coordinated intelligence for a specific operator and/or enterprise that participates in the supply chain for the category of goods.
  • the set of adaptive intelligence systems that provide coordinated artificial intelligence employs a neural network that processes at least one of demand management application outputs and supply chain application outputs to provide the coordinated intelligence.
  • the set of adaptive intelligence systems that provide coordinated artificial intelligence is configured through the user interface for at least two demand management applications selected from the list consisting of a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service
  • the set of adaptive intelligence systems that provide coordinated artificial intelligence is configured through the user interface for at least two supply chain applications selected from the list consisting of a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, and an order for components management application.
  • the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.
  • an artificial intelligence system of the adaptive intelligence systems layer operates on or responsive to data collected by or produced by other systems of the adaptive intelligence systems layer.
  • a set of artificial intelligence systems may provide coordinated intelligence for a specific operator and/or enterprise that participates in the supply chain for the category of goods.
  • the coordinated intelligence includes a portion of a set of artificial intelligence systems that employs a neural network that processes at least one of demand management application outputs and supply chain application outputs to provide the coordinated intelligence.
  • the demand management applications include at least two of a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service.
  • the supply chain applications include at least two of a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, and an order for components management application.
  • an artificial intelligence system facilitates coordinated intelligence for the sets of applications by processing data that is available in any of a plurality of data sources including processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System.
  • ERP Enterprise Resource Planning
  • CRM Customer Relationship Management
  • CEM Customer Experience Management
  • SLM Service Lifecycle Management
  • PLM Product Lifecycle Management
  • the set of adaptive intelligence systems are configured in a topology that facilitates shared adaptation capabilities among at least two adaptive intelligence systems in the set of adaptive intelligence systems.
  • the set of adaptive intelligence systems employ artificial intelligence to provision available network resources for both the set of demand management applications and for the set of supply chain applications.
  • the set of demand management applications comprises a demand planning application.
  • the set of adaptive intelligence systems employ artificial intelligence to improve at least one of the list of outputs consisting of a process output, an application output, a process outcome and an application outcome.
  • digital twin technology can present large amounts of data in a digestible format that represents salient characteristics of an item, often updated in real time or near real time as the twin is updated to reflect the current state based on a pipeline of data about a represented item. While this is helpful, current digital twin technology has its limitations due to the fact that different roles within an organization may require different information to draw their insights. For example, a CEO of an industrial facility makes decisions based on a “10,000 foot view” of the company. The CEO may review profit and loss (P&L) data, industry trends, and employee trends (e.g., employee satisfaction or employee retention rates) to make overall decisions on behalf of the organization but does not necessarily need to see the granular data points to make decisions.
  • P&L profit and loss
  • industry trends e.g., employee satisfaction or employee retention rates
  • a different user such as a CFO
  • a CTO may have no need for P&L data but may require an in-depth visualization of the processes within different manufacturing facilities to gain a better understanding of opportunities to improve process outcomes or to diagnose issues within processes, equipment or systems.
  • digital twins and other interfaces that are configured for particular roles.
  • a given role may have varying needs based on context. For example, while the CEO might focus on higher-level data for many activities, such as strategic decision making or board communications, the same CEO may find more granular, micro-scale data useful for other activities, such as when an issue is escalated from a subdivision of the organization for input.
  • context-adaptive digital twins for each role including ones that provide relevant displays and information of the right type at the right time for various situations and activities undertaken by the role.
  • an enterprise management platform integrates a set of executive digital twins that take data from an intelligent data and networking pipeline to provide role-specific features, including AI-enabled expert agent features and enhanced collaboration features, and salient views of the entities and workflows of an enterprise, thereby enabling executives to monitor and control entities and workflows to an unprecedented degree at appropriate levels of granularity and using familiar taxonomies and decision-making frameworks.
  • the present disclosure further relates to an executive control tower and enterprise management platform that is configured to provide and use a converged technology stack that includes intelligent sensing and data collection, curation and handling of data through various stages of a distributed storage, networking and connectivity pipeline (from a set of local operational environments through information technology networks to various distributed on-premises and cloud computing environments), and deployment of various application-specific and general artificial intelligence capabilities in order to enable executive control towers, including role-specific executive digital twins, that are used by executives in management of the value chain network operations of an enterprise.
  • a converged technology stack that includes intelligent sensing and data collection, curation and handling of data through various stages of a distributed storage, networking and connectivity pipeline (from a set of local operational environments through information technology networks to various distributed on-premises and cloud computing environments), and deployment of various application-specific and general artificial intelligence capabilities in order to enable executive control towers, including role-specific executive digital twins, that are used by executives in management of the value chain network operations of an enterprise.
  • a method for configuring role-based digital twins comprising: receiving, by a processing system having one or more processors, an organizational definition of an enterprise, wherein the organizational definition defines a set of roles within the enterprise; generating, by the processing system, an organizational digital twin of the enterprise based on the organizational definition, wherein the organizational digital twin is a digital representation of an organizational structure of the enterprise; determining, by the processing system, a set of relationships between different roles within the set of roles based on the organizational definition; determining, by the processing system, a set of settings for a role from the set of roles based on the determined set of relationships; linking an identity of a respective individual to the role; determining, by the processing system, a configuration of a presentation layer of a role-based digital twin corresponding to the role based on the settings of the role that is linked to the identity, wherein the configuration of the presentation layer defines a set of states that is depicted in the role-based digital twin associated with the role; determining, by the processing
  • an organizational definition may further identify a set of physical assets of the enterprise.
  • determining a set of relationships may include parsing the organizational definition to identify a reporting structure and one or more business units of the enterprise.
  • a set of relationships may be inferred from a reporting structure and a business unit.
  • a set of identities may be linked to a set of roles, wherein each identity corresponds to a respective role from the set of roles.
  • a role-based digital twin may integrate with an enterprise resource planning system that operates on the organizational digital twin that represents a set of roles in the enterprise, such that changes in an enterprise resource planning system are automatically reflected in the organizational digital twin.
  • an organizational structure may include hierarchical components, which may be embodied in a graph data structure.
  • a set of settings for the set of roles may include role-based permission settings.
  • a role-based permission setting may be based on hierarchical components defined in the organizational definition.
  • a set of settings for a set of roles may include role-based preference settings.
  • a role-based preference setting may be configured based on a set of role-specific templates.
  • a set of templates may include at least one of a CEO template, a COO template, a CFO template, a counsel template, a board member template, a CTO template, a chief marketing officer template, an information technology manager template, a chief information officer template, a chief data officer template, an investor template, a customer template, a vendor template, a supplier template, an engineering manager template, a project manager template, an operations manager template, a sales manager template, a salesperson template, a service manager template, a maintenance operator template, and a business development template.
  • a set of settings for the set of roles may include role-based taxonomy settings.