Systems, methods, kits, and apparatus for digital product network systems and biology-based value chain networks

Abstract

A digital product network system generally includes a set of digital products, each having a product memory, a product network interface, and a product processor programmed with product instructions; a product network control tower, having a control tower memory, a control tower network interface, and a control tower processor programmed with control tower instructions; and a digital twin system defined at least in part by at least one of the product instructions or the control tower instructions to encode a set of digital twins representing the set of digital products.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Application No. 63 / 176,198, filed April 16, 2021, U.S. Provisional Application No. 63 / 282,507, filed November 23, 2021, U.S. Provisional Application No. 63 / 299,710, filed January 14, 2022, and U.S. Provisional Application No. 63 / 302,018, filed January 21, 2022. This application claims priority to Indian Application No. IN202211008709, filed February 18, 2022. The entire disclosures of the above applications are incorporated by reference.

[0002] The present disclosure relates to information technology methods and systems for managing value chain network entities, including supply chain and demand management entities, and to the field of enterprise management platforms, and more particularly, to quantum and biologically based value chain networks. [Background technology]

[0003] Historically, many of the various categories of goods purchased and used by residential consumers, businesses, and other customers were primarily supplied in a relatively linear manner, with manufacturers and other suppliers of finished goods, parts, and other items delivering the items to shipping companies, freight forwarders, and others, who then delivered the items to warehouses for temporary storage, to retailers where the customers purchased them, or directly to the customers' locations. Manufacturers and retailers engaged in various sales and marketing activities, such as product design, shelf and advertising placement, and pricing, to stimulate and meet customer demand.

[0004] Orders for products were fulfilled by manufacturers through a supply chain such as that 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 products 130 available at origin 102 in response to orders. Products 130 were transported and stored through the supply chain via various transportation facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112, and delivery systems 114, such as trucks and other vehicles, trains, etc. Often, maritime facilities and infrastructure, such as ships, barges, docks, and ports, provided transportation via waterways between origin 102 and one or more destinations 104.

[0005] Organizations have access to nearly unlimited amounts of data. With the advent of smart connected devices, wearable technology, and the Internet of Things (IoT), the amount of data available to organizations planning, overseeing, managing, and operating their value chain networks has dramatically increased and is likely to continue to grow. For example, a manufacturing facility, warehouse, campus, or other operational environment may have hundreds or even thousands of IoT sensors providing metrics such as vibration data measuring the vibration signatures of critical machinery, temperature throughout the facility, motion sensors that can track processing capacity, asset tracking sensors and beacons to identify items, cameras and optical sensors, chemical and biological sensors, and many other metrics. Furthermore, as wearable technology becomes more prevalent, wearables have the potential to provide insights into worker movements, health indicators, physiological status, activity status, behavior, and other characteristics. Additionally, as organizations implement CRM systems, ERP systems, operations systems, information technology systems, and advanced analytics and other systems that leverage information and information technology, they gain access to an increasingly wide range of other large datasets (including datasets generated by or for the organization and third-party datasets), such as marketing data, sales data, operations data, information technology data, performance data, customer data, financial data, market data, pricing data, and supply chain data.

[0006] The existence of more data and new types of data offers many opportunities for organizations to gain competitive advantage, but also presents challenges such as complexity and volume that can overwhelm users and lead them to miss insight opportunities. There is a need for methods and systems that enable companies to not only capture data, but also transform the data into insights, and then transform those insights into informed decisions and timely execution of efficient operations.

[0007] It is becoming more common to acquire large data sets from thousands, or potentially millions, of devices (containing numerous sensors) distributed across multiple organizations in a value chain network. For example, retail stores are seeing the proliferation of RFID (Radio Frequency Identification) tags on individual items. In these and other similar situations, the sheer number of data streams can overwhelm the ability to transmit data over the network and to create effective, automated, centralized decision-making.

[0008] The proliferation of data-generating devices (e.g., sensors) has created opportunities to manage networks, such as value chain networks, with input from vast numbers of distributed points of semi-intelligent control. However, current approaches often rely on limited, centralized data collection due to bandwidth, storage, processing, and / or other limitations. Summary of the Invention

[0009] One aspect of the present disclosure relates to a method for transmitting a predictive model of a data stream from a first device to a second device. The method may include receiving, by the first device, a plurality of data values ​​of the data stream. The data values ​​may include sensor data collected from one or more sensor devices. The method may include generating, by the first device, a predictive model for predicting future data values ​​of the data stream based on the received plurality of data values. Generating the predictive model may include determining a plurality of model parameters. The method may include transmitting, by the first device, the plurality of model parameters to the second device. The method may include receiving, by the second device, the plurality of model parameters. The method may include parameterizing, by the second device, the predictive model using the plurality of model parameters. The method may include predicting, by the second device, future data values ​​of the data stream using the parameterized predictive model. In an embodiment, the parameters include vectors. In an embodiment, the vectors are motion vectors associated with a robot. In an embodiment, the future data values ​​of the data stream include one or more future predicted positions of the robot. In embodiments, the predictive model predicts inventory levels of an item, and the method further includes detecting a future shortage of the item based on the future data values. The method may further include taking action to avoid out-of-stock of the item. In embodiments, the predictive model is a behavioral analysis model. In embodiments, the future data values ​​indicate a predicted behavior of an entity. In embodiments, the predictive model is an augmentation model, and the future data values ​​correspond to an inoperative sensor. In embodiments, the predictive model is a classification model. In embodiments, the future data values ​​indicate a predicted future state of a system including one or more sensor devices. In embodiments, the sensor is an RFID sensor associated with a cargo. In embodiments, the future data values ​​indicate a future location of the cargo. In embodiments, the sensor is a security camera.In an embodiment, the data stream includes motion vectors extracted from video data captured by a security camera. In an embodiment, the sensor is a vibration sensor that measures vibrations generated by the machine. In an embodiment, the future data values ​​indicate a potential need for maintenance of the machine. The method may further include receiving, by the first device, additional data values ​​of the data stream. The method may include refining, by the first device, the predictive model using the additional data values. In an embodiment, refining the predictive model adjusts model parameters. The method may include transmitting the adjusted model parameters to a second device. The method may further include receiving, by the second device, the adjusted model parameters. The method may include reparameterizing the predictive model using the adjusted model parameters. The method may include generating additional future data values ​​using the reparameterized predictive model.

[0010] Another aspect of the present disclosure relates to a method for prioritizing predictive model data streams. The method may include receiving, by a first device, a plurality of predictive model data streams. In an embodiment, each of the predictive model data streams includes a set of model parameters for a corresponding predictive model. In an embodiment, each of the predictive models is trained to predict future data values ​​of a data source. The method may include prioritizing, by the first device, each of the plurality of predictive model data streams. The method may include selecting, by the first device, at least one of the predictive model data streams based on the corresponding priority. The method may include parameterizing, by the first device, the predictive model using the set of model parameters included in the selected predictive model stream. The method may include predicting, by the first device, future data values ​​of the data source using the parameterized predictive model. In an embodiment, the selected at least one predictive model data stream is associated with a high priority. In an embodiment, selecting includes suppressing non-selected predictive model data streams based on a priority associated with each non-selected predictive model data stream. In an embodiment, assigning a priority to each of the plurality of predictive model data streams includes determining whether each of the sets of model parameters is anomalous. In embodiments, assigning a priority to each of the plurality of predictive model data streams includes determining whether each of a set of model parameters has changed from a previous value. In embodiments, the set of model parameters includes at least one vector. In embodiments, the at least one vector includes a motion vector associated with the robot. In embodiments, the future data values ​​include one or more future predicted positions of the robot. In some embodiments, the predictive model predicts inventory levels of an item. The method may further include detecting a future shortage of the item based on the future data values. The method may include taking action to avoid running out of the item. In embodiments, the predictive model is a behavioral analysis model.In embodiments, the future data values ​​indicate predicted behavior of the entity. In some embodiments, the predictive model is an augmentation model. In embodiments, the future data values ​​correspond to an inoperable sensor. In embodiments, the predictive model is a classification model. In some embodiments, the future data values ​​indicate a predicted future state of a system including one or more sensor devices. In embodiments, the sensor is an RFID sensor associated with a load. In some embodiments, the future data values ​​indicate a future location of the load. In embodiments, the sensor is a security camera. In some embodiments, the data stream includes motion vectors extracted from video data captured by the security camera. In embodiments, the sensor is a vibration sensor that measures vibrations generated by a machine. In some embodiments, the future data values ​​indicate a potential need for maintenance on the machine.

[0011] According to some embodiments of the present disclosure, a computer-implemented method is disclosed substantially as described herein with reference to any of the examples and / or any of the accompanying drawings. According to some embodiments of the present disclosure, a computing system is disclosed including one or more processors and one or more memories configured to perform operations substantially as described herein with reference to any of the examples and / or any of the accompanying drawings. According to some embodiments of the present disclosure, a computer program product is disclosed that resides on a computer-readable storage medium having a plurality of instructions stored thereon that, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations substantially as described herein with reference to any of the examples and / or any of the accompanying drawings. According to some embodiments of the present disclosure, a device is disclosed that is configured substantially as described herein with reference to any of the examples and / or any of the accompanying drawings.

[0012] A more complete understanding of the present disclosure will be obtained from the following description and accompanying drawings, and the appended claims. All documents referenced herein are incorporated by reference. [Brief explanation of the drawings]

[0013] The accompanying drawings, which are included to provide a better understanding of this disclosure, illustrate embodiments of the disclosure and, together with the specification, serve to explain many aspects of the disclosure.

[0014] [Figure 1] FIG. 1 is a block diagram illustrating the prior art relationships of various entities and facilities in a supply chain.

[0015] [Figure 2] FIG. 2 is a block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0016] [Figure 3] FIG. 3 is another block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0017] [Figure 4] FIG. 4 is a block diagram illustrating the components and interrelationships of the systems and processes of the digital product network of FIGS. 2 and 3 according to the present disclosure.

[0018] [Figure 5] FIG. 5 is a block diagram illustrating the system and process components and interrelationships of a value chain network technology stack according to the present disclosure.

[0019] [Figure 6] FIG. 6 is a block diagram illustrating a platform and relationships for orchestrating the control of various entities in a value chain network in accordance with the present disclosure.

[0020] [Figure 7] FIG. 7 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0021] [Figure 8] FIG. 8 is a block diagram illustrating components and relationships of value chain entities managed by an embodiment of a value chain network management platform according to the present disclosure.

[0022] [Figure 9] FIG. 9 is a block diagram illustrating the network relationships of entities in a value chain network according to the present disclosure.

[0023] [Figure 10] FIG. 10 is a block diagram illustrating a set of applications supported by a unified data handling layer in a value chain network management platform according to the present disclosure.

[0024] [Figure 11] FIG. 11 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0025] [Figure 12] FIG. 12 is a block diagram illustrating components and relationships of a data storage layer in an embodiment of a value chain network management platform according to the present disclosure.

[0026] [Figure 13] FIG. 13 is a block diagram illustrating components and relationships of the adaptive intelligent system layer in an embodiment of a value chain network management platform according to the present disclosure.

[0027] [Figure 14] FIG. 14 is a block diagram illustrating providing an adaptive intelligence system for collaborative intelligence for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0028] [Figure 15] FIG. 15 is a block diagram illustrating providing a hybrid adaptive intelligence system for collaborative intelligence for a set of demand and supply applications or categories of goods in accordance with the present disclosure.

[0029] [Figure 16] FIG. 16 is a block diagram illustrating providing an adaptive intelligence system for predictive intelligence for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0030] [Figure 17] FIG. 17 is a block diagram illustrating an adaptive intelligence system for providing classification intelligence for a set of supply and demand applications for categories of goods in accordance with the present disclosure.

[0031] [Figure 18] FIG. 18 is a block diagram illustrating providing an adaptive intelligence system for generating automated control signals for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0032] [Figure 19] FIG. 19 is a block diagram illustrating training an artificial intelligence / machine learning system to generate intelligent routing recommendations for a selected value chain network in accordance with the present disclosure.

[0033] [Figure 20]FIG. 20 is a block diagram illustrating a semi-perceptual problem recognition system for recognizing pain points / problem situations in a value chain network according to the present disclosure.

[0034] [Figure 21] FIG. 21 is a block diagram illustrating a set of artificial intelligence systems operating on value chain information to enable automatic adjustment of an enterprise's value chain activities in accordance with the present disclosure.

[0035] [Figure 22] FIG. 22 is a block diagram illustrating the components and relationships involved in integrating a set of digital twins in one embodiment of a value chain network management platform according to the present disclosure.

[0036] [Figure 23] FIG. 23 is a block diagram illustrating a set of digital twins involved in an embodiment of a value chain network management platform according to the present disclosure.

[0037] [Figure 24] FIG. 24 is a block diagram illustrating components and relationships of an entity discovery and management system in an embodiment of a value chain network management platform according to the present disclosure.

[0038] [Figure 25] FIG. 25 is a block diagram illustrating components and relationships of a robotic process automation system in an embodiment of a value chain network management platform according to the present disclosure.

[0039] [Figure 26] FIG. 26 is a block diagram illustrating the components and relationships of a set of opportunity miners in one embodiment of a value chain network management platform according to the present disclosure.

[0040] [Figure 27] FIG. 27 is a block diagram illustrating components and relationships of a set of edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0041] [Figure 28] FIG. 28 is a block diagram illustrating components and relationships in one embodiment of a value chain network management platform according to the present disclosure.

[0042] [Figure 29] FIG. 29 is a block diagram illustrating additional details of components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0043] [Figure 30] FIG. 30 is a block diagram illustrating components and relationships in one embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities in accordance with the present disclosure.

[0044] [Figure 31] FIG. 31 is a block diagram illustrating the components and relationships of a unified database in one embodiment of a value chain network management platform according to the present disclosure.

[0045] [Figure 32] FIG. 32 is a block diagram illustrating components and relationships of a set of unified data collection systems in an embodiment of a value chain network management platform according to the present disclosure.

[0046] [Figure 33] FIG. 33 is a block diagram illustrating components and relationships of a set of Internet of Things monitoring systems in an embodiment of a value chain network management platform according to the present disclosure.

[0047] [Figure 34] FIG. 34 is a block diagram illustrating the components and relationships of a machine vision system and a digital twin in an embodiment of a value chain network management platform according to the present disclosure.

[0048] [Figure 35] FIG. 35 is a block diagram illustrating components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0049] [Figure 36] FIG. 36 is a block diagram illustrating additional details of components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0050] [Figure 37] FIG. 37 is a block diagram illustrating components and relationships of a set of unified adaptive intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0051] [Figure 38] FIG. 38 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a value chain system, according to some embodiments of the present disclosure.

[0052] [Figure 39] FIG. 39 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a container fleet management system, according to some embodiments of the present disclosure.

[0053] [Figure 40]FIG. 40 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a logistics design system, according to some embodiments of the present disclosure.

[0054] [Figure 41] FIG. 41 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a packaging design system, according to some embodiments of the present disclosure.

[0055] [Figure 42] FIG. 42 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a waste mitigation system, according to some embodiments of the present disclosure.

[0056] [Figure 43] FIG. 43 is a schematic diagram illustrating an example of a portion of an information technology system for value chain artificial intelligence leveraging digital twins, according to some embodiments of the present disclosure.

[0057] [Figure 44] FIG. 44 is a block diagram illustrating the components and relationships of a set of intelligent project management facilities in an embodiment of a value chain network management platform according to the present disclosure.

[0058] [Figure 45] FIG. 45 is a block diagram illustrating components and relationships of an intelligent task recommendation system in an embodiment of a value chain network management platform according to the present disclosure.

[0059] [Figure 46]FIG. 46 is a block diagram illustrating components and relationships of a routing system between nodes of a value chain network in an embodiment of a value chain network management platform according to the present disclosure.

[0060] [Figure 47] FIG. 47 is a block diagram illustrating components and relationships of a dashboard for managing a set of digital twins in an embodiment of a value chain network management platform.

[0061] [Figure 48] FIG. 48 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform using a microservices architecture.

[0062] [Figure 49] FIG. 49 is a block diagram illustrating components and relationships of an Internet of Things data collection architecture and sensor recommendation system in an embodiment of a value chain network management platform.

[0063] [Figure 50] FIG. 50 is a block diagram illustrating the components and relationships of a social data collection architecture in an embodiment of a value chain network management platform.

[0064] [Figure 51] FIG. 51 is a block diagram illustrating components and relationships of a crowdsourcing data collection architecture in an embodiment of a value chain network management platform.

[0065] [Figure 52] FIG. 52 is a perspective view illustrating an embodiment of a set of value chain network digital twins representing virtual models of a set of value chain network entities according to the present disclosure.

[0066] [Figure 53] FIG. 53 is a perspective view illustrating an embodiment of a warehouse digital twin kit system according to the present disclosure.

[0067] [Figure 54] FIG. 54 is a perspective view illustrating an embodiment of a stress test performed on a value chain network in accordance with the present disclosure.

[0068] [Figure 55] FIG. 55 is a perspective view illustrating an embodiment of a method used by a machine to detect faults and predict future failures of the machine in accordance with the present disclosure.

[0069] [Figure 56] FIG. 56 is a perspective view illustrating an embodiment of a deployment of machine twins to perform predictive maintenance on a set of machines according to the present disclosure.

[0070] [Figure 57] FIG. 57 is a schematic diagram illustrating an example of a portion of a system for a value chain customer digital twin and a customer profile digital twin, according to some embodiments of the present disclosure.

[0071] [Figure 58] FIG. 58 is a schematic diagram illustrating an example of an advertising application interfacing with an adaptive intelligent systems layer in accordance with the present disclosure.

[0072] [Figure 59] FIG. 59 is a schematic diagram illustrating an example of an e-commerce application integrated with an adaptive intelligent systems layer in accordance with the present disclosure.

[0073] [Figure 60] FIG. 60 is a schematic diagram illustrating an example of a demand management application integrated with an adaptive intelligent systems layer in accordance with the present disclosure.

[0074] [Figure 61] FIG. 61 is a schematic diagram illustrating an example of a portion of a system for value chain smart supply part digital twin according to some embodiments of the present disclosure.

[0075] [Figure 62] FIG. 62 is a schematic diagram illustrating an example of a risk management application interfacing with an adaptive intelligent systems layer in accordance with the present disclosure.

[0076] [Figure 63] FIG. 63 is a perspective view of maritime assets associated with a value chain network management platform including port infrastructure components according to the present disclosure.

[0077] [Figure 64] 64 and 65 are perspective views of maritime assets associated with a value chain network management platform including a vessel component in accordance with the present disclosure. [Figure 65] 64 and 65 are perspective views of maritime assets associated with a value chain network management platform including a vessel component in accordance with the present disclosure.

[0078] [Figure 66] FIG. 66 is a perspective view of maritime assets associated with a value chain network management platform including a barge component according to the present disclosure.

[0079] [Figure 67] FIG. 67 is a perspective view of maritime assets associated with a value chain network management platform, including those involved in maritime events, legal proceedings, and utilizing geofenced parameters, in accordance with the present disclosure.

[0080] [Figure 68]FIG. 68 is a schematic diagram illustrating an example environment of an enterprise and executive control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.

[0081] [Figure 69] FIG. 69 is a schematic diagram illustrating an example of a component set of an enterprise control tower and management platform according to some embodiments of the present disclosure.

[0082] [Figure 70] FIG. 70 is a schematic diagram illustrating an example of an enterprise data model according to some embodiments of the present disclosure.

[0083] [Figure 71] FIG. 71 is a schematic diagram illustrating examples of different types of enterprise digital twins, including executive digital twins, associated with the data, processing, and application layers of an enterprise digital twin framework according to some embodiments of the present disclosure.

[0084] [Figure 72] FIG. 72 is a schematic diagram illustrating an example of an enterprise and executive control tower and management platform, according to some embodiments of the present disclosure.

[0085] [Figure 73] FIG. 73 is a flowchart illustrating an example of operations for configuring and servicing an enterprise digital twin.

[0086] [Figure 74] FIG. 74 illustrates an example of the operation of a method for constructing a digital twin of an organization.

[0087] [Figure 75] FIG. 75 illustrates an example of the operations of a method for creating an executive digital twin.

[0088] [Figure 76] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 77] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 78] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 79]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 80] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 81] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 82]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 83] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 84] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 85]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 86] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 87] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 88]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 89] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 90] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 91]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 92] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 93] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 94]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 95] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 96] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 97]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 98] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 99] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 100]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 101] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 102] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 103]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure.

[0089] [Figure 104] FIG. 104 is a schematic diagram illustrating an example intelligence service system according to some embodiments of the present disclosure.

[0090] [Figure 105] FIG. 105 is a schematic diagram illustrating an example neural network having multiple layers, according to some embodiments of the present disclosure.

[0091] [Figure 106] FIG. 106 is a schematic diagram illustrating an exemplary convolutional neural network (CNN) according to some embodiments of the present disclosure.

[0092] [Figure 107] FIG. 107 is a schematic diagram illustrating an example neural network for implementing natural language processing according to some embodiments of the present disclosure.

[0093] [Figure 108] FIG. 108 is a schematic diagram illustrating an example of a reinforcement learning-based approach for performing one or more tasks by a mobile system, according to some embodiments of the present disclosure.

[0094] [Figure 109] FIG. 109 is a schematic diagram illustrating an exemplary physical direction determination chip according to some embodiments of the present disclosure.

[0095] [Figure 110] FIG. 110 is a schematic diagram illustrating an exemplary network reinforcement chip according to some embodiments of the present disclosure.

[0096] [Figure 111] FIG. 111 is a schematic diagram illustrating an exemplary diagnostic chip according to some embodiments of the present disclosure.

[0097] [Figure 112] FIG. 112 is a schematic diagram illustrating an exemplary governance chip according to some embodiments of the present disclosure.

[0098] [Figure 113] FIG. 113 is a schematic diagram illustrating an exemplary prediction, classification, and recommendation chip according to some embodiments of the present disclosure.

[0099] [Figure 114] FIG. 114 is a perspective view illustrating an example environment of an autonomous additive manufacturing platform according to some embodiments of the present disclosure.

[0100] [Figure 115] FIG. 115 is a schematic diagram illustrating an exemplary implementation of an autonomous additive manufacturing platform for automating and optimizing digital manufacturing workflows for metal additive manufacturing, according to some embodiments of the present disclosure.

[0101] [Figure 116] FIG. 116 is a flow diagram illustrating the optimization of different parameters of an additive manufacturing process according to some embodiments of the present disclosure.

[0102] [Figure 117] FIG. 117 is a schematic diagram illustrating a system for learning with data from an autonomous additive manufacturing platform to train an artificial learning system that uses a digital twin for classification, prediction, and decision-making, according to some embodiments of the present disclosure.

[0103] [Figure 118] FIG. 118 is a schematic diagram illustrating an example implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network, according to some embodiments of the present disclosure.

[0104] [Figure 119] FIG. 119 is a schematic diagram illustrating an exemplary embodiment of an autonomous additive manufacturing platform for automating and managing manufacturing functions and sub-processes, including process and material selection, hybrid part workflow, feedstock blending, part design optimization, risk prediction and management, marketing and customer service, in accordance with some embodiments of the present disclosure.

[0105] [Figure 120] FIG. 120 is a perspective view of a distributed manufacturing network enabled by an autonomous additive manufacturing platform and built on a distributed ledger system, according to some embodiments of the present disclosure.

[0106] [Figure 121] FIG. 121 is a schematic diagram illustrating an example implementation of a distributed manufacturing network in which digital thread data is tokenized and stored in a distributed ledger to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network, according to some embodiments of the present disclosure.

[0107] [Figure 122] FIG. 122 is a perspective view of an example of a conventional computer vision system for creating an image of an object of interest.

[0108] [Figure 123] FIG. 123 is a schematic diagram illustrating an example implementation of a dynamic vision system for dynamically learning object concepts related to objects of interest, according to some embodiments of the present disclosure.

[0109] [Figure 124] FIG. 124 is a schematic diagram illustrating an example architecture of a dynamic vision system according to some embodiments of the present disclosure.

[0110] [Figure 125] FIG. 125 is a flow diagram illustrating a method for object recognition by a dynamic vision system according to some embodiments of the present disclosure.

[0111] [Figure 126] FIG. 126 is a schematic diagram illustrating an example of a dynamic vision system for modeling, simulating, and optimizing various optical, mechanical, design, and lighting parameters of the dynamic vision system according to some embodiments of the present disclosure.

[0112] [Figure 127] FIG. 127 is a schematic diagram illustrating an example implementation of a dynamic vision system with detailed views of various components, along with integration of the dynamic vision system with one or more third-party systems, according to some embodiments of the present disclosure.

[0113] [Figure 128] FIG. 128 is a schematic diagram illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0114] [Figure 129] FIG. 129 is a schematic diagram illustrating example configurations of multi-purpose and special-purpose robots according to some embodiments of the present disclosure.

[0115] [Figure 130] FIG. 130 is a schematic diagram illustrating an example of a platform-level intelligence layer of a fleet management platform according to some embodiments of the present disclosure.

[0116] [Figure 131]FIG. 131 is a schematic diagram illustrating an example configuration of an intelligence layer according to some embodiments of the present disclosure.

[0117] [Figure 132] FIG. 132 is a schematic diagram illustrating an example security framework according to some embodiments of the present disclosure.

[0118] [Figure 133] FIG. 133 is a schematic diagram illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0119] [Figure 134] FIG. 134 is a schematic diagram illustrating an example data flow of a job configuration system according to some embodiments of the present disclosure.

[0120] [Figure 135] FIG. 135 is a schematic diagram illustrating an example data flow of a fleet operations system according to some embodiments of the present disclosure.

[0121] [Figure 136] FIG. 136 is a schematic diagram illustrating an example job analysis system and task definition system and example data flow thereof, according to some embodiments of the present disclosure.

[0122] [Figure 137] FIG. 137 is a schematic diagram illustrating an example fleet configuration system and its example data flow, according to some embodiments of the present disclosure.

[0123] [Figure 138] FIG. 138 is a schematic diagram illustrating an example workflow definition system and its example data flow, according to some embodiments of the present disclosure.

[0124] [Figure 139]FIG. 139 is a schematic diagram illustrating an example configuration of a multipurpose robot and its components according to some embodiments of the present disclosure.

[0125] [Figure 140] FIG. 140 is a schematic diagram illustrating an example architecture of a robotic control system according to some embodiments of the present disclosure.

[0126] [Figure 141] FIG. 141 is a schematic diagram illustrating an example architecture of a robotic control system 12150 that utilizes data from multiple sensors in a vision and sensing system, according to some embodiments of the present disclosure.

[0127] [Figure 142] FIG. 142 is a schematic diagram illustrating an example of a robotic vision and sensing system according to some embodiments of the present disclosure.

[0128] [Figure 143] FIG. 143 is a schematic diagram illustrating an exemplary process performed by a multipurpose robot to harvest crops, according to some embodiments of the present disclosure.

[0129] [Figure 144] FIG. 144 is a schematic diagram illustrating an example environment for an intermodal smart container system according to some embodiments of the present disclosure.

[0130] [Figure 145] FIG. 145 is a schematic diagram illustrating an example configuration of a smart container according to some embodiments of the present disclosure.

[0131] [Figure 146] FIG. 146 is a schematic diagram illustrating an intelligence service adapted to provide intelligence services to a smart intermodal container system according to some embodiments of the present disclosure.

[0132] [Figure 147] FIG. 147 is a schematic diagram illustrating a digital twin module according to some embodiments of the present disclosure.

[0133] [Figure 148] FIG. 148 illustrates an example embodiment of a method for receiving a request to update one or more properties of a digital twin of a shipping entity and / or an environment.

[0134] [Figure 149] FIG. 149 illustrates an example embodiment of a method for updating a set of downtime cost values ​​in a smart container's digital twin, according to some embodiments of the present disclosure.

[0135] [Figure 150] FIG. 150 is a schematic diagram illustrating an example environment for a digital product network according to some embodiments of the present disclosure.

[0136] [Figure 151] FIG. 151 is a schematic diagram illustrating an example environment for a connected product according to some embodiments of the present disclosure.

[0137] [Figure 152] FIG. 152 is a schematic diagram illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0138] [Figure 153] FIG. 153 is a schematic diagram illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0139] [Fig. 154] FIG. 154 is a flow diagram illustrating a method for using product level data according to some embodiments of the present disclosure.

[0140] [Figure 155]FIG. 155 is a schematic diagram illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0141] [Figure 156] FIG. 156 is a schematic diagram illustrating an example of a smart futures contract system according to some embodiments of the present disclosure.

[0142] [Figure 157] FIG. 157 is a schematic diagram illustrating an example environment of an edge networking system according to some embodiments of the present disclosure.

[0143] [Figure 158] FIG. 158 is a schematic diagram illustrating an example environment of an edge networking system including a VCN bus according to some embodiments of the present disclosure.

[0144] [Figure 159] FIG. 159 is a schematic diagram illustrating an example environment of an edge networking system according to some embodiments of the present disclosure, including a configured device EDNW system.

[0145] [Figure 160] FIG. 160 is a schematic diagram of an example embodiment of a quantum computing service according to some embodiments of the present disclosure.

[0146] [Figure 161] FIG. 161 is a diagram illustrating quantum computing service request processing according to some embodiments of the present disclosure.

[0147] [Figure 162] FIG. 162 is a perspective view illustrating an embodiment of a biology-based value chain network system according to the present disclosure.

[0148] [Figure 163]FIG. 163 is a perspective view showing the thalamic services according to the present disclosure and how they are coordinated within the module.

[0149] [Fig. 164] FIG. 164 is a block diagram illustrating an energy system that can communicate with similar systems, subsystems, components, and value chain network management platforms, according to some embodiments of the present disclosure.

[0150] [Figure 165] FIG. 165 is a block diagram illustrating an overview of a dual-process artificial neural network system according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0151] (Detailed explanation) Over time, companies have increasingly used technology solutions to improve outcomes associated with traditional supply chains, such as those depicted in Figure 1. These solutions include software systems for forecasting and managing customer demand, RFID and asset tracking systems for tracking goods moving through the supply chain, and navigation and routing systems for improving route selection efficiency. However, several major trends are forcing manufacturers, retailers, and other companies to improve their supply chain performance. First, online and e-commerce operators, particularly Amazon®, have become the largest retail channel for many categories of goods, deploying 112 distribution and fulfillment centers across some regions, such as the United States, and housing hundreds of thousands, or even more, of product categories (SKUs), allowing customers to receive their orders the next day, or even the same day (and, in some cases, delivered to their doorsteps by drones, robots, and / or autonomous vehicles). For retailers without widely distributed fulfillment centers and warehouses, customer expectations for delivery speed are increasing pressure for supply chain efficiency and optimization. Therefore, a need remains for improved supply chain methods and systems.

[0152] Second, agile manufacturing capabilities (especially using 3D printing and robotic assembly), customer profiling techniques, and online ratings and reviews are driving customer expectations for product customization and personalization. Therefore, to stay ahead of the competition, manufacturers and retailers need to improve the methods and systems they use to understand, predict, and satisfy customer demand.

[0153] Supply chain management and demand planning and management have historically been largely separate activities that come together when demand is converted into an order and passed to the supply side for fulfillment in the supply chain. As expectations for speed and personalization increase, there is a need for methods and systems that can provide unified orchestration of demand and supply.

[0154] Paralleling these other major trends is the emergence of the Internet of Things, with several categories of such products, particularly smart home products like thermostats, lighting systems, and speakers, increasingly enabled with onboard network connectivity and processing power, often including voice-controlled intelligent agents like Alexa or Siri that can control devices, trigger specific application functions like music playback, or even place product orders. In some cases, smart products 650 even initiate orders, like a printer ordering refill cartridges. While intelligent products 650 are sometimes involved in connected systems, like an Amazon Echo product controlling a TV or a sensor-enabled thermostat or security camera connecting to a mobile device, most intelligent products still primarily engage in isolated, application-specific sets of interactions. As artificial intelligence capabilities improve and computing and networking capabilities become increasingly powerful, intelligent products will be able to address a growing number of applications. With the move to network-enabled edge devices and systems present in the supply environment 670, the demand environment 672, and all the locations, systems, and facilities that form the path of the product 1510 from the manufacturer's loading dock to the customer 662 or retailer 664 destination 612, there is a need and opportunity to dramatically improve the intelligence, control, and automation of all elements involved in supply and demand.

[0155] Value Chain Network Referring to FIG. 2, a block diagram illustrating the components and interrelationships of value chain network systems and processes is shown at 200. In an exemplary embodiment, “value chain network,” as used herein, refers to the elements and interconnections of historically separate demand management systems and processes and supply chain management systems and processes, made possible by the development and convergence of numerous diverse technologies. In an exemplary embodiment, a value chain control tower 260 (e.g., sometimes referred to herein as a “value chain network management platform,” “VCNP,” or simply the “system,” or “platform”), a big data center (e.g., big data processing 230), and associated processing capabilities receive data flows, data pools, data streams, and / or other data configurations and transmission modalities received from, for example, a digital product network 21002, directly from customers (e.g., directly connected customers 250), or other third parties 220. Communications related to market orchestration activities and communications 210, analytics 232, or any other type of input may also be utilized by a value chain control tower for demand enrichment 262, synchronized planning 234, intelligent sourcing 238, dynamic fulfillment 240, or any other smart operations informed by collaborative and adaptive intelligence, as described herein.

[0156] 3, another block diagram illustrating the components and interrelationships of value chain network systems and processes, as well as associated use cases, data processing, and related entities is shown. In an exemplary embodiment, a value chain control tower 360 can coordinate market orchestration activities 310, including, but not limited to, demand curve management 352, ecosystem synchronization 348, intelligent sourcing 344, dynamic fulfillment 350, value chain analytics 340, and / or smart supply chain operations 342. In an exemplary embodiment, the value chain control tower 360 can be connected to, communicate with, or otherwise operably coupled to an adaptive data pipeline 302 and processing facilities, which may further connect to external data sources 320 and a data handling stack 330 (e.g., value chain network technologies), which may include an intelligent, user-adaptive interface, adaptive intelligence and control 332, and / or adaptive data monitoring and storage 334, as described herein. The value chain control tower 302 may also be further connected to, in communication with, or otherwise operably coupled to additional value chain entities, including, but not limited to, the digital product network 21002, customers (e.g., designated connected customers 362), and / or other connected operations 364 and entities of the value chain network.

[0157] Digital Product Network (DPN) Referring to FIG. 4, a block diagram illustrating the components and interrelationships of systems and processes of a digital product network at 400 is shown. In an exemplary embodiment, products (including goods and services) can create and send data, such as product-level data, to communication layers and / or edge data processing facilities in a value chain network technology stack. This data can generate enriched product-level data and be combined with third-party data for further processing, modeling, or other adaptive or collaborative intelligence activities, as described herein, including, but not limited to, generating and / or simulating product and value chain use cases, for which the data can be utilized by products, product development processes, product designs, etc.

[0158] Stack View Example Referring to FIG. 5 , a block diagram illustrating the system and process components and interrelationships of a value chain network technology stack is shown at 500, which may include, but is not limited to, a presentation layer, an intelligence layer, and serverless functionality such as a platform (e.g., a development platform and a hosting platform), a data facility (e.g., data-related due to IoT and big data), and a data aggregation facility. In an exemplary embodiment, the presentation layer may include, but is not limited to, a user interface, modules for research and discovery, and modules for tracking user experience and engagement. In an exemplary embodiment, the intelligence layer may include, but is not limited to, statistical and computational methods, semantic models, analytical libraries, a development environment for analysis, algorithms, logic and rules, and machine learning. In an exemplary embodiment, the platform or value chain network technology stack may include a development environment, APIs for connectivity, cloud and / or hosting applications, and device detection. In an exemplary embodiment, the data aggregation facility or layer may include, but is not limited to, modules for data normalization for common transmission and modules for heterogeneous data collection from heterogeneous devices. In an exemplary embodiment, the data facilities or layers may include, but are not limited to, IoT and big data access, control, and collection and replacement. In an exemplary embodiment, the value chain network technology stack may be further associated with additional data sources and / or technology enablers.

[0159] Value Chain Orchestration from a Command Platform FIG. 6 illustrates a connected value chain network 668 in which a value chain network management platform 604 (sometimes referred to herein as a “value chain control tower,” “VCNP,” or simply “system,” or “platform”) organizes various factors involved in planning, monitoring, controlling, and optimizing the various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, and logistics and distribution factors. By virtue of the unified platform 604 for monitoring and managing supply and demand factors and status information (e.g., quality and status, planning, ordering and confirmation, and / or tracking and tracing), demand factors can be shared for and among various entities (including, e.g., customers / consumers, suppliers, distributors, distribution of suppliers, etc., and production, such as producers or production facilities). The value chain network 668 includes not only the intelligent product 1510 but also all the equipment, infrastructure, personnel, and other entities involved in planning and fulfilling demand for it. The value chain network 668 can include not only the intelligent product 1510 but all the equipment, infrastructure, personnel, and other entities involved in planning and satisfying demand for it.

[0160] Value Chain Network and Value Chain Network Management Platform Referring to FIG. 7 , the value chain network 668 managed by the value chain management platform 604 may include a set of value chain network entities 652 such as, but not limited to: a product 1510, which may be an intelligent product 1510; a set of production facilities 674 involved in the production of the finished product, components, systems, subsystems, materials used in the product, etc.; various entities, activities, and other supply factors 648 involved in the supply environment 670, such as suppliers 642, points of origin 610; various entities, activities, and other supply factors 648 involved in the demand environment 672, such as suppliers 642, points of origin 610; various entities, activities and other demand factors 644, such as customers 662 (including consumers, businesses, and intermediate customers such as value-added resellers and distributors), retailers 664 (including online retailers, mobile retailers, traditional brick-and-mortar retailers, pop-up shops, etc.) located and / or operating in various destinations 612; various distribution environments 678 and distribution facilities 658, such as warehousing facilities 654, fulfillment facilities 628, and delivery systems 632, and maritime facilities 622, such as port infrastructure facilities 660, floating assets 620, and shipyards 638. In embodiments, the value chain network management platform 604 monitors, controls, and otherwise enables the management (and sometimes autonomous or semi-autonomous operation) of the processes, workflows, activities, events, and applications 630 (sometimes collectively referred to as simply "applications 630") of the extensive value chain network 668.

[0161] 7, there is shown a high-level schematic diagram of a value chain network management platform 604. The value chain network management platform 604 may include a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements that work in conjunction to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact, etc. within, or that own, operate, support, or enable one or more value chain network processes, workflows, activities, events, and / or applications 630, or that are part of, integrated with, linked to, or connected to the VCNP 604 in connection with products 1510 (which may be any category of product, such as finished goods, software products, hardware products, materials, equipment, etc.). The intelligent product 1510 may be of any category, such as a product, software product, hardware product, component product, material, equipment item, consumer packaged goods item, consumer product, food product, beverage product, household product, business product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product and / or set of related services, that may be operated by an intelligent product 1510, and in embodiments encompasses an intelligent product 1510 that enables a set of capabilities such as data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, acoustic processing systems, data storage, data integration, and / or various Internet of Things functions.

[0162] In an embodiment, the management platform 604 may include a set of data processing layers 608, each configured to provide a set of capabilities that facilitate the development and deployment of intelligence, such as to facilitate automation, machine learning, the application of artificial intelligence, intelligent transactions, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In an embodiment, the data processing layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604, via a value chain monitoring system layer 614. The value chain monitoring system layer 614 may include, integrate with, and / or work with various data collection and management systems 640, sometimes conveniently referred to as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about various data layers 624 or their services or components. In an embodiment, data processing tier 608 is configured in a topology that facilitates shared or common data storage across multiple applications and uses of platform 604 through a value chain network-oriented data storage system layer 624, sometimes referred to herein for convenience simply as data storage layer 624 or storage layer 624. As shown in FIG. 7 , data processing tier 608 may also include an adaptive intelligent systems layer 614. Adaptive intelligent systems layer 614 may include a set of data processing, artificial intelligence, and computation systems 634, which are described in more detail elsewhere in this disclosure. Data processing, artificial intelligence, and computation systems 634 may relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, model-based systems, etc.).Specifically, the data processing, artificial intelligence, and computation system 634, in some embodiments, may involve the use of recurrent networks as adaptive intelligence systems that operate on a blockchain of transactions in a supply chain to determine patterns, use with biological systems, opportunity mining (e.g., artificial intelligence systems may be used to monitor new data sources as opportunities to automatically deploy intelligence), robotic process automation (e.g., automating intelligent agents for various workflows), edge and network intelligence (e.g., involved in monitoring systems that adaptively use available RF spectrum, adaptively use available fixed network spectrum, adaptively store data based on available storage conditions, adaptively sense based on a type of context sensing), etc.

[0163] In embodiments, the data handling layer 608 may be depicted in a vertical stack or ribbon in the diagram and may represent many functionalities available to the platform 604, including storage, monitoring, and processing applications and resources, as well as combinations thereof. In embodiments, the set of capabilities in the data handling layer 608 may include a shared microservices architecture. By these examples, the set of capabilities may be deployed to provide multiple separate services or applications, which may be configured as one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within or resident in a particular application or process. In some examples, the set of capabilities may include one or more activities marshaled for the benefit of the platform. In some examples, the set of capabilities may include one or more events organized for the benefit of the platform. In embodiments, one of the set of capabilities of the platform may be deployed within at least a portion of a common architecture, such as a common architecture supporting a common data schema. In embodiments, one of the set of capabilities of the platform may be deployed within at least a portion of a common architecture that can support common storage. In embodiments, one of the set of capabilities of the platform may be deployed within at least a portion of a common architecture that can support a common monitoring system. In embodiments, one or more capability sets of the platform may be deployed within at least a portion of a common architecture that can support one or more common processing frameworks. In embodiments, the set of capabilities of the data handling layer 608 may include examples where storage functions support scalable processing capabilities, scalable monitoring systems, digital twin systems, payment interface systems, etc.By these examples, one or more software development kits may be provided by the platform along with deployment interfaces to facilitate connection and use of the capabilities of the data handling layer 608. In a further example, an adaptive intelligence system may analyze, remodel, configure, and reconfigure one or more capabilities of the data handling layer 608. In an embodiment, the platform 604 may include a common data storage schema that provides services to shipyard entity-related services and warehousing entity services, for example. There are many other applicable examples and combinations applicable to the aforementioned examples involving the many value chain entities disclosed herein. By these examples, the platform 604 may be shown to create connectivity (e.g., capability and information provision) across many value chain entities. In many examples, there are pairings (dual, triple, quadruple, etc.) of similar types of value chain entities that use one or more small capability sets of the data handling layer 608 to deploy (interact, depend, etc.) a common data schema, a common architecture, a common interface, etc. While services and capabilities may be provided to a single value chain entity, a platform can be shown to provide myriad benefits to the value chain and consumers by supporting connectivity between value chain entities and the applications used by the entities.

[0164] Platform-managed value chain network entities Referring to FIG. 8 , the value chain network management platform 604 is illustrated in association with a set of value chain entities 652 that may be managed by, integrated with, or integrated into the platform 604, and / or that may provide inputs to and / or take outputs from the platform 604, such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events, and applications 630 (collectively “applications 630” or simply “activities”). Connectivity with the value chain entities 652 may be facilitated by a set of connectivity facilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in more detail below. This may include connectivity and interface capabilities for individual services of the platform, for the data handling layer, for the platform as a whole, and / or between the value chain entities 652.

[0165] These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals, or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, including, but not limited to: machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loaders, cranes, lifts, carriers, trucks, loaders, unloaders, packers, pickers, and robotic systems, including physical robots, collaborative robots (e.g., "cobots"), drones, autonomous vehicles, software hotspots, and many others); products 650 (which may be any category of product, such as a finished product, software product, hardware product, component product, material, item of equipment, item of consumer packaged goods, consumer product, food product, beverage product, household product, business product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or set of other types of products and / or related services); value chain processes 722 (shipping process, transportation process, sea transportation process, etc.); uplink processes, inspection processes, transportation processes, loading / unloading processes, packing / unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, etc.), border control processes, port-related processes, software processes (including applications, programs, services, etc.), packing / loading processes, financial processes (insurance processes, reporting processes, trading processes, etc.), inspection / diagnosis processes, security processes, safety processes, reporting processes, asset tracking processes, etc.), wearable and portable devices 720 (mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearable devices, head-warmed devices, clothing-integrated devices, armbands, bracelets, neck-worn devices, AR / VR devices, headphones, etc.); workers 718 (delivery workers,shipping workers, barge workers, port workers, dock workers, train workers, ship workers, fulfillment center delivery workers, warehouse workers, vehicle drivers, operations managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, stevedores, inspectors, delivery personnel, environmental managers, financial asset managers, process supervisors and workers (of any of the processes mentioned herein), security personnel, safety personnel, and many others; suppliers 642 (suppliers of goods and related services of any kind, parts suppliers, Customers 662 (including consumers, licensees, businesses, value-added resellers, other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use a category of goods and / or related services); a wide range of operating facilities 712 (including loading docks, storage and warehousing facilities 654, storage, distribution facilities 658, and fulfillment centers 628, air travel facilities 740 (including aircraft, airports, hangars, runways, fueling bases, etc.), maritime facilities 622 (including port infrastructure, Facilities 622 (docks, yards, cranes, roll-on / roll-off facilities, ramps, containers, container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (ships, barges, boats, and others), facilities and other items at origin 610 and / or destination 628, transportation facilities 710 (container ships, barges, and other floating assets 620, as well as land vehicles and other delivery systems 632 used to transport goods, such as trucks, trains, and others); items or factors that consider demand (i.e., e., demand factors 644) (including market factors, events, and many others); supply factors 648) (i.e., supply factors 648) (market factors, weather, availability of parts and materials, and many others); logistics factors 750 (availability of transportation routes, weather, fuel prices, regulatory factors, availability of space (on vehicles, in containers, in packages, in warehouses, in fulfillment centers, on shelves, and many others); retailers 664 (including online retailers 730, such as in the form of e-commerce sites 730), routes for transportation (waterways 732, roads 734, airways,railways 738, etc.), robotic systems 744 (including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, etc.); drones 748 (including for package delivery, site mapping, monitoring or inspection, etc.), autonomous vehicles 742 (for package delivery, etc.), software platforms 752 (such as enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, platform as a service platforms, infrastructure as a service platforms, software-based data storage platforms, analytics platforms, artificial intelligence platforms, etc.), and many others. In some demonstrative embodiments, product 1510 may be encompassed as intelligent product 1510, or VCNP 604 may include intelligent product 1510. The intelligent product 1510 may enable a range of capabilities, including, but not limited to, data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and / or various Internet of Things capabilities. The intelligent product 1510 may include a form of information technology. The intelligent product 1510 may have a processor, computer random access memory, and a communications module. The intelligent product 1510 may be a passive intelligent product, similar to an RFID-type data structure, where the intelligent product can be pinged or read. The product 1510 may be considered an entity in a value chain network (e.g., under the control of a platform) and may be made intelligent by adding RFID to the surrounding infrastructure so that data can be read from the intelligent product 1510. The intelligent product 1510 may also incorporate sensors, IoT devices,The intelligent product 1510 can be connected via tags or other components to form a value chain network, such that connectivity is built around the intelligent product 1510.

[0166] In embodiments, the monitoring system layer 614 may monitor any or all of the value chain entities 652 in the value chain network 668, exchange data with the value chain entities 652, provide control instructions to or take instructions from any of the value chain entities 652, or via various functions of the data processing layer 608 described throughout this disclosure, etc.

[0167] Network characteristics of value chain network entities 9 , there is illustrated the orchestration of a set of deeply interconnected value chain network entities 652 in a value chain network 668 by a value chain network management platform 604. Each of the value chain network entities 652 may have a connection to a VCNP 604, a connection to a set of other value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a connection via the cloud, or other connection), and / or a connection to other value chain network entities 652 via the VCNP 604. The value chain network management platform 604 can manage connections, configure or provide resources to enable connections, and / or manage applications 630 that utilize connections, such as by using information from one set of entities 652 to notify applications 630 that include another set of entities 652, by coordinating the activities of a set of entities 652, by providing input to artificial intelligence systems of the VCNP 604 or to artificial intelligence systems of or relating to a set of entities 652, by interacting with edge computation systems and their environments deployed on or within the entities 652, etc.

[0168] The entities 652 may be external, such that the VCNP 604 interacts with these entities 652. The VCNP 604 may act as a control tower for establishing oversight (e.g., establishing common oversight across multiple entities 652). In one unified platform, there may be an interface where users can view various items, such as user destinations, ports, aviation and rail assets, and orders. It is then conceivable to establish a common data schema that enables services to operate across any one of these applications. This could involve taking any of the data flowing through or about any of these entities 652 and pulling the data into a framework where other applications across supply and demand can interact with the entities 652. This could be a shared data pipeline coming from IoT systems and other external data sources, fed into the monitoring layer and stored in a common data schema in the storage layer, and then various intelligence may be trained to identify meaning across these entities 652. In an exemplary embodiment, a supplier may go bankrupt or be determined to be bankrupt, causing VCNP 604 to automatically trigger a replacement smart contract to send revised terms to a secondary supplier. There may also be management of various aspects of the supply chain. For example, immediate automatic pricing changes on the demand side in response to confirmation that another supplier has gone bankrupt (e.g., from a bankruptcy announcement). Other similar examples may be used based on what occurs in its automation layer, which may be enabled by VCNP 604. Next, in this VCNP 604 interface layer, a digital twin is used to allow users to monitor what is happening with each of these entities 652, which typically do not appear together, including identifying problem states. For example, after viewing a quarterly bad financial report for a supplier, the report may be flagged for close monitoring, such as for potential future bankruptcy.

[0169] For example, an IoT system deployed at a fulfillment center 628 may interface with an intelligent product 1510 to take customer feedback regarding the product 1510, and an application 630 for the fulfillment center 628, upon receiving customer feedback via its connection to the intelligent product 1510 about an issue with the product 1510, may initiate a workflow to perform corrective action on similar products 650 before the products 650 are dispatched from the fulfillment center 628. Similarly, a port infrastructure facility 660, such as a yard for holding shipping containers, via its connection to floating assets 620 (ships, barges, etc.), may notify a fleet of floating assets 620 that the port is approaching capacity, thereby initiating a negotiation process (which may include automated negotiations based on a set of rules and governed by smart contracts) regarding remaining capacity, allowing some assets 620 to be redirected to an alternative port or holding facility. These and many other connections between value chain network entities 652, whether one-to-one connections, one-to-many connections, many-to-many connections, or connections between defined groups of entities 652 (such as those controlled by the same owner or operator), are encompassed herein as applications 630 managed by VCNP 604.

[0170] Platform-managed value chain network activities and applications 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604, and / or participating in the set of value chain network entities 652, may include any one or more of a wide variety of types of applications, such as, but not limited to, supply chain management applications 21004 (such as managing the timing, quantity, logistics, shipping, delivery, and other details of orders for goods, parts, and other items); asset management applications 814 (such as managing the timing, quantity, logistics, shipping, delivery, and other details of orders for goods, parts, and other items); value chain management applications 814 (such as managing floating assets (ships, boats, barges, floating platforms, etc.), real estate (such as those used for warehouses, ports, shipyards, distribution centers, and other building locations), equipment, machinery, supplies (such as those used for handling containers, cargo, parcels, goods, and other items), and the like). for managing assets (such as, but not limited to, cargo, packages, goods, and other items), vehicles (such as forklifts, delivery trucks, autonomous vehicles, and other systems used to move goods), human resources (such as workers), software, information technology resources, data processing resources, data storage resources, power generation and / or storage resources, computing resources, and other assets); financial applications 822 (such as, but not limited to, for handling financial matters related to value chain entities and assets, including, but not limited to, payments, collateral, bonds, customs, duties, levies, taxes, and the like); for managing risk or liability related to shipments, goods, products, assets, people, floating assets, vehicles, equipment, parts, information technology systems, security systems, security events, cybersecurity systems, property, health conditions, death, fire, flood, weather, failure, negligence, business interruption, personal injury, property damage, business loss, breach of contract, and the like);Demand management applications 824 (such as, but not limited to, demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, marketing analytics applications, customer relationship management applications, search engine optimization applications, sales applications, marketing analytics applications, customer relationship management applications, search engine optimization applications, sales management applications, advertising network applications, behavior tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, product or service targeting applications, etc.) or service recommendation engines, etc. (including those that use one or more features of the intelligent products 1510 or are run using the intelligence features of the intelligent products 1510); trading applications 858 (such as, but not limited to, buying applications, selling applications, bidding applications, auction applications, reverse auction applications, bid / ask matching applications, analytics applications for analyzing value chain performance, yields, return on investment, or other metrics, or others); tax applications 850 (such as, but not limited to, those for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, or other elements relating to taxes, duties, levies, tariffs, credits, fees, or other government charges, such as, but not limited to, taxes, customs duties, value added taxes, sales taxes, income taxes, property taxes, municipal fees, pollution taxes, renewable energy credits, pollution abatement credits, import duties, export duties, etc.);identity management applications 830 (such as for managing one or more identities of entities 652 involved in the value chain, such as, but not limited to, one or more of an identity verification application, a biometric identity verification application, a pattern-based identity verification application, a location-based identity verification application, a user behavior-based application, a fraud detection application, a network address-based fraud detection application, a blacklist application, a whitelist application, a content inspection-based fraud detection application, or other fraud detection application); inventory management applications 820 (such as, but not limited to, for managing inventory at a fulfillment center, distribution center, warehouse, storage facility, store, port, ship, or other floating asset, or other location); security applications, solutions, or services 834 (referred to herein as security applications, such as, but not limited to, any of the identity management applications 830 described above, as well as physical security systems (e.g., access control systems (e.g., using biometric access control, fingerprint authentication, retinal scans, passwords, and other access controls), safes, vaults, cages, vaults, secure storage facilities, etc.), surveillance systems (e.g., using cameras, surveillance systems (e.g., using cameras, motion sensors, infrared sensors, and other sensors), perimeter security systems, floating security systems for floating assets, cybersecurity systems (e.g., virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyberattack detection and remediation, packet inspection, traffic inspection, DNS attack remediation and detection, etc.), or other security applications);safety applications 840 (e.g., including, but not limited to, any application for detecting, characterizing, or predicting the likelihood and / or extent of an accident or other damage event, including safety management based on any of the data sources, events, or entities noted throughout this disclosure or throughout the documents incorporated herein by reference, for improving worker safety, reducing the likelihood of damage to property, reducing accident risk, reducing the likelihood of damage to goods (e.g., cargo), risk management for insured goods, loan collateral, etc.); blockchain applications 844 (e.g., including, but not limited to, a distributed ledger or other blockchain-based application capturing a series of transactions, such as debits or credits, purchases or sales, exchanges of in-kind, smart contract events, etc.); facility management applications 850 (e.g., facility management applications for shipyards, ports, distribution centers, warehouses, docks, stores, fulfillment centers, storage facilities, etc.); information technology systems, such as for managing and designing infrastructure, buildings, systems, real estate, personal property, and other assets that support the value chain, robotics / autonomous vehicle systems, packaging systems, packing systems, picking systems, inventory tracking systems, inspection systems, routing systems for mobile robots, workflow systems for human assets, etc.; regulatory applications 852 (such as, but not limited to, applications to regulate any of the applications, services, transactions, activities, workflows, events, entities, or other items described in this specification and the documents incorporated herein by reference, such as regulations regarding permitted routes, permitted cargo and goods, permitted parties to transactions, required disclosures, privacy, pricing, marketing, product and service offerings, data use (including data privacy regulations, regulations regarding data retention, etc.), banking, marketing, sales, financial planning, etc.);commerce applications, solutions, or services 854 (such as e-commerce site marketplaces, online sites, auction sites or marketplaces, spot marketplaces, advertising marketplaces, reverse auction marketplaces, advertising networks, or other marketplaces); vendor management applications 832 (such as, but not limited to, applications for managing a set of vendors or potential vendors and / or for managing the procurement of a set of goods, components, or materials that may be supplied in a value chain, including functionality such as vendor qualification, vendor ratings, requests for proposals, requests for information, bonds or other performance guarantees, contract management, etc.); analytics applications 838 (such as, but not limited to, analytics applications relating to any of the data types, applications, events, workflows, or entities mentioned throughout this disclosure or the documents incorporated by reference herein, such as, for example, big data applications, user behavior applications, forecasting applications, classification applications, dashboards, pattern recognition applications, econometric applications, financial yield applications, return on investment applications, scenario planning applications, decision support applications, demand forecasting applications, demand planning applications, route planning applications, weather forecasting applications, and many other applications);Pricing application 842 (e.g., without limitation, a smart contract application, solution, or service (including those referenced in this disclosure and documents incorporated herein by reference) and any smart contract type referenced in this disclosure or documents incorporated herein by reference, such as, but not limited to, a smart contract for the sale of goods, a smart contract for ordering goods, a smart contract for delivery resources, a smart contract for labor, a smart contract for the delivery of goods, a smart contract for the installation of goods, a smart contract with tokens or cryptocurrency as consideration, a smart contract that attributes rights, options, futures, or interests based on future terms, a security, a commodity, smart contracts for futures, options, derivatives, etc.; smart contracts for current or future resources; smart contracts configured to consider or accommodate tax, regulatory, or compliance parameters; smart contracts configured to execute arbitrage trades; or many other smart contracts. Thus, the value chain management platform 604 may host a wide range of heterogeneous applications 630 (such term including the above and other value chain applications, services, solutions, etc.) to enable interaction between them, such that any set or larger combination or permutation of such services may be improved compared to isolated applications of the same type through shared microservices, shared data infrastructure, and shared intelligence.

[0171] With further reference to FIG. 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involved with the set of value chain network entities 652 may further include, but are not limited to: a payment application 860 (such as for performing payment calculations (including based on contextual factors such as applicable taxes, duties, etc. in the geography of the entity 652), fund transfers, payment settlements to parties, etc. for any of the applications 630 noted herein); a process management application 862 (such as for managing any of the processes or workflows described throughout this disclosure, including supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, ordering processes, navigation processes, and many others); and any process, workflow, activity, or other application described herein. compatibility testing applications 864, such as for assessing compatibility between value chain network entities 652 or activities involved in any of the applications 630 (e.g., to determine compatibility of a container or package with a product 1510, compatibility of a product 1510 with another product 1510 (such as when one is a supply, resupply, replacement part, etc. of the other), such as to determine compatibility of a container or package with a product 1510, compatibility of a product 1510 with a set of customer requirements, compatibility of infrastructure and equipment entities 652 (such as between a container ship or barge and a port or waterway, between a container and a storage facility, between a truck and a road, between a drone or robot and a package, between a drone, AV, or robot and a delivery destination, and many others); infrastructure testing applications 802 (such as to test the capabilities of infrastructure elements to support a product 1510 or application 630, including but not limited to storage capacity, lifting capacity, movement capacity, memory capacity, network capacity, environmental control capacity, software capacity, security capacity, and many others);and / or an incident management application 910 (for managing events, accidents, and other incidents that may occur in one or more environments involving the value chain network entities 652, such as, but not limited to, vehicle accidents, worker injuries, shutdown incidents, property damage incidents, product damage incidents, product liability incidents, regulatory non-compliance incidents, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, and others, and combinations thereof), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, abuse incidents, and many others);

[0172] 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involved with the set of value chain network entities 652 may further include, but are not limited to: predictive maintenance applications 910 (e.g., for forecasting, forecasting, and taking actions to manage breakdowns, malfunctions, shutdowns, damage, required maintenance, required repairs, required service, required support, etc., for the set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, etc.); logistics applications 912 (e.g., for managing logistics for pickup, delivery, transferring items onto material handling equipment, loading, unloading, packaging, picking, shipping, driving, and other activities involved in scheduling and managing the movement of products 650 and other items between origins and destinations via various intermediate points); reverse logistics applications 914 (e.g., for managing returned or unreturned products 650 and other items); waste reduction applications 920 (e.g., for reducing packaging waste, solid waste, energy waste, liquid waste, pollution, contaminants, computing resource waste, human resource waste, or other waste involved in a value chain network entity 652 or activity); augmented reality, mixed reality, and / or virtual reality applications 930 (e.g., for visualizing one or more value chain network entities 652 or activities involved in one or more applications 630, such as, but not limited to, the movement of a product 1510, the interior of a facility, the state or condition of an item of goods, one or more environmental conditions, weather conditions, the packaging configuration of a container or set of containers, or many others); demand forecasting applications 940 (e.g., for forecasting demand for a product 1510, a category of product, a potential product, and / or factors contributing to the demand, such as market factors, wealth factors, demographic factors, weather factors, economic factors, etc.);a demand aggregation application 942 (e.g., for aggregating information, orders, and / or commitments (optionally embodied in one or more contracts, which may be smart contracts) for one or more products 650, categories, etc., including current demand for existing products and future demand for products that are not yet available); a customer profiling application 944 (e.g., for profiling one or more demographic, psychographic, behavioral, economic, geographic, or other attributes of a set of customers, such as based on past purchasing data, loyalty program data, behavioral tracking data (including data captured in customer interactions with smart products 1510), online clickstream data, interactions with intelligent agents, and other data sources), and / or a component supply application 948 (e.g., for managing a supply chain of components for a set of products 650);

[0173] 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involved with the set of value chain network entities 652 may further include, but are not limited to: policy management applications 868 (e.g., for deploying one or more policies, rules, etc. for governance of one or more value chain network entities 652 or applications 630, such as for managing the execution of one or more workflows (which may include configuring policies in the platform 604 for each workflow); regulatory (maritime, food and drug, healthcare, environmental, health, safety, tax, financial reporting, commercial, etc.); , and other regulations described throughout this disclosure or that would be understood in the art), to manage the provision of resources (such as connectivity, computing, human, energy, and other resources, including smart contracts, and the platform 604 can automatically deploy governance functions to associated entities 652 and applications 630, such as through connectivity facilities 642), to manage interactions with other entities (such as including policies for sharing information and access to resources), to manage data access (including privacy data, operational data, status data, and many other data types), to manage security access to infrastructure, products, facilities, locations, etc., and many other things;product configuration applications 870 (e.g., for enabling a product manager and / or an automated product configuration process (optionally using robotic process automation) to determine the configuration of a product 1510, including on-the-fly configuration, such as during agile manufacturing, in-the-field configuration or customization (e.g., by 3D printing one or more features or elements), or remote configuration or customization, such as firmware download, field programmable gate array configuration, software installation, etc.); warehouse management and fulfillment applications 872 (for managing warehouses, distribution centers, fulfillment centers, etc., including selecting products, configuring product storage locations, determining routes for moving products within the facility by people, mobile robots, etc., and scheduling picking and packing); determining routes, paths, and workflows, managing the operations of robots, drones, conveyors, and other equipment, scheduling product delivery to docks, etc., and many other functions; kit configuration and deployment applications 874 (e.g., to enable users of the VCNP to configure kits, boxes, or otherwise pre-integrated, pre-delivered, and / or pre-configured systems to allow customers or operators to quickly deploy a subset of the VCNP 604's functionality for a particular value chain network entity 652 and / or application 630); and / or product testing applications 878 for testing products 1510 (e.g., for performance, functionality and feature activation, safety, compliance with policies or regulations, quality, quality of service, likelihood of failure, and many other factors);

[0174] Still referring to FIG. 10 , a set of applications 614 are provided on, integrated with, and / or managed by or for, and / or include the VCNP 604.The value chain network entity 652 may further include, but is not limited to, a maritime fleet management application 880 (for managing a collection of maritime assets such as container ships, barges, boats, etc., and related infrastructure facilities such as docks, cranes, ports, etc., to determine optimal routes for fleet assets based on weather, market, traffic, and other conditions, ensure compliance with policies and regulations, ensure safety, improve environmental factors, improve financial metrics, and many other purposes); a shipping management application 882 (for managing a collection of shipping assets such as trucks, trains, planes, etc., to optimize financial yields, improve safety, reduce energy consumption, reduce delays, mitigate environmental impacts, and many other purposes); an opportunity matching application 884 (for example, for matching one or more demand factors with one or more supply factors, and to match the needs and capabilities of the value chain network entity 652 with the opportunities that the value chain network entity 652 has); workforce management applications 888 (such as for managing workers in various workforces, including workforces in or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise locations, retail stores, online / e-commerce site management facilities, ports, ships, boats, barges, trains, depots, and other facilities mentioned throughout this disclosure); distribution and delivery applications 890 (such as for planning, scheduling, routing, and otherwise managing the distribution and delivery of products 650 and other items); and / or enterprise resource planning (ERP) applications 892 (such as for planning the utilization of enterprise resources, including labor resources, financial resources, energy resources, physical assets, digital assets, and other resources).

[0175] Core functions and interactions of the data handling layer (adaptive intelligence, monitoring, data storage, applications) Referring to FIG. 11 , a high-level schematic diagram of one embodiment of a value chain network management platform 604 is shown, which may be a set of systems, applications, or other entities operating in conjunction to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact, or own, operate, support, or enable one or more value chain network processes, workflows, activities, events, and / or applications 630, or that may otherwise be part of, integrated with, linked to, or otherwise operated by the platform 604. 15. The platform 604 operates in connection with products 1510 (which may be a finished product, a software product, a hardware product, a component product, a material, an equipment item, a consumer packaged good, a consumer product, a food product, a beverage product, a household product, an industrial product, a consumable product, a pharmaceutical product, a medical device product, a technology product, an entertainment product, or any other type of product or related service, and in embodiments may encompass intelligent products enabled with processing, networking, sensing, computation, and / or other Internet of Things capabilities), including a set of applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements. The value chain entities 652 may include any of a wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals, or other entities mentioned throughout this disclosure or in documents incorporated by reference herein that are involved in or are involved in a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many other activities that are involved in the various value chain network processes, workflows, activities, events, and applications 630).

[0176] In an embodiment, the value chain network management platform 604 may include a set of data processing layers 608, each configured to provide a set of functions that facilitate the development and deployment of intelligence for a wide variety of value chain network applications and end uses, such as to facilitate automation, machine learning, the application of artificial intelligence, intelligent transactions, intelligent operations, remote control, analysis, monitoring, reporting, state management, event management, process management, and many others. In an embodiment, the data processing layers 608 may include a value chain network monitoring system layer 614, a value chain network entity-oriented data storage system layer 624 (sometimes referred to herein as simply the data storage layer 624 for convenience), an adaptive intelligent system layer 614, and the value chain network management platform 604. The value chain network management platform 604 may include a data handling layer 608 to provide management of the value chain network management platform 604 and / or other layers, such as a value chain network monitoring system layer 614, a value chain network entity-oriented data storage system layer 624 (e.g., data storage layer 624), and an adaptive intelligent system layer 614. Each of the data handling layers 608 may include various services, programs, applications, workflows, systems, components, and modules, as further described herein and in documents incorporated by reference. In an embodiment, each of the data handling layers 608 (and optionally the platform 604 as a whole) is configured such that one or more of its elements can be accessed as a service by the other layers 624 or by other systems (e.g., configured as a Platform-as-a-Service deployed on a set of cloud infrastructure components in a microservices architecture).For example, the platform 604 may have (or may configure and / or provision) a set of connectivity facilities 642, such as network connections (including various configurations, types, and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, etc., that the data handling layer 608 may use to communicate data or information between the data handling layer 608 and other layers, systems, or subsystems of the platform 604, as well as with external systems, such as value chain entities 652 or cloud-based or on-premise enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems, etc.). Each of the data handling layers 608 may include a set of services (e.g., microservices) for data handling, including facilities for data extraction, transformation, and loading, facilities for data cleansing and deduplication, facilities for data normalization, facilities for data synchronization, facilities for data security, facilities for computation (e.g., facilities for performing predefined computational operations on data streams and providing output streams), facilities for compression and decompression, facilities for analysis (such as providing automatic generation of data visualizations), etc.

[0177] In an embodiment, each data processing layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each other data processing layer 608. These may include data integration functions, such as for extracting, converting, loading, normalizing, compressing, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information exchanged between layers and / or applications 630. In an embodiment, the data processing layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring system layer 614. The value chain monitoring system layer 614 may include, integrate with, and / or work with various data collection and management systems 640, sometimes conveniently referred to as data collection systems 640, to collect and organize data collected from or about value chain entities 652, as well as data collected from or about various data layers 624 or their services or components. For example, streams of physiological data from wearable devices worn by workers undertaking tasks or consumers engaging in activities may be distributed via the monitoring system layer 614 to multiple separate applications within the value chain management platform 604, such as one that facilitates monitoring the worker's physiological, psychological, performance level, alertness, or other state, and another that facilitates operational efficiency and / or effectiveness. In an embodiment, the monitoring system layer 614 facilitates time synchronization, normalization, etc., of the data collected for one or more value chain network entities 652.For example, one or more video streams or other sensor data collected of or about a worker 718 or other entity in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock so that the relative timing of the set of video or other data can be understood by a system that can process the video, such as a machine learning system operating on the images in the video, the changes between images in different frames of the video, or the like. In such an example, the monitoring system layer 614 may further align the set of video, camera images, sensor data, etc. with other data, such as streams of data from wearable devices, streams of data generated by value chain network systems (ships, lifts, vehicles, containers, material handling systems, packaging systems, delivery systems, drones / robots, etc.), streams of data collected by mobile data collectors, etc. Configuring the monitoring system layer 614 as a common platform or set of microservices accessed across many applications can dramatically reduce the number of interconnections an owner or other operator in a value chain network needs to have in order to have a growing set of data. Applications monitor a set of IoT devices and other systems or devices under their control.

[0178] In embodiments, the data processing layer 608 is configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 via a value chain network-oriented data storage system layer 624, sometimes referred to herein for convenience simply as the data storage layer 624 or storage layer 624. For example, various data collected for value chain entities 652, as well as data generated by other data processing layer 608, may be stored in the data storage layer 624 such that any of the various data processing layer 608 services, applications, programs, etc., can access a common data source (which may consist of a single logical data source distributed across disparate physical and / or virtual storage locations). This may facilitate a dramatic reduction in the amount of data storage required to process the vast amounts of data generated by or for value chain network entities 652 as applications 630 and value chain network uses grow and proliferate. For example, a supply chain or inventory management application of the value chain management platform 604, such as for ordering replacement parts for machinery or equipment, may have access to the same data set regarding which parts have been replaced for a set of machinery, as may a predictive maintenance application used to predict whether a ship component or port equipment is likely to need a replacement part. Similarly, predictions may also be used regarding the resupply of items.

[0179] In an embodiment, the value chain network data objects 1004 may be provided according to an object-oriented data model that defines the classes, objects, attributes, parameters, and other characteristics of the set of data objects (such as those associated with the value chain network entities 652 and applications 630) handled by the platform 604.

[0180] In embodiments, the data storage system layer 624 can provide an extremely rich environment for the collection of data that can be used to extract features or inputs for intelligent systems, such as expert systems, analytical systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems as disclosed throughout this disclosure and the documents incorporated by reference herein. As a result, each application 630 in the platform 604 and each adaptive intelligent system in the adaptive intelligent systems layer 614 can benefit from data collected or generated by or for each other. In embodiments, the data storage system layer 624 can facilitate the collection of data that can be used to extract features or inputs for intelligent systems, such as development frameworks from artificial intelligence. In examples, the collection of data can be capturing and / or storing event logs (naturally stored or ad hoc, as needed), performing periodic checks of on-board diagnostic data, or the like. In examples, pre-computation of functions can be deployed using, for example, AWS Lambda or various other cloud-based, on-demand computational capabilities, such as pre-computing, multiplexing signals, etc. In many instances, there are pairings (dual, triple, quadruple, etc.) of similar types of value chain entities that can use one or more capability sets of the data handling layer 608 to extend connectivity and services across the value chain entities and across the applications used by the entities, even when aggregating hundreds or thousands of data types from relatively heterogeneous entities.In these examples, various pairings of similar value chain entities, using at least in part connectivity and services across value chain entities and applications, can direct information from the connected data pairings to artificial intelligence services, including various neural networks and hybrid combinations thereof, as disclosed herein. In these examples, genetic programming techniques may be deployed to prune some of the input features of the information from the connected data pairings. In these examples, genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairings. These genetic programming techniques may be shown to increase the effectiveness of decisions established by the artificial intelligence services. In these examples, information from the connected data pairings may be migrated to other tiers on the platform, including those supporting or deploying robotic process automation, forecasting, prediction, and other resources, such that the shared data schema may facilitate the capabilities and resources of platform 604.

[0181] A wide variety of storage media and data storage types, data architectures 1002, and formats may be used to store a wide range of data types in the storage layer 624, including, but not limited to, asset and facility data 1030, state data 1140 (such as that indicating the state, condition status, or other indicators for any of the value chain network entities 652, any of the applications 630 or their components or workflows, or any of the components or elements of the platform 604), worker data 1032 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data, and many other types); event data 1034 (including operational data, transaction data, workflow data, maintenance data, and events that occur within the value chain network 668 or with respect to one or more applications 630, or many other types of data related to events, such as those relating to any of a wide range of events, including process events, financial events, transactional events, output events, input events, state events, and quality data, including output events, input events, state change events, operational events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, charging events, shipping events, warehousing events, movement of goods, border crossings, movement of cargo, inspection events, supply events, and many other events; claims data 664 (such as insurance claims for business interruption insurance, product liability insurance, insurance for goods, premises, or equipment, flood insurance, insurance for contract-related risks, product liability, general liability, workers' compensation, injury, and other liability claims, and claims data related to contracts, such as supply contract performance claims, product delivery requirements, warranty claims, indemnity claims, delivery requirements, timing requirements, milestones, key performance indicators, etc.);accounting data 730 (such as data regarding the completion of contract requirements, bond satisfaction, payment of duties, etc.), and risk management data 732 (such as data regarding supply items, amounts, pricing, delivery, source of supply, routing, customs information, and many others), and many other data types related to the value chain network entities 652 and applications 630;

[0182] In an embodiment, data processing tier 608 is configured in a topology that facilitates shared adaptive capabilities, which may be provided, managed, mediated, etc. by one or more of a set of services, components, programs, systems, or capabilities in adaptive intelligence systems tier 614, conveniently referred to herein as adaptive intelligence tier 614. Adaptive intelligence systems tier 614 may include a set of data processing, artificial intelligence, and computation systems 634, which are described in more detail elsewhere in this disclosure. Thus, the use of various resources such as computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for a single device or a peered network), and available cloud infrastructure), data storage resources (such as local storage on a device, storage resources within or on a value chain entity or environment (including on-device storage, storage on an asset tag, local area network storage, etc.), network storage resources, cloud-based storage resources, database resources, etc.), network resources (including cellular network spectrum, wireless network resources, fixed network resources, etc.), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others), etc., can be optimized in a collaborative or shared manner for the benefit of multiple applications, programs, workflows, etc., on behalf of operators, enterprises, etc. For example, the adaptive intelligence layer 614 can manage and provide available network resources for both supply chain management applications and demand planning applications (among many other possibilities), such that low-latency resources are used for the supply chain management application (where quick decisions may be critical) and longer-latency resources are used for the demand planning application.As described in more detail throughout this disclosure and the documents incorporated herein by reference, a wide variety of adaptations may be provided for various services and capabilities across various tiers 624, including those based on application requirements, quality of service, on-time delivery, service goals, budgets, costs, pricing, risk factors, operational goals, efficiency goals, optimization parameters, return on investment, profitability, uptime / downtime, worker utilization, and many others.

[0183] The value chain management platform 604, which may be referred to herein as the platform 604 for convenience, enables an operator to manage multiple aspects of a value chain network environment or entity 652 in a common application environment (e.g., utilizing common data storage in the data storage layer 624, common data collection or monitoring in the monitoring system layer 614, and / or common adaptive intelligence in the adaptive intelligence layer 614). Output from the applications 630 of the platform 604 may be provided to other data handling layers 624. These may include, but are not limited to, state and status information for various objects, entities, processes, flows, etc.; object information such as identity, attribute, and parameter information for various classes of objects of various data types; event and change information for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including regarding yield, engagement, return on investment, profitability, efficiency, timeliness, service quality, product quality, customer satisfaction, etc.). Output from each application 630 may be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614.Thus, the cross-application nature of the platform 604 facilitates convenient configuration of all of the infrastructure elements necessary to add intelligence to any given application, such as by feeding machine learning on cross-application results, providing automation enrichment for a given application through machine learning based on results from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604. In examples, there may be systems, components, services, and other capabilities that optimize the control, automation, or one or more performance characteristics of one or more value chain network entities 652, or that may generally improve either the outputs and outcomes 1040 of processes and applications pursued through use of the platform 604. In some examples, the outputs and results 1040 from the various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, and the like involved in the steps of the process intended to be automated.

[0184] Data Storage Layer Deep Dive - Alternative Data Architectures 12 , additional details, components, subsystems, and other elements of optional embodiments of the data storage layer 624 of the platform 604 are illustrated. A variety of data architectures may be used, including traditional relational and object-oriented data architectures, a blockchain architecture 1180, an asset tag data storage architecture 1178, a local storage architecture 1190, a network storage architecture 1174, a multi-tenant architecture 1132, a distributed data architecture 1002, a value chain network (VCN) data object architecture 1004, a cluster-based architecture 1128, an event database architecture 1034, a state database architecture 1140, a graph database architecture 1124, a self-organizing architecture 1134, and other data architectures 1002.

[0185] The adaptive intelligent systems layer 614 of the platform 604 may include one or more protocol adapters 1110 to facilitate data storage, search access, query management, loading, extraction, normalization, and / or transformation to enable the use of various other data storage architectures 1002, such as to enable extraction from a database of one format and loading into a data system using a different protocol or data structure.

[0186] In embodiments, the value chain network-oriented data storage systems layer 624 may include, but is not limited to, physical storage systems, virtual storage systems, local storage systems (e.g., part of local storage architecture 1190), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of network storage architecture 1174 such as those using NVME, storage-attached networks, and other network storage systems), and many others.

[0187] In an embodiment, the storage layer 624 may store data in one or more knowledge graphs (such as directed acyclic graphs, data maps, data hierarchies, data clusters with links and nodes, self-organizing maps, etc.) within the graph database architecture 1124. In an exemplary embodiment, a knowledge graph may be a powerful example of when graph databases and graph database architectures may be used. In some examples, a knowledge graph may be used to graph a workflow. For linear workflows, a directed acyclic graph may be used. For contingent workflows, a cyclic graph may be used. A graph database (e.g., the graph database architecture 1124) may include a knowledge graph, or a knowledge graph may be an example of a graph database. In an exemplary embodiment, the knowledge graph may include ontologies and connections (e.g., relationships) between the ontologies in the knowledge graph. In one example, a knowledge graph may be used to capture a human expert's articulation of a knowledge domain so that there may be identification of opportunities to design and build robotic process automation or other intelligence that can replicate this knowledge set. The platform can be used to recognize that types of experts use this factual knowledge base (from the knowledge graph) combined with capabilities replicable by artificial intelligence, which may vary depending on the type of expert involved. For example, artificial intelligence such as convolutional neural networks might be used in spatiotemporal aspects used for diagnosing problems or packing boxes in a warehouse. Meanwhile, the platform can use a different type of knowledge graph for a self-organizing map of experts whose primary task is segmenting customers into customer segmentation groups. In some examples, the knowledge graph may be constructed from various data, such as job credentials, job listings, and analytical output deliverables. In embodiments, the data storage layer 624 may store data in a digital thread, ledger, or the like, such as for maintaining a serial or other record of entities 652 over time, including any of the entities described herein.In an embodiment, the data storage layer 624 may use and enable asset tags 1178, which may include data structures associated with assets and accessible and managed, such as by using access controls, with data storage and retrieval optionally linked to local processes, but also optionally open to remote retrieval and storage options. In an embodiment, the storage layer 624 may include one or more blockchains 1180, such as those storing identity data, transaction data, past interaction data, etc., with access controls that may be role-based or based on credentials associated with value chain entities 652, services, or one or more applications 630. The data stored by the data storage system 624 may include accounting and other financial data 730, access data 734, asset and facility data 1030 (such as relating to any of the value chain assets and facilities described herein), asset tag data 1178, worker data 1032, event data 1034, risk management data 732, pricing data 738, safety data 664, and many other types of data that may be associated with, generated by, or generated for any of the value chain entities and activities described herein and in the documents incorporated by reference.

[0188] Adaptive Intelligent Systems and Monitoring Layer 13 , which illustrates additional details, components, subsystems, and other elements of optional embodiments of platform 604. Management platform 604, in various optional embodiments, may include a set of applications 614 that enable an operator or owner of a value chain network entity, or other user, to manage, monitor, control, analyze, or otherwise interact with one or more elements of value chain network entity 652, such as any of the elements noted above and associated throughout this disclosure.

[0189] In an embodiment, the adaptive intelligent systems layer 614 may enhance one or more of the applications 630 in the application platform 604; may improve the performance of one or more of the components, or the overall performance of the connectivity equipment 642 (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors); may improve other capabilities within the adaptive intelligent systems layer 614; may improve the performance (e.g., speed / latency, energy utilization, storage capacity, storage efficiency, reliability, security, etc.) or overall performance of one or more components of the value chain network-oriented data storage system 624; may optimize the control, automation, or one or more performance characteristics of one or more value chain network entities 652; or generally improve any of the outputs and results 1040 of the processes and applications pursued through use of the platform 604.

[0190] These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adapters 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction systems 1920 (e.g., for setting up, provisioning, configuring, and otherwise managing sets of interactions between and among sets of value chain network entities 652 within a value chain network 668), and other systems.

[0191] In embodiments, the value chain monitoring system layer 614 and its data collection systems 640 may include a wide range of systems for data collection, including, but not limited to, real-time monitoring systems 1520 (such as on-board monitoring systems on ships and other floating assets, on delivery vehicles, on trucks and other transport assets, and event and status reporting systems at shipyards, ports, warehouses, distribution centers, and other locations, on-board diagnostics (OBD) and telematics systems on floating assets, vehicles, and equipment); systems that provide diagnostic codes and events via event buses, communication ports, or other communication systems; monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, people tracking systems, and ambient sensing systems installed in various environments where value chain activities and other events occur); and portable and mobile data collectors, RFID and other tag readers, removable and replaceable monitoring systems such as smartphones, tablets, and other mobile devices capable of data collection, software interfaces, and the like. Interaction observation systems 1500 (such as for recording and tracking events involving user interaction with a software user interface, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigation actions, eye movements, finger movements, gestures, menu selections, etc., as well as software interactions that occur as a result of many other programs, such as through APIs); mobile data collectors 1170 (as broadly described herein and in documents incorporated by reference), vision monitoring systems 1930 (such as using video and still imaging systems, LIDAR, IR, and other systems that enable visualization of entities 652 items, people, materials, components, machinery, equipment, personnel, gestures, facial expressions, location, configuration, and other factors or parameters, as well as inspection systems that monitor processes, worker activities, etc.);Interaction Point Systems 1530 (such as dashboards, user interfaces, and control systems for value chain entities); Physical Process Observation Systems 1510 (such as for tracking the physical activities of operators, workers, customers, etc., the physical activities of individuals (such as shippers, delivery workers, packers, pickers, assemblers, customers, tradespeople, vendors, distributors, etc.), the physical interactions of workers with other workers, the interactions of workers with physical entities such as machines and equipment, and the interactions of physical entities with other physical entities (such as through the use of video and still image cameras, motion sensing systems (including optical sensors, LIDAR, IR and other sensor sets), robotic motion tracking systems (such as tracking the movement of systems attached to humans or physical entities), and many others); Machine Condition Monitoring Systems 1940 (such as through the use of clients, servers, cloud resources, control systems, display screens, sensors, cameras, vehicles, including on-board and external monitors of the state, condition, operating parameters, or other measures of condition of any value chain entity, such as a machine or component thereof, such as a robot, or other machine; sensors and cameras 1950 and other IoT data collection systems 1172 (such as on-board sensors, sensors or other data collectors (including click-tracking sensors) in or about the value chain environment (e.g., but not limited to, origins, loading docks, vehicles or floating assets used to transport goods, containers, ports, distribution centers, storage facilities, warehouses, delivery vehicles, etc.), and destinations), cameras for monitoring the environment as a whole, dedicated cameras for specific machines, processes, workers, etc., wearable cameras, portable cameras, cameras located on mobile robots, cameras on portable devices such as smartphones and tablets, and many other sensor types (including any of the many sensor types disclosed throughout this disclosure or in the documents incorporated by reference herein;) indoor location monitoring systems 1532 (including cameras, IR systems, motion detection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectrum detection systems, time-of-flight systems, chemical nose and other chemical sensor sets, and other sensors); user feedback systems 1534 (including survey systems, touchpads, voice-based feedback systems, rating systems, facial expression monitoring systems, affect monitoring systems, gesture monitoring systems, and others); behavior monitoring systems 1538 (such as for monitoring movements, shopping behavior, purchasing behavior, clicking behavior, behavior indicative of fraud or deception, user interface interaction, product return behavior, behavior indicative of interest, attention, boredom, etc., behavior indicative of mood (fidgeting, staying still, moving closer, changing posture, etc.), etc.), and any of a wide variety of Internet of Things (IoT) data collectors 1172, such as those described throughout this disclosure and in the documents incorporated herein by reference;

[0192] In an embodiment, the value chain monitoring system layer 614 and its data collection system 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652, such as any of the entities described throughout this disclosure. This may include components or subsystems for searching for entities within the value chain network 668, by device identifier, by network location, by geolocation (e.g., by geofence), by indoor location (e.g., by proximity of IoT-enabled devices to known resources such as infrastructure, Wifi routers, switches, etc.), by cellular location (e.g., proximity to cellular towers), by identity management system (e.g., where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise, by an identifier assigned and / or managed by the platform 604), etc. The entity discovery 1900 may initiate a handshake between a set of devices, such as to initiate interactions that provide various applications 630 or other functionality of the platform 604.

[0193] 14, a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, etc.) for a set of demand management applications 824 and a set of supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 can provide the artificial intelligence 1160 through a set of data processing, artificial intelligence, and computation systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with a set of value chain applications (e.g., demand management applications 824 and supply chain applications 812). The adaptive intelligence systems 614 may include artificial intelligence, including any of various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.

[0194] In embodiments, the user interface may include an interface for configuring the artificial intelligence system 1160 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and feed them into the neural network, artificial intelligence system 1160, or any of the other adaptive intelligence systems 614 described throughout this disclosure and documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of the category of commodity 3010. In embodiments, the selected data sources of the value chain may be applied as inputs for classification or prediction, or as outcomes related to the value chain, the category of commodity 3010, etc.

[0195] In embodiments, providing coordinated intelligence may include providing artificial intelligence functionality, such as artificial intelligence system 1160. The artificial intelligence system may facilitate collaborative intelligence for a set of demand management applications 824 or a set of supply chain applications 812, or both, such as a category of goods, a customer experience management (CEM) system, a service lifecycle management (SLM) system, a product lifecycle management (PLM) system, etc., by processing data available in any of the value chain data sources, such as value chain processes, bills of materials, manifests, delivery schedules, weather data, traffic data, product design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, etc.

[0196] In embodiments, user interface 3020 may provide access to, among other things, artificial intelligence capabilities, applications, systems, etc. for tailoring intelligence for value chain applications, particularly value chain applications for the commodity 3010 category. User interface 3020 may be adapted to receive information describing the commodity 3010 category and configure user access to responsive artificial intelligence capabilities, whereby a user is guided via the user interface to artificial intelligence capabilities suitable for use with value chain applications (e.g., a set of demand management applications 824 and supply chain applications 812) that contribute to goods / services in the commodity 3010 category. User interface 3020 may facilitate providing tailored intelligence comprised of artificial intelligence capabilities that provide tailored intelligence for specific operators and / or companies participating in the commodity category's supply chain.

[0197] In embodiments, the user interface 3020 may be configured to facilitate a user's selection and / or configuration of multiple artificial intelligence systems 1160 for use with the value chain. The user interface may present the set of demand management applications 824 and supply chain applications 812 as connected entities that respectively receive, process, and generate output that may be shared between the applications. The type of artificial intelligence system 1160 may be indicated in the user interface 3020 in response to the set of connected applications or their data elements being indicated in the user interface, such as by a user hovering a pointer near the connected set of applications. In embodiments, the user interface 3020 provides a set of functionality that facilitates access to the set of adaptive intelligence systems and facilitates the development and deployment of intelligence for at least one function selected from the list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

[0198] The adaptive intelligence system 614 may be configured with data processing, artificial intelligence, and computing systems 634 that may operate collaboratively to provide collaborative intelligence, such as when artificial intelligence systems 1160 operate on or respond to data collected by or generated by other systems in the adaptive intelligence system 614, such as data processing systems. In an embodiment, providing collaborative intelligence may include employing one or more types of neural networks described herein and in the documents incorporated by reference to operate a portion of the set of artificial intelligence systems 1160 that process either demand management application outputs and supply chain application outputs to provide collaborative intelligence.

[0199] In an embodiment, providing tailored intelligence for the set of demand management applications 824 may include configuring (e.g., through a user interface 3020, etc.) at least one of the adaptive intelligence systems 614 for at least one or more demand management applications selected from a list of demand management applications including a demand planning application, a demand forecasting application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavior tracking application, a marketing analytics application, a location-based product or service targeting application, a collaborative filtering application, a product or service recommendation engine, etc.

[0200] Similarly, providing collaborative intelligence for a set of supply chain applications 812 may include configuring at least one of the adaptive intelligence systems 614 for at least one or more supply chain applications selected from a list of supply chain applications including a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order of goods management application, a parts management application, and the like.

[0201] In embodiments, the management platform 102 may facilitate access, such as via a user interface 3020, to a set of adaptive intelligence systems 614 that provide coordinated intelligence for a set of demand management applications 824 and supply chain applications 812 through the application of artificial intelligence. In such embodiments, a user may seek to align supply and demand while ensuring profitability, etc. of a value chain for a category of goods 3010. By providing access to artificial intelligence capabilities 1160, the management platform allows a user to focus on demand and supply applications while taking advantage of technologies such as expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, and deep learning systems.

[0202] In an embodiment, the management platform 102 may provide, such as through a user interface 3020, a set of adaptive intelligence systems 614 that provide artificial intelligence 1160 tailored for the set of demand management applications 824 and supply chain applications 824. The supply chain applications 812 are used for a category of goods 3020 by, for example, (automatically) determining relationships between the demand management and supply chain applications based on inputs used by the applications, results produced by the applications, and value chain outcomes. The artificial intelligence 1160 may be tailored by, for example, a set of data processing, artificial intelligence, and computation systems 634 available through the adaptive intelligence systems 614.

[0203] In an embodiment, management platform 102 may be configured with a set of artificial intelligence systems 1160 as part of a set of adaptive intelligence systems 614 that provides coordinated intelligence for the set of demand management applications 824 and supply chain applications 812 for the category of goods 3010. The set of artificial intelligence systems 1160 can provide the coordinated intelligence such that at least one supply chain application in the set of supply chain applications 812 generates results that address at least one aspect of supply for at least one of the goods in the category of goods, as determined by at least one demand management application in the set of demand management applications 824. In an example, the behavioral tracking demand management application can generate results regarding usage behavior of goods within the category of goods 3010. The artificial intelligence system 1160 can process the behavioral data and conclude that there is a recognized need for expanded consumer access to a second good in the category of goods 3010. This tailored intelligence can be applied, optionally automatically, to a set of supply chain applications 812, for example, to allocate production resources or other resources in a commodity category's value chain to the second product. As an example, a distributor that stocks retail store shelves can receive a new inventory plan that allocates more retail store shelf space to the second product, such as by taking space away from lower-margin products.

[0204] In embodiments, the set of artificial intelligence systems 1160, etc., can provide coordinated intelligence to the set of supply chain and demand management applications, for example, by determining optional temporal prioritization of demand management application outputs that influence control of the supply chain applications so that optional temporal demand for at least one product in the category of products 3010 can be met. Seasonal adjustments in the prioritization of demand request results are an example of a temporal change. Prioritization adjustments can also be made locally. For example, a large college football team may play in its home stadium, temporarily adjusting the local supply of tailgating equipment, even though the demand management application results indicate that there is currently no demand for small propane stoves in the wider area.

[0205] The set of adaptive intelligence systems 614 that provide tailored intelligence, such as by providing artificial intelligence capabilities 1160, may also facilitate the development and deployment of intelligence for at least one function selected from the list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management. The set of adaptive intelligence systems 614 may be organized as the following tiers: The platform and the artificial intelligence systems therein can operate on or respond to data collected by and / or generated by other systems in the adaptive intelligence systems tier (e.g., data processing systems, expert systems, machine learning systems, etc.).

[0206] In addition to providing federated intelligence configured for a particular category of goods, federated intelligence may be provided for particular value chain entities 652, such as supply chain operators, businesses, enterprises, etc., that participate in the supply chain for a category of goods.

[0207] Providing tailored intelligence may include employing neural networks to process at least one of the inputs and outputs of a set of demand management and supply chain applications. Neural networks can be used in demand applications such as demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, marketing analytics applications, customer relationship management applications, search engine optimization applications, sales management applications, advertising network applications, behavior tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, and product or service recommendation engines. Neural networks can also be used in supply chain applications such as product timing management applications, product quantity management applications, logistics management applications, shipping applications, delivery applications, ordering of product management applications, and parts management applications. The neural network may provide coordinated intelligence by processing data available in any of multiple value chain data sources for a category of goods, including, but not limited to, processes, bills of materials, weather, traffic, design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, customer experience management (CEM) systems, service lifecycle management (SLM) systems, product lifecycle management (PLM) systems, etc. Neural networks configured to provide collaborative intelligence may share their adaptive capabilities with other adaptive intelligence systems 614, such as when these systems are configured in a topology that facilitates such shared adaptation.In embodiments, the neural network may facilitate providing available value chain / supply chain network resources to both a set of demand management applications and a set of supply chain applications. In embodiments, the neural network may provide coordinated intelligence to improve at least one of a list of outputs consisting of process outputs, application outputs, process results, application results, etc.

[0208] Referring to FIG. 15 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a hybrid set of adaptive intelligence systems 614. The hybrid set of adaptive intelligence systems 614 provides coordinated intelligence through the application of artificial intelligence, such as through the application of a hybrid artificial intelligence system 3060, and optionally one or more expert systems, machine learning systems, etc., for use with a set of demand management applications 824 and a set of supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The hybrid adaptive intelligence system 614 can provide two types of artificial intelligence systems, Type A 3052 and Type B 3054, through a set of data processing, artificial intelligence, and computation systems 634. In an embodiment, the hybrid adaptive intelligence system 614 is selectable and / or configurable via the user interface 3020 such that one or more of the hybrid adaptive intelligence system 614 can operate on or in cooperation with a set of supply chain applications (e.g., demand management application 824 and supply chain application 812). The hybrid adaptive intelligence system 614 may include a hybrid artificial intelligence system 3060 that may include at least two types of artificial intelligence capabilities, including any of various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.The hybrid adaptive intelligence system 614 may facilitate applying a first type of artificial intelligence system 1160 to a set of demand management applications 824 and a second type of artificial intelligence system 1160 to a set of supply chain applications 812, where each of the first type and second type of artificial intelligence systems 1160 may operate independently, operate cooperatively, and optionally coordinate operation to provide coordinated intelligence for the operation of a value chain that produces at least one commodity within a category of commodity 3010.

[0209] In an embodiment, user interface 3020 may include an interface for configuring hybrid artificial intelligence system 3060 to take inputs from selected data sources of the value chain (such as data sources used by set of demand management applications 824 and / or set of supply chain applications 812) and feed them to at least one of two types of artificial intelligence systems (the types of which are described throughout this disclosure and in documents incorporated by reference herein) of hybrid artificial intelligence system 3060 to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of category of commodity 3010. In an embodiment, the selected data sources of the value chain may be applied as inputs for classification or prediction, or as outcomes related to the value chain, category of commodity 3010, etc.

[0210] In an embodiment, the hybrid adaptive intelligence system 614 provides a plurality of separate artificial intelligence systems 1160, a hybrid artificial intelligence system 3060, and combinations thereof. In an embodiment, any of the plurality of separate artificial intelligence systems 1160 and the hybrid artificial intelligence system 3060 may be configured as a plurality of neural network-based systems, such as a classification adaptive neural network, a predictive adaptive neural network, etc. As an example of the hybrid adaptive intelligence system 614, a machine learning-based artificial intelligence system may be provided for the set of demand management applications 824, and a neural network-based artificial intelligence system may be provided for the set of supply chain applications 812. The hybrid adaptive artificial intelligence system 3060 may provide a hybrid artificial intelligence system 3060 that may include a first type of artificial intelligence applied to the demand management applications 824 and that is different from a second type of artificial intelligence applied to the supply chain applications 812. The hybrid artificial intelligence system 3060 may include any combination of types of artificial intelligence systems, including a plurality of first type artificial intelligences (e.g., neural networks) and at least one second type artificial intelligence (e.g., an expert system), etc. In an embodiment, the hybrid artificial intelligence system may configure a hybrid neural network that applies a first type of neural network for demand management applications 824 and a second type of neural network for supply chain applications 812. Additionally, the hybrid artificial intelligence system 3060 may provide two types of artificial intelligence for different applications, such as different demand management applications 824 (e.g., a sales management application and a demand forecasting application) or different supply chain applications 812 (e.g., a logistics management application and a production quality control application).

[0211] In embodiments, the hybrid adaptive intelligence system 614 may be applied as separate artificial intelligence capabilities to separate demand management applications 824. By way of example, tailored intelligence from hybrid artificial intelligence capabilities may be provided to demand planning applications by feedforward neural networks, demand forecasting applications by machine learning systems, sales applications by self-organizing neural networks, future demand aggregation applications by radial basis function neural networks, marketing applications by convolutional neural networks, advertising applications by recurrent neural networks, e-commerce applications by hierarchical neural networks, marketing analytics applications by probabilistic neural networks, customer relationship management applications by associative neural networks, etc.

[0212] Referring to FIG. 16 , a management platform for an information technology system, such as a management platform for a goods and / or services value chain, is depicted as a block diagram of functional elements and representative interconnections for providing a set of forecasts 3070. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide the set of forecasts 3070 through the application of artificial intelligence, such as through the application of artificial intelligence systems 1160, and optionally through one or more expert systems, machine learning systems, etc., for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 can provide the set of forecasts 3070 through a set of data processing, artificial intelligence, and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the set of coordinated value chain applications. The adaptive intelligence systems 614 can include artificial intelligence systems that provide artificial intelligence capabilities known to be associated with artificial intelligence. Intelligence including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.The adaptive intelligence system 614 may facilitate applying adaptive intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812, such as by generating a forecast set 3070 that may facilitate coordinating the two sets of value chain applications, or at least may facilitate coordinating at least one demand management application and at least one supply chain application from each set.

[0213] In some embodiments, the set of forecasts 3070 includes at least one prediction of an impact on a supply chain application based on the current state of the coordinated demand management applications, such as a prediction that demand for a good will decrease more quickly than previously predicted. Conversely, the set of forecasts 3070 includes at least one prediction of an impact on a demand management application based on the current state of the coordinated supply chain applications, such as a prediction that a supply shortage of a good will likely impact demand measurements for a related good. In some embodiments, the set of forecasts 3070 is a set of predictions of adjustments in supply necessary to meet demand. Other predictions include at least one prediction of a change in demand that will impact supply. Still other predictions in the set of forecasts predict a change in supply that will impact at least one of the set of demand management applications, such as a promotion application for at least one product in a category of products. A prediction in the set of forecasts may be as simple as a prediction of the likelihood that the supply of a product in a category of products will not meet the demand set by a demand setting application.

[0214] In embodiments, the adaptive intelligence system 614 may provide a set of artificial intelligence capabilities to facilitate providing a set of predictions for a coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to predict fault or problem conditions in demand management applications, such as a lack of sufficient validated feedback. The probabilistic neural network may be used to predict problem conditions based on a collection of machine operation information and machine preventive maintenance information for machines that perform value chain operations (e.g., production machines, automated handling machines, packaging machines, shipping machines, etc.).

[0215] In an embodiment, the set of predictions 3070 may be provided directly by the management platform 102 through a set of adaptive artificial intelligence systems.

[0216] In an embodiment, the forecast set 3070 may be provided for a tuned set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities to tune the set of demand management applications and supply chain applications.

[0217] In an embodiment, the forecast set 3070 may be a forecast of outcomes for operating a value chain using a tailored set of demand management and supply chain applications for a category of goods, allowing a user to run test cases of the tailored set of demand management and supply chain applications to determine which sets are likely to produce desirable outcomes (viable candidates for applications of the tailored set) and which sets are likely to produce undesirable outcomes.

[0218] 17 , a management platform for an information technology system, such as a management platform for a goods and / or services value chain, is depicted as a block diagram of functional elements and representative interconnections for providing a set of taxonomies 3080. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide the set of taxonomies 3080 through the application of artificial intelligence, such as by application of artificial intelligence systems 1160, and optionally through one or more expert systems, machine learning systems, etc., for use with a coordinated set of demand management applications 824 and supply chain applications 812 for categories of goods 3010 that may be produced, sold, resold, rented, leased, transferred, serviced, recycled, renewed, enhanced, etc., throughout the value chain. The adaptive intelligence systems 614 can provide the set of taxonomies 3080 through a set of data processing, artificial intelligence, and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the set of coordinated value chain applications. The adaptive intelligence systems 614 may include artificial intelligence systems that provide classification capabilities through, among other things, any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.The adaptive intelligence system 614 may facilitate applying adaptive intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812, such as by generating a set of classifications 3080 that may facilitate coordinating the two sets of value chain applications, or at least may facilitate coordinating at least one demand management application and at least one supply chain application from each set.

[0219] In an embodiment, the set of categories 3080 includes at least one classification of the current state of a supply chain application for use by the adjusted demand management application, such as a classification of a problem condition that may affect the operation of a demand management application, such as a marketing application. Such classifications are useful in determining how to adjust market expectations for a commodity that is likely to have a lower yield than previously expected. The converse may also be true in that the set of categories 3080 includes at least one classification of the current state of a demand management application and its relationship to the adjusted supply chain application. In an embodiment, the set of categories 3080 is a set of classifications of adjustments in supply needed to meet demand; for example, an adjustment to production labor needs would be classified differently from an adjustment to a third-party logistics provider. Another classification may include at least one classification of a perceived change in demand and the resulting potential impact on supply management. Additionally, another classification in the set of categories may include the impact of a supply chain application on at least one of the set of demand management applications, such as a promotion application for at least one commodity in a commodity category. A classification in the set of categories may be as simple as classifying the likelihood that the supply of a commodity in a commodity category will not meet the demand set by a demand setting application.

[0220] In embodiments, the adaptive intelligence system 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of classifications 3080 for the coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to classify fault or problem conditions of demand management applications, such as classifying a lack of sufficient validated feedback. The probabilistic neural network may be used to classify problem conditions of machines that perform value chain operations (e.g., production machines, automated processing machines, packaging machines, shipping machines, etc.) as related to at least one of machine operational information and preventive maintenance information.

[0221] In an embodiment, the set of classifications 3080 may be provided directly by management platform 102 through a set of adaptive artificial intelligence systems. Additionally, the set of classifications 3080 may be provided for a tailored set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities to tailor the set of demand management applications and supply chain applications.

[0222] In an embodiment, the set of classifications 3080 may be a classification of results for operating a value chain using a tailored set of demand management applications and supply chain applications for a category of goods, allowing a user to run test cases of the tailored set of demand management applications and supply chain applications to determine which sets may produce results classified as desirable (e.g., viable candidates for the tailored set of applications) and results classified as undesirable.

[0223] In an embodiment, the set of classifications may be comprised of a set of adaptive intelligence functions, such as a neural network, that may be adapted to classify information related to categories of goods. In one example, the neural network may be a multi-layer feed-forward neural network.

[0224] In an embodiment, performing the classification may include classifying the discovered value chain entities as either demand centric or supply centric.

[0225] In embodiments, the set of classifications 3080 may be achieved through the use of the artificial intelligence system 1160 to coordinate the set of coordinated demand management applications and supply chain applications. The artificial intelligence system may configure and generate the set of classifications 3080 as a means by which the demand management applications and supply chain applications can be coordinated. In one example, classifications of information flows throughout a value chain may be classified as relevant to both demand management applications and supply chain applications. This common association may be a point of coordination between the applications. In embodiments, the set of classifications may be artificial intelligence-generated classifications resulting from operating a supply chain that relies on the coordinated demand management applications 824 and supply chain applications 812.

[0226] Referring to FIG. 18 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections for achieving automated control intelligence. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide automated control signaling 3092 to a coordinated set of demand management applications 824 and supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 can provide the automated control signals 3092 through a set of data processing, artificial intelligence, and computational systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can automatically control a set of supply chain applications (e.g., the demand management applications 824 and the supply chain applications 812). The adaptive intelligence system 614 may include artificial intelligence, including any of a variety of expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in documents incorporated by reference.

[0227] In embodiments, the user interface 3020 may include an interface for configuring the adaptive intelligence system 614 to take inputs from selected data sources of the value chain 3094 (such as data sources used by the coordinated set of demand management applications 824 and / or the set of supply chain applications 812) and feed them to a neural network, or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated by reference herein, to generate automated control signals 3092, for example, to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of the category of commodity 3010. In embodiments, the selected data sources of the value chain may be used to determine aspects of the automated control signals, such as temporal adjustments to control outcomes associated with the value chain for at least the category of commodity 3010.

[0228] In one example, the set of automated control signals can include at least one control signal for automating the execution of a supply chain application, such as a production start, automated material ordering, inventory check, or billing application, in a coordinated set of demand management applications and supply chain applications. In yet another example of automated control signal generation, the set of automated control signals can include at least one control signal for automating the execution of a demand management application, such as a product recall application, an email distribution application, or the like, in a coordinated set of demand management applications and supply chain applications. In yet another example, the automated control signal can control the timing of a demand management application based on the supply status of a commodity.

[0229] In embodiments, the adaptive intelligence system 614 can apply machine learning to the results of supply chain management to automatically adapt the set of demand management application control signals. Similarly, the adaptive intelligence system 614 can apply machine learning to the results of demand management to automatically adapt the set of supply chain application control signals. The adaptive intelligence system 614 can provide further processing for automatic control signal generation, such as by applying artificial intelligence to determine aspects of the value chain that affect the automated control of the coordinated set of demand management and supply chain applications for a category of goods. The determined aspects can be used in the generation and operation of automatic control intelligence / signals, such as by filtering value chain information for aspects that do not affect the targeted demand management and supply chain applications.

[0230] For example, automated controls of a supply chain application may be limited by policies, operational limits, safety constraints, etc. A set of adaptive intelligence systems may determine ranges of supply chain application control values ​​within which the controls may be automated. In embodiments, the ranges may be associated with supply rates, supply timing rates, mix of products within a category of products, etc.

[0231] Embodiments are described herein for using artificial intelligence systems or capabilities to identify, configure, and adjust automated control signals. Such embodiments may further include a closed loop of feedback (e.g., status information, output information, outcomes, etc.) from a coordinated set of demand management and supply chain applications, optionally processed using machine learning, and used to adapt the automated control signals for at least one commodity within a category of commodities. The automated control signals may be adapted based on an indication of feedback from the supply chain applications that, for example, the yield of a certain commodity suggests a production problem. In this example, the automated control signals may affect production rates, and the feedback may cause the signals to automatically self-adjust to a slower production rate until the production problem is resolved.

[0232] Referring to FIG. 19 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections for providing information routing recommendations. The management platform includes a set of value chain networks 3102 from which network data 3110 is collected from a series of information routing activities, including results, parameters, routing activity information, etc. Among the set of value chain networks 3102, a selected value chain network 3104 is selected for which at least one information routing recommendation 3130 is provided. The artificial intelligence system 1160 can include a machine learning system and can be trained using a training set derived from the results, parameters, and routing activity information of the network data 3110 for the set of value chain networks 3102. The artificial intelligence system 1160 can further provide the information routing recommendation 3130 based on the current status 3120 of the selected value chain network 3104. The artificial intelligence system can use machine learning to train on information transaction types within the set of value chain networks 3102, thereby learning appropriate factors for different transaction types (e.g., real-time inventory updates, buyer credit checks, engineering sign-offs, etc.) and contributing information routing recommendations accordingly. The artificial intelligence system can also use machine learning to train on information value for different types and / or classes of information routed within and throughout the set of value chain networks 3102. Information may be evaluated on a wide range of factors, including the timing of information availability and the timing of information consumption, as well as information content-based value, such as information that prevents a value chain network element (e.g., a production provider) from performing a desired action (e.g., starting mass production without a work order). Thus, information routing recommendations may be based on training on transaction type, information value, and combinations thereof.These are merely exemplary information routing recommendation training and recommendation building blocks, and are presented here without limitation as a basis for other training and recommendation blocks.

[0233] In embodiments, the artificial intelligence system 1160 may provide information routing recommendations 3130 based on transaction type, transaction type and information type, network type, etc. The information routing recommendations may be based on a combination of factors such as information type and network type, such as when an information type (streaming) does not match a network type (small transactions).

[0234] In embodiments, the artificial intelligence system 1160 may use machine learning to develop an understanding of the networks in the selected value chain network 3104, such as network topology, network load, network reliability, and network latency. This understanding may be combined with, for example, detected or predicted network conditions to form information routing recommendations. Aspects such as the presence of edge intelligence in the value chain network 3104 may influence one or more information routing recommendations. As an example, the type of information may be incompatible with the type of network, but the network may be configured with edge intelligence that allows the artificial intelligence system 1160 to adapt the format of the routed information to make it compatible with the type of network being targeted. This is also an example of a more general consideration for information routing recommendations, which are network resources (e.g., presence, availability, and capabilities), such as edge computing, server access, and network-based storage resources. Similarly, value chain network entities may influence information routing recommendations. In embodiments, the information routing recommendation may avoid routing information that is confidential to a first supplier in the value chain through network nodes controlled by the supplier's competitors. In an embodiment, the information routing recommendation may include routing to a first node where the information is partially consumed and partially processed for further routing, such as by splitting the partially processed portion into destination-specific information sets for further routing.

[0235] In embodiments, the artificial intelligence system 1160 can provide information routing recommendations based on goals, such as value chain network goals and information routing goals. The goal-based information routing recommendations may include routing goals such as a quality of service routing goal, a routing reliability goal (which may be measured based on transmission failure rates, etc.). Other goals may include latency measurements associated with one or more candidate routes. Information routing recommendations may be based on information availability in the selected value chain network (e.g., when the information is available and needs to be delivered). For information available well in advance of when it is needed (e.g., nightly production reports routable at 2:00 AM are first needed by 7:00 AM), routing recommendations may include using lower-cost resources, involving shorter routing delays, etc. For information available just before it is needed (e.g., product test results needed within a few hundred milliseconds of the test completion to maintain production uptime),

[0236] Information routing recommendations may be formed by the artificial intelligence system 1160 based on information permanence factors, such as the time the information is available for immediate routing within the value chain network. Information routing recommendations that factor information permanence may select network resources based on availability, cost, etc., during the time of information permanence.

[0237] Information value and its impact on information value may influence information routing recommendations. As an example, information valid for a single shipment (e.g., production volume of a product) may lose substantial value once the shipment is satisfactorily received. In such an example, an information routing recommendation may indicate routing the relevant information to all of the highest priority consumers of that information while it is still valid. Similarly, the routing of information consumed by multiple value chain entities may need to be coordinated so that each value chain entity receives the information at a desired time / moment, such as during the same production shift, at the start of work (which may be different if the entities are in different time zones).

[0238] In embodiments, information routing recommendations may be based on the topology of the value chain, the location and availability of network storage resources, and the like.

[0239] In embodiments, one or more information routing recommendations may be adapted while the information is being routed based on, for example, changes in network resource availability, discovery of network resources, dynamic load on the network, priority of recommendations generated after the information for the first recommendation is in-route, etc.

[0240] Referring to FIG. 20 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections for semi-perceptual problem recognition of pain points in a value chain network. The management platform includes a set 3152 of value chain network entities from which entity-related data 3160 is collected, including entity-related outcomes, parameters, activity information, etc. Among the set 3152 of value chain network entities, a set 3154 of value chain network entities is selected in which at least one pain point problem state 3172 is detected. The artificial intelligence system 1160 can be trained with a training set derived from the entity-related data 3160, including training on outcomes related to the value chain entities, e.g., parameters related to value chain operation, value chain activity information, etc. The artificial intelligence system can further employ machine learning to facilitate learning of problem state factors 3180 that may characterize the problem state input as training data. These factors 3180 may further be used by an instance of artificial intelligence 1160' running on a computing resource 3170 that is local to the value chain network entity experiencing the pain point problem / outcome. The goal of this configuration of artificial intelligence systems, datasets, and value chain networks is to recognize problem conditions in selected portions of the value chain.

[0241] In embodiments, recognizing a problem state may be based on variance analysis of value chain measurements (e.g., load, wait time, delivery time, cost, etc.), particularly variances occurring over time in a particular measurement. Variances exceeding a variance threshold (e.g., an arbitrarily dynamic range of results of value chain operations such as production, shipping, customs clearance, etc.) may indicate a pain point.

[0242] In addition to detecting problem conditions, the platform 102 predicts pain points based at least in part on correlations with detected problem conditions, such as through semi-perceptual problem recognition methods. Correlations may be derived from the value chain; for example, a shipper cannot deliver international goods until they are processed by customs, or sales forecasts cannot be provided with high reliability without high-quality field data, etc. In embodiments, predicted pain points may be points in value chain activities further along the supply chain, activities occurring in related activities (e.g., tax planning related to tax law), etc. Predicted pain points may be assigned a risk value based on aspects of the detected problem condition and the correlation between the activities of the predicted pain point and the activities of the problem condition. If a production operation can receive materials from two suppliers, a problem condition with one of the suppliers may indicate a low risk of the pain point in the use of the material. Similarly, if a demand management application indicates high demand for a product and a problem is detected with the information underlying that demand, the risk of excess inventory (a pain point) may be high depending, for example, on how far along the value chain the product is.

[0243] In embodiments, semi-perceptual problem recognition may involve more than a simple concatenation of data and operational states of entities engaged in the value chain. Problem recognition may also be based on human factors, such as perceived stress of a production supervisor, shipper, etc. Human factors used for semi-perceptual problem recognition may be collected from sensors (e.g., wearable physiological sensors, etc.) that facilitate detection of human stress levels, etc.

[0244] In embodiments, semi-perceptual problem recognition may also be based on unstructured information, such as digital communications, voice messaging, etc., that may be shared among, originate from, or be received by humans involved in value chain operations. As an example, natural language processing of email communications between employees within a company may indicate, for example, levels of discomfort with a supplier in the value chain. Data related to suppliers (e.g., on-time production, quality, etc.) may be within the value chain. Information within this unstructured content may indicate potential pain points, such as personal issues with key supplier participants. Natural language processing, artificial intelligence, and optionally machine learning may be employed to enhance problem state recognition.

[0245] In embodiments, semi-perceptual problem recognition may be based on an analysis of variances of measures of value chain operations / entities / applications, including the variance of a given measure over time, the variance of two related measures, etc. In embodiments, variances of results over time may indicate a problem condition and / or suggest a pain point. In embodiments, an artificial intelligence-based system may determine an acceptable range of result variance and apply that range to measurements of a selected set of value chain network entities, such as entities that share one or more similarities, to facilitate detection of a problem condition. In embodiments, the acceptable range of result variance may indicate a problem condition trigger threshold that can be used by a local instance of the artificial intelligence to signal a problem condition. In such a scenario, a problem condition may be detected when at least one measurement of a value chain activity / entity, etc., is greater than a problem condition threshold determined by the artificial intelligence. Variation analysis for problem condition detection may include detecting variability in start / end times of scheduled value chain network entity activities, variability in at least one of production time, production quality, production rate, production start time, production resource availability or trends, variability in measures of shipping supply chain entities, variability in transfer times from one transportation mode to another (e.g., when the variability is greater than a transportation mode problem condition threshold), variability in quality testing, etc.

[0246] In embodiments, the semi-perceptual problem recognition system can include machine learning / artificial intelligence predicting further correlated pain points along the supply chain from the detected pain points, such as the risk and / or need for overtime, expedited shipping, discounts on product prices, etc.

[0247] In an embodiment, the machine learning / artificial intelligence system is capable of processing results, parameters, and data collected from a set of data sources associated with a set of value chain entities and activities to detect at least one pain point selected from a list of pain points consisting of: delayed shipments, damaged containers, damaged goods, wrong goods, customs delays, unpaid duties, weather events, damaged infrastructure, blocked waterways, incompatible infrastructure, congested ports, congested handling infrastructure, congested roads, congested distribution centers, rejected goods, returned goods, waste, wasted energy, wasted labor, wasted labor, poor customer service, empty transport vehicles on return trips, excessive fuel prices, excessive duties, etc.

[0248] Referring to FIG. 21 , an information technology system management platform, such as a goods and / or services value chain management platform, is depicted as a block diagram of functional elements and representative interconnections that automate the coordination of value chain network activities for an enterprise's product suite. The management platform includes a set of networked value chain network entities 3202 that generate activity information 3208 used by an artificial intelligence system 1160 to automate the coordination 3220 of value chain network activities 3212 for a set of products 3210 for the enterprise 3204. In an embodiment, a value chain monitoring system 614 monitors the activities of the set of networked value chain entities 3202 and can cooperate with a data collection and management system 640 to collect and store value chain entity monitoring information, such as activity information, configuration information, and the like. This collected information may be configured as activity information 3208 regarding a set of activities associated with the set of products 3210 for the enterprise 3204. In an embodiment, the artificial intelligence system 1160 may use an application programming interface 642 to automate access to the monitored activity information 3208.

[0249] A value chain may include multiple interconnected entities, each performing several activities to complete the value chain. While humans play a significant role in some activities within a value chain network, more automated coordination and unified orchestration of supply and demand may be achieved using artificial intelligence-based systems (e.g., machine learning, expert systems, self-organizing systems, etc., including systems such as those described herein and the documents incorporated by reference) to coordinate supply chain activities. The use of artificial intelligence not only provides greater capabilities to end users, but may also further enrich the emerging nature of self-adaptive systems, including Internet of Things (IoT) devices and intelligent products, which can play a key role in automatically coordinating supply chain activities.

[0250] For example, an IoT system deployed at a fulfillment center 628 may interface with an intelligent product 1510 to accept customer feedback regarding the product 1510, and an application 630 for the fulfillment center 628, upon receiving customer feedback via a connection path to the intelligent product 1510 about a problem with the product 1510, may initiate a workflow to perform corrective actions on similar products 650 before the product 650 is shipped from the fulfillment center 628. The workflow may be configured by an artificial intelligence system 1160 that analyzes the problem with the product 1510, develops an understanding of the value chain network activities that produce the product, determines resources required for the workflow, coordinates with inventory and production systems to adapt existing workflows, etc. The artificial intelligence system 1160 may further coordinate with a demand management application to address temporary impacts to product availability, etc.

[0251] In embodiments, automated coordination of a set of value chain network activities for a set of products for an enterprise can rely on the coordinated intelligence methods and systems described herein, including, optionally, using artificial intelligence to provide coordinated intelligence, coordinate activities, etc., to facilitate coordination of demand management activities, supply chain activities, etc. As an example, artificial intelligence may facilitate determining relationships between value change network activities based on inputs used by the activities and results produced by the activities. Artificial intelligence can be integrated with and / or collaborate with platform activities, such as value chain network entity activities, to continuously monitor activities, identify temporal aspects requiring adjustment (e.g., when a change in supply temporarily impacts demand activity), and automate such adjustments. Automated coordination of value chain network activities within and between value chain network entity activities can benefit from advanced artificial intelligence systems that may enable the use of different artificial intelligence capabilities for any value chain set of entities, applications, or conditions. The use of hybrid artificial intelligence systems can provide benefits by applying two or more types of intelligence to a set of conditions to facilitate automated selection by humans and / or computers. Artificial intelligence can further enhance the automated coordination of value chain network entity activities through intelligent operations such as generating prediction sets, generating classification sets, generating automated control signals (which may be communicated between value chain network entities, etc.), etc. Other exemplary artificial intelligence-based influences on the automated coordination of value chain network entity activities include machine learning-based information routing and recommendations therefor, semi-perceptual problem recognition based on both structured (e.g., production data) and unstructured (e.g., human emotion) sources, etc.The artificial intelligence system can facilitate automatic adjustment of a set of products or the enterprise's value chain network entity activities based on adaptive intelligence provided by the platform for a category of goods into which the enterprise's set of products may be grouped. As an example, adaptive intelligence may be provided by the platform for a curtain hanger category of goods, and the enterprise's product set may include a line of adaptive curtain hangers. Through the understanding developed of the overall curtain hanger category, artificial intelligence capabilities may be applied to the enterprise's value chain network activities to automate aspects of the value chain, such as the exchange of information between activities.

[0252] Digital Twin System in Value Chain Entity Management Platform 22 , the adaptive intelligence layer 614 may include a value chain network digital twin system 1700, which may include a set of components, processes, services, interfaces, and other elements for the development and deployment of digital twin capabilities for visualization of various value chain entities 652, environments, and applications 630, as well as collaborative intelligence (including artificial intelligence 1160, edge intelligence 1400, analytics, and other capabilities) and other value-added services and capabilities enabled or facilitated by the digital twin 1700. Without limitation, the digital twin 1700 may be used and / or applied to each of the processes managed, controlled, or mediated by each of the application set 614 in the platform application layer.

[0253] In embodiments, digital twin 1700 may leverage the presence of multiple applications 630 within value chain management platform 604 such that the set of applications share data sources (e.g., in data storage layer 624) and other inputs (e.g., from monitoring layer 614) collected about value chain entities 652, and share outputs, events, state information, and outputs, which collectively may provide a much richer environment for enriching the content within digital twin 1700, including the use of artificial intelligence 1160 (including various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and any of the other systems described throughout this disclosure and in documents incorporated by reference), through the use of content collected by monitoring layer 614 and data collection systems 640.

[0254] In embodiments, digital twin 1700 may be used in connection with a sharing or convergence process between various sets of applications 630 of applications 604, such as, but not limited to, a convergence process including a security application 834 and an inventory management application 820, integrated automation between a blockchain-based application 844 and a facilities management application 850, and many others. In embodiments, the convergence process may include a shared data structure for multiple applications 630 (including those that track the same transactions on the blockchain but may consume different subsets of available attributes of data objects held on the blockchain, or those that use a common set of nodes and links in a knowledge graph) that may be connected to digital twin 1700 so that digital twin 1700 is updated accordingly. For example, a transaction indicating a change in ownership of entity 652 may be stored on the blockchain and used by multiple applications 630 to enable role-based access control, role-based permissions for remote control, identity-based event reporting, etc., and connected to and shared with digital twin 1700 so that digital twin 1700 is updated accordingly. In an embodiment, a converged process may include a process flow shared across applications 630, including a subset of a larger flow involving one or more of a set of applications 614 that may be connected to and shared with digital twin 1700 so that digital twin 1700 is updated accordingly. For example, an inspection flow for value chain network entity 652 may be useful to analytics solution 838, asset management solution 814, etc.

[0255] In embodiments, digital twin 1700 may be provided for a wide variety of value chain network applications 630 mentioned throughout this disclosure and the documents incorporated herein by reference. The environment for development of digital twin 1700 may include a set of interfaces for a developer to configure artificial intelligence system 1160 to capture inputs from selected data sources in data storage layer 624 and events or other data from monitoring system layer 614 and provide them for inclusion in digital twin 1700. The development environment for digital twin 1700 may be configured to capture outputs and results from various applications 630.

[0256] Digital twin of the value chain network Referring to FIG. 23 , any of the value chain network entities 652 can be depicted in a set of one or more digital twins 1700, such as by inputting value chain network data objects 1004, such as event data 1034, state data 1140, or other data relating to the value chain network entity 652, application 630, or components or elements of the platform 604 as described throughout this disclosure, into the digital twin 1700.

[0257] Thus, the platform 604 may include, integrate, integrate, manage, control, coordinate, or otherwise handle any of a wide variety of digital twins 1700, such as a distribution twin 1714 (such as representing a distribution facility, asset, object, worker, etc.); a warehouse twin 1712 (such as representing a warehouse facility, asset, object, worker, etc.); a port infrastructure twin 1714 (such as representing a seaport, airport, other facility, asset, object, worker, etc.); a shipping facility twin 1720; an operations facility twin 1722; a customer twin 1730 (such as representing the physical, behavioral, demographic, psychographic, financial, historical, affinities, interests, and other characteristics of a customer group or individual customer); a worker twin 1740 (such as representing a worker's physical attributes, physiological data, state data, psychographic information, emotional state, fatigue / energy state, attention state, skills, training, abilities, roles, authorities, responsibilities, work status, activities, and other attributes); Able / Portable Devices Twin 1750, Process Twin 1760, Machinery Twin 21010 (e.g., various machines used to support the Value Chain Network 668), Product Twin 1780, Origin Twin 1560, Supplier Twin 1630, Supply Factors Twin 1650, Offshore Facilities Twin 1572, Floating Assets Twin 1570, Shipyard Twin 1620, Destination Twin 1562, Fulfillment Twin 1600, Delivery Systems Twin 1610, Demand Factors Twin 16 40, Retailers Twin 1790; E-commerce & Online Site Operators Twin 1800; Waterways Twin 1810; Roadways Twin 1820; Railways Twin 1830; Aviation Facilities Twin 1840 (including aircraft, runways, airports, hangars, warehouses, airways, refueling facilities, and other property, goods, and labor used in connection with the air transportation of goods Twin 650); Autonomous Vehicles Twin 1850; Robotics Twin 1860; Drones Twin 1870; Logistics Factors Twin 1880; etc.Each of these can have the characteristics of a digital twin described throughout this disclosure and the documents incorporated by reference herein, such as mirroring or reflecting changes in state of associated physical objects or other entities, providing functionality for modeling the behavior or interactions of associated physical objects or other entities, enabling simulation, providing a display of state, and many others.

[0258] In example embodiments, the digital twin system can be configured to generate various enterprise digital twins 1700 associated with a value chain (e.g., specifically, value chain network entities 652). For example, a company that produces goods internationally (or across multiple facilities) can configure a set of digital twins 1700, such as a supplier twin representing the company's supply chain, a factory twin for various production facilities, a product twin representing the products manufactured by the company, a distribution twin representing the company's distribution chain, and other suitable twins. In doing so, the company can define the structural elements of each digital twin and the system data corresponding to the structural elements of the digital twin. For example, when generating a production facility twin, the company can define the layout and spatial definition of the facility and the processes performed at the facility. The company may also define data sources corresponding to the value chain network entities 652, such as sensor systems, smart manufacturing equipment, inventory systems, and logistics systems that provide data related to the facility. The company can associate the data sources with elements of the production facility and / or the processes occurring at the facility. Similarly, the company can define the structure, process, and layout definitions of its supply chain and distribution chain and connect relevant data sources, such as supplier databases and logistics platforms, to generate twins for the respective distribution and supply chains. An enterprise can further correlate these digital twins to have a view of its value chain. In embodiments, the digital twin system can run a simulation of the enterprise. The value chain incorporates real-time data from various value chain network entities 652 of the enterprise.In some of these embodiments, the digital twin system can recommend to a user interacting with the enterprise digital twin 1700 decisions such as when to order a particular part to manufacture a particular product, when to schedule maintenance on a machine and / or when to replace a machine given the predicted demand for the manufactured product (e.g., when a digital simulation on the digital twin indicates that demand for a particular product is likely to be lowest or have the least impact on the enterprise's profit and loss statement), what day and time to ship an item, etc. The foregoing examples are non-limiting examples of how a digital twin can ingest system data and run simulations to achieve one or more goals.

[0259] Entity discovery and interaction management Referring to FIG. 24, the monitoring system layer 614, which includes various data collection systems 640 (such as IoT data collection systems, data collection systems that search social networks, websites, and other online resources, crowdsourcing systems, etc.), may include a set of entity discovery systems 1900 for identifying a set of value chain network entities 652, for identifying types of value chain network entities 652, for identifying specific value chain network entities 652, etc., and for managing the identities of the value chain network entities 652, including for resolving ambiguity (such as when a single entity is identified differently in different systems, when different entities are identified similarly), for de-duplication of entity identities, for resolution of entity identities, for enrichment of entity identities (such as enriching data objects with additional data collected about the entities within the platform), etc. Entity discovery 1900 may also include discovery of interactions between entities, such as how entities are connected (e.g., by what network connections, data integration systems, and / or interfaces), what data is exchanged between entities (including what types of data objects are exchanged, what common workflows involve the entities, what inputs and outputs are exchanged between entities, etc.), what rules or policies govern the entities, etc. Platform 604 may include a set of entity interaction management systems 1902, which may consist of one or more artificial intelligence systems (including any of the types described throughout this disclosure) for managing the set of interactions between entities discovered through entity discovery 1900, including those that rely on a training set of data to manage interactions between entities based on how the entities have been managed by human supervisors or other systems.

[0260] As an illustrative example among many possibilities, the entity discovery system 1900 may be used to not only discover network-connected cameras that mark the loading docks of facilities that produce products for a company, but also to identify what interface or protocol is required to access the feed of video content from the cameras. The entity interaction management system 1902 may then be used to interact with the interface or protocol to configure access to the feed and provide the feed to another system for further processing, such as having the artificial intelligence system 1160 process the feed to discover content related to the company's activities. For example, the artificial intelligence system 1160 may process image frames from the video feed to find markings (e.g., produce labels, SKUs, images, logos), shapes (e.g., packages of a particular size or shape), activities (e.g., loading or unloading activities), etc. that indicate that product has moved within the loading dock. This information may be used to substitute for, augment, or verify other information, such as RFID tracking information. Similar discovery and interaction management activities may be performed with any of the types of value chain network entities 652 described throughout this disclosure.

[0261] Robotic Process Automation in Value Chain Networks 25, the adaptive intelligence layer 614 may include a robotic process automation (RPA) system 1442, which may include a set of components, processes, services, interfaces, and other elements for the development and deployment of automation capabilities for various value chain entities 652, environments, and applications 630. Without limitation, the robotic process automation 1442 may be applied to each of the processes managed, controlled, or mediated by each of the application set 614 in the platform application layer, to the functions, components, workflows, processes of the VCNP 604 itself, processes including the value chain network entities 652, and other processes.

[0262] In embodiments, robotic process automation 1442 may take advantage of the presence of multiple applications 630 within the value chain management platform 604, a set of such applications may share data sources (such as in the data storage layer 624) and other inputs (such as from the monitoring layer 614) collected about the value chain entities 652, as well as share outputs, events, state information, and outputs, which collectively may provide a much richer environment for process automation, including through the use of artificial intelligence 1160 (including various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and any of the other systems described throughout this disclosure and in the documents incorporated by reference). For example, the asset management application 814 may use robotic process automation 1442 to automate asset inspection processes that are typically performed or overseen by humans (e.g., by automating a process that includes visual inspection using video or still images, such as from a camera displaying images of the entities 652), such as when the robotic process automation 1442 system is trained to automate the inspection by observing the interaction of a set of human inspectors or supervisors with interfaces used to identify, diagnose, measure, parameterize, or otherwise characterize possible defects or advantageous characteristics of equipment or other assets.In embodiments, the human inspector's or supervisor's interaction can include a labeled data set in which labels or tags indicate defect types, advantageous characteristics, or other characteristics, and a machine learning system can be trained to identify the same characteristics using the training data set. The machine learning system can be trained to identify the same characteristics using the training data set, which can then be used to automate the inspection process such that defects or advantageous characteristics are automatically classified and detected in a set of video or still images, which can then be used within the value chain network asset management application 814 to flag items that need further inspection, should be rejected, should be disclosed to a prospective buyer, should be remediated, etc. In embodiments, the robotic process automation 1442 can include multi-application or cross-application sharing of inputs, data structures, data sources, events, states, outputs, or results. For example, the asset management application 814 can receive information from a marketplace application 854 that can enrich the robotic process automation 1442 of the asset management application 814, such as information about the current characteristics of an item from a particular vendor in the supply chain for the asset, which can assist in entering characteristics about the asset for purposes of facilitating the inspection process, negotiation process, delivery process, etc. These and many other examples of multi-application or cross-application sharing for robotic process automation 1442 across applications 630 are encompassed by this disclosure. Robotic process automation 1442 may be used with various functions of VCNP 604. For example, in some embodiments, robotic process automation 1442 may be described as training a robot to operate and automate tasks that were at least largely dominated by humans. One of these tasks may be used to train a robot that can train other robots.The robotic process automation 1442 can be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., a trained agent or a trained robotic process automation system) perform these tasks previously performed by humans. For example, the robotic process automation 1442 may utilize software that can provide software interaction observations (such as mouse movements, mouse clicks, cursor movements, navigation actions, menu selections, keyboard typing, and many others), such as a customer 714 purchasing a product, as recorded and / or tracked by the software interaction observation system 1500. This can include monitoring a user's mouse clicks, mouse movements, and / or keyboard typing to train the robotic system to perform the same clicks and / or typing. In another example, the robotic process automation 1442 may utilize software to simulate physical interactions with the robot and other systems, training the robotic system to sequence or undertake the same physical interactions. For example, a robot may be trained to rebuild a set of bearings by having it watch a video of someone performing this task. This may include tracking physical interactions as well as tracking interactions at the software level. The robotic process automation 1442 can understand what the underlying capabilities are being deployed so that the VCNP 604 can pre-configure combinations of neural networks that can be used to replicate the performance of human capabilities.

[0263] In embodiments, robotic process automation may apply to sharing or convergence processes between various sets of applications 630 in applications 604, such as, but not limited to, convergence processes involving security applications 834 and inventory applications 820, integrated automation between blockchain-based applications 844 and vendor management applications 832, and many others. In embodiments, convergence processes may include shared data structures for multiple applications 630 (including those that track the same transactions on the blockchain but may consume different subsets of available attributes of data objects held on the blockchain, or those that use a common set of knowledge graph nodes and links). For example, a transaction indicating a change in ownership of an entity 652 may be stored on the blockchain and used by multiple applications 630. These applications may include: user-based access control, role-based permissions for remote control, identity-based event reporting, etc. In embodiments, a converged process may include shared process flows across applications 630, including a subset of a larger flow involving one or more of the set of applications 614. For example, a risk management or inspection flow for entity 652 may provide services to inventory management applications 832, asset management applications 814, demand management applications 824, and supply chain applications 812, among others.

[0264] In embodiments, robotic process automation 1442 may be provided for a broad range of value chain network processes (including, but not limited to, all of the applications 630) mentioned throughout this disclosure and the documents incorporated by reference herein. The development environment for robotic process automation for a value chain network may include a set of interfaces for developers to configure the artificial intelligence system 1160 to take inputs from selected data sources in the VCN data storage layer 624, and event data 1034, state data 1140, or other value chain network data objects 1004 from the monitoring system layer 614, and feed them to a neural network, etc., as inputs for classification or prediction, or as outputs related to the platform 102, value chain network entities 652, applications 630, etc. The RPA development environment 1442 may again be configured to take outputs and results 1040 from the various applications 630 to facilitate automated learning and improvement of the classification, prediction, etc. involved in the process steps intended to be automated. In an embodiment, the development environment, and resulting robotic process automation 1442, may include monitoring a combination of both software program interaction observations 1500 (e.g., by workers interacting with various software interfaces of applications 630 involved in the value chain network entities 652) and physical process interaction observations 1510 (e.g., by observing workers interacting with or using machines, equipment, tools, etc. in the value chain network 668). In an embodiment, the software interaction observations 1500 may include interactions between software components with other software components, such as how one application 630 interacts with another application 630 via an API.In embodiments, physical process interaction observations 1510 include observations of how human workers interact with value chain entities 652 (e.g., via video cameras, motion detectors, or other sensors, as well as detection of the location, movement, etc. of hardware, such as robotic hardware) (e.g., worker location (path through a location, process, etc., including where a given type of worker is located during a given series of events); how workers manipulate equipment, cargo, containers, packages, products 650, or other items using various tools, equipment, and physical interfaces; the timing of worker responses with respect to various events (e.g., alerts and responses to alerts); procedures by which workers perform scheduled delivery, movement, maintenance, update, repair, and service processes; procedures by which workers adjust or adjust items involved in a workflow; and many others). Physical process observations 1510 may include tracking worker position, angle, force, velocity, acceleration, pressure, torque, etc., as workers manipulate with tools on hardware, such as containers or packages, or on equipment involved in handling the product. Such observations may be obtained through any combination of video data, data detected within the machine (such as the positions of machine elements detected and reported by position detectors), data collected by wearable devices (such as exoskeletons including position detectors, force detectors, torque detectors, etc. configured to detect physical characteristics of the interactions between human workers and hardware items for the purpose of developing a training dataset). By collecting both software interaction observations 1500 and physical process interaction observations 1510, the RPA system 1442 may more comprehensively automate processes involving value chain entities 652, such as by using software automation in combination with physical robots.

[0265] In embodiments, Robotic Process Automation 1442 is configured to train a set of physical robots that have hardware elements that facilitate taking on tasks traditionally performed by humans. These may include robots to walk (including going up and down stairs to deliver packages), climb (such as climbing a ladder in a warehouse to reach shelves where product 650 is stored), move around a facility, attach to items, grasp items (using robotic arms, hands, pliers, etc.), lift items, carry items, remove and replace items, use tools, and many other tasks.

[0266] Value Chain Management Platform - Integrated Robotic Process Automation for Demand Management and Supply Chain In embodiments, provided herein are methods, systems, components, and other elements for an information technology system that may include a cloud-based management VCNP 604 having a microservices architecture, a set of interfaces 702, a set of network connectivity facilities 642, adaptive intelligence facilities 614, data storage facilities 624, data collection systems 640, and monitoring facilities 614 coordinated for monitoring and managing a set of value chain network entities 652; a collection of applications to enable an enterprise to manage a collection of value chain network entities from point of origin to point of customer use; and a unified collection of robotic process automation systems 1442 that provide coordinated automation among various applications 630, including demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications for a category of goods.

[0267] Accordingly, provided herein are methods, systems, components, and other elements for information technology systems that may include: a cloud-based management platform having a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and managing a set of value chain network entities, and a set of applications to enable an enterprise to manage the set of value chain network entities from origin to point of customer use; and a unified set of robotic process automation systems that provide coordinated automation between at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods.

[0268] Value Chain Management Platform - Robotic Process Automation Services in Microservices Architecture for Value Chain Networks In embodiments, provided herein are methods, systems, components, and other elements for an information technology system that may include a cloud-based management VCNP 102 having a microservices architecture, a set of interfaces 702, a set of network connectivity facilities 642, an adaptive intelligence facility 614, a data storage facility 624, a data collection system 640, and a monitoring facility 614 that are coordinated for monitoring and managing a set of value chain network entities 652; a set of applications for enabling an enterprise to manage the set of value chain network entities from origin to 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 microservices layer including a robotic process automation layer 1442 that uses information collected by the data collection layer 640 and a set of results and activities 1040 including the applications of the application layer 630 to automate a set of actions for at least a subset of the applications 630.

[0269] Accordingly, provided herein are methods, systems, components, and other elements for information technology systems that may include: a cloud-based management platform having a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities tailored for monitoring and managing a set of value chain network entities, and a set of applications for enabling an enterprise to manage the set of value chain network entities from origin to 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 microservices layer including a robotic process automation layer that uses information collected by the data collection layer and a set of results and activities including the applications of the application layer to automate a set of actions of at least a subset of the applications.

[0270] Value Chain Management Platform - Robotic Process Automation for Value Chain Network Processes In embodiments, provided herein are methods, systems, components, and other elements for an information technology system that may include a cloud-based management VCNP 102 having a microservices architecture, a set of interfaces 702 coordinated for monitoring and managing a set of value chain network entities 652, a set of network connectivity facilities 642, adaptive intelligence facilities 614, data storage facilities 624, data collection systems 640, and monitoring facilities 614; a set of applications for enabling an enterprise to manage a set of value chain network entities from origination to point of customer use; and a set of robotic process automation systems 1442 for automating a set of processes within the value chain network, the robotic process automation systems 1442 learning from a training set of data including a set of user interactions with interfaces 702 of the set of software systems used to monitor and manage the value chain network entities 652, as well as the outputs and results 1040 of various processes and applications that may occur with or within the VCNP 102.

[0271] In an embodiment, the value chain network entities 652 may include, for example, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, seaports, airports, airways, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand drivers, supply requirements, etc. may include factors, delivery systems, floating assets, origins, destinations, arrivals, points, points of interest, airways, waterways, roads, railroads, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, delivery systems, floating assets, origins, destinations, storage points, 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, material handling facilities, drones / robots / AVs, waterways, port infrastructure facilities, or many others.

[0272] In embodiments, the robotic process automation layer automates processes such as, but not limited to, the following: selecting product quantities for an order, selecting a carrier for shipping, selecting a vendor for parts, selecting a vendor for a finished product order, selecting product variants for marketing, selecting product assortments for shelves, determining pricing for finished products, configuring service offers related to a product, configuring product bundles, configuring product kits, configuring product packaging, configuring product displays, configuring product images, configuring product descriptions, configuring website navigation paths related to a product, determining product inventory levels, selecting a logistics type, configuring a product delivery schedule, configuring a logistics schedule, configuring an input set for machine learning, preparing product documentation, preparing required product disclosures, configuring a product against a set of local requirements, configuring a product set for compatibility, configuring a request for proposal, ordering equipment for a warehouse, ordering equipment for a fulfillment center, classifying product defects in images, inspecting products in images, selecting inputs for a digital twin, selecting outputs from a digital twin, selecting visual elements for presentation in a digital twin, diagnosing causes of delays in a supply chain, diagnosing causes of shortages in a supply chain, diagnosing causes of congestion in a supply chain, diagnosing causes of cost overruns in a supply chain, diagnosing causes of product defects in a supply chain, predicting maintenance requirements in supply chain infrastructure, and others.

[0273] Accordingly, provided herein are methods, systems, components, and other elements for an information technology system that may include a cloud-based management platform having a microservices architecture, a set of interfaces coordinated for monitoring and managing a set of value chain network entities, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities; and a set of robotic process automation systems for automating a set of processes in a value chain network, the robotic process automation systems streaming a training set of data including a set of users; and interactions with a set of interfaces and other elements of a set of software systems used to monitor and manage value chain network entities.

[0274] In embodiments, one of the processes automated by the robotic process automation described in any of the embodiments disclosed herein may include the following: In embodiments, the RPA includes selecting a quantity of a product for an order. In embodiments, one of the processes automated by the robotic process automation includes selecting a carrier for shipping. In embodiments, one of the processes automated by the robotic process automation includes selecting a vendor of parts. In embodiments, one of the processes automated by the robotic process automation includes selecting a vendor for a finished product order. In embodiments, one of the processes automated by the robotic process automation includes selecting a product variant for marketing. In embodiments, one of the processes automated by the robotic process automation includes selecting a merchandise assortment for shelves. In embodiments, one of the processes automated by the robotic process automation includes determining pricing for finished products. In embodiments, one of the processes automated by the robotic process automation includes configuring a service offering related to a product. In embodiments, one of the processes automated by the robotic process automation includes configuring a product bundle. In embodiments, one of the processes automated by the robotic process automation includes configuring a product kit. In embodiments, one of the processes automated by the robotic process automation includes configuring a product package. In embodiments, one of the processes automated by the robotic process automation includes configuring product displays. In embodiments, one of the processes automated by the robotic process automation includes configuring product images. In embodiments, one of the processes automated by the robotic process automation includes configuring product descriptions. In embodiments, one of the processes automated by the robotic process automation includes configuring website navigation paths related to products. In embodiments, one of the processes automated by the robotic process automation includes determining product inventory levels.In embodiments, one of the processes automated by the robotic process automation includes selecting a logistics type. In embodiments, one of the processes automated by the robotic process automation includes configuring a schedule for product delivery. In embodiments, one of the processes automated by the robotic process automation includes configuring a logistics schedule. In embodiments, one of the processes automated by the robotic process automation includes configuring an input set for machine learning. In embodiments, one of the processes automated by the robotic process automation includes creating product documentation. In embodiments, one of the processes automated by the robotic process automation includes creating required disclosures for a product. In embodiments, one of the processes automated by the robotic process automation includes configuring a product to a set of local requirements. In embodiments, one of the processes automated by the robotic process automation includes configuring a set of products for compatibility. In embodiments, one of the processes automated by the robotic process automation includes configuring a request for proposal.

[0275] In embodiments, one of the processes automated by the robotic process automation includes ordering equipment for a warehouse. In embodiments, one of the processes automated by the robotic process automation includes ordering equipment for a fulfillment center. In embodiments, one of the processes automated by the robotic process automation includes classifying product defects in images. In embodiments, one of the processes automated by the robotic process automation includes inspecting products in images.

[0276] In embodiments, one of the processes automated by the robotic process automation includes inspecting product quality data from a set of sensors. In embodiments, one of the processes automated by the robotic process automation includes inspecting data from a set of on-board diagnostics on a product. In embodiments, one of the processes automated by the robotic process automation includes inspecting diagnostic data from an Internet of Things system. In embodiments, one of the processes automated by the robotic process automation includes reviewing sensor data from environmental sensors in a set of supply chain environments.

[0277] In embodiments, one of the processes automated by the robotic process automation includes selecting inputs for a digital twin. In embodiments, one of the processes automated by the robotic process automation includes selecting outputs from the digital twin. In embodiments, one of the processes automated by the robotic process automation includes selecting visual elements for presentation in the digital twin. In embodiments, one of the processes automated by the robotic process automation includes diagnosing causes of delays in a supply chain. In embodiments, one of the processes automated by the robotic process automation includes diagnosing causes of scarcity in a supply chain. In embodiments, one of the processes automated by the robotic process automation includes diagnosing causes of congestion in a supply chain.

[0278] In embodiments, one of the processes automated by robotic process automation includes diagnosing causes of cost overruns in a supply chain. In embodiments, one of the processes automated by robotic process automation includes diagnosing causes of product defects in a supply chain. In embodiments, one of the processes automated by robotic process automation includes predicting maintenance requirements in a supply chain infrastructure.

[0279] In embodiments, the set of demand management applications, supply chain applications, intelligent product applications, and enterprise resource management applications may include, for example, those related to supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contracts, infrastructure management, facilities management, analytics, finance, trading, tax, regulation, identity management, commerce, e-commerce, payment, 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 Profiling, Business Entity Profiling, Enterprise Profiling, Workforce Profiling, Parts Supply Policy Management, Product Design, Product Configuration, Product Updates, Product Maintenance, Product Support, Product Testing, Warehouse Management, Distribution, Fulfillment, Kit Configuration, Kit Deployment, Kit Support, Kit Updates, Kit Maintenance, Kit Modifications, Kit Management, Shipping Fleet Management, Vehicle Fleet Management, Workforce Management, Maritime Fleet Management, Navigation, Routing, Shipment Management, Opportunity Matching, Search, Advertising, Entity Discovery, Entity Search, Distribution, Delivery, Enterprise Resource Planning, and many more.

[0280] Opportunity Miners for Automatic Improvement of Adaptive Intelligence 26 , a set of opportunity miners 1460 may be provided as part of the adaptive intelligence layer 614, which may be configured to seek and recommend opportunities to improve one or more of the elements of the platform 604, such as through artificial intelligence 1160, automation (including robotic process automation 1442), or additions to one or more of the systems, subsystems, components, applications of the VCNP 102, or those with which the VCNP 102 interacts. In an embodiment, the opportunity miners 1460 may be configured or used by a developer of an AI or RPA solution to find opportunities for better solutions and to optimize existing solutions within the value chain network 668. In an embodiment, opportunity miner 1460 may include a set of systems that collect information within VCNP 102, in, about, and about a set of value chain network entities 652 and environments, the collected information potentially helping to identify and prioritize opportunities for increased automation and / or intelligence for the value chain network 668, for the applications 630, for the value chain network entities 652, or for the VCNP 102 itself. For example, opportunity miner 1460 may include a system that observes clusters of value chain network workers by time, type, and location, such as using cameras, wearables, or other sensors, to identify labor-intensive areas and processes in the set of environments of the value chain network 668. These may be presented as ranked or prioritized lists, visualizations to show locations of high labor activity (such as heat maps showing dwell times of customers, workers, or other individuals on a map of the environment, or heat maps showing paths traveled by customers or workers within the environment), and the like. In embodiments, analysis 838 may be used to identify which environments or activities would most benefit from automation for the purposes of improved delivery times, congestion relief, and other performance improvements.

[0281] In embodiments, opportunity mining may include a facility for the recruitment of appropriate training datasets that can be used to facilitate process automation. For example, certain inputs, if available, provide exceptional value to automation, such as video datasets capturing highly experienced and / or highly specialized workers performing complex tasks. The opportunity miner 1460 may search for such video datasets as described herein; however, if unsuccessful (or to supplement available data), the platform may include a system by which a user, such as a developer, can specify a desired type of data, such as software interaction data (e.g., of experts working with a program to perform a specific task), video data (e.g., video showing a set of experts performing a particular type of delivery process, packing process, picking process, container movement process, etc.), and / or physical process observation data (e.g., video, sensor data, etc.). As a result, interactions captured in response to the specification may be captured as a dataset in the data storage layer 624, such as for consumption by various applications 630, adaptive intelligence system 614, and other processes and systems. In embodiments, the library may include videos specifically developed as instructional videos, such as to facilitate developing automation maps that can follow the instructions in the video, such as providing a sequence of steps according to a procedure or protocol, breaking down a procedure or protocol into substeps that are candidates for automation, etc. In embodiments, such videos can be processed by natural language processing to automatically develop labeled sequences of instructions that developers can use to facilitate maps, graphs, or other models of processes that assist in the development of process automation. In embodiments, a designated set of training datasets can be configured to serve as inputs for training.In such cases, training data can be time-synchronized with other data in the platform 604, such as outputs and results from applications 630 and value chain entities 652, so that a given video of a process can be associated with those outputs and results, thereby enabling learning feedback that is sensitive to the results that occurred during a given process captured (e.g., on video or through observing software interactions or physical process interactions). For example, this could relate to an instructional video, such as a video of someone building or rebuilding (e.g., rebuilding a bearing set). This instructional video might include the individual steps for the rebuild, enabling rendition of the training to provide instructions, such as analyzing the video into stages that mimic the expert's rendition in the video. For example, tagging the video to include references to each stage and status (e.g., Stage 1 Complete, Stage 2, etc.) might also be included.

[0282] In embodiments, opportunity miner 1460 may include methods, systems, processes, components, services, and other elements for mining opportunities for the definition, formation, configuration, and execution of smart contracts. Data collected within platform 604, such as data processed by data processing layer 608, data stored by data storage layer 624, data collected by monitoring layer 614 and collection system 640, data collected about or from entities 652, or data obtained from external sources, may be used to recognize profitable opportunities for the application or configuration of smart contracts. For example, pricing information about entities 652 handled by pricing application 842 or otherwise collected may be used to recognize situations in which the same item or items are unequally priced (in spot markets, futures markets, etc.), and opportunity miner 1460 may provide alerts indicating opportunities for smart contract formation, such as an agreement to buy below a predetermined threshold in one environment and sell above a predetermined threshold in another environment, or vice versa.

[0283] In some examples, as shown in FIG. 26, the adaptive intelligent system 614 can include a value translator 1470. The value translator 1470 may be associated with the demand side of a transaction. Specifically, for example, the value translator 1470 may understand the negative currencies of two marketplaces and be able to translate the value currency into other currencies (e.g., not just fiat currencies, which already have explicit translation capabilities). In some examples, the value translator 1470 may be associated with points in a points-based system (e.g., in a cost-based routing system). In an exemplary embodiment, the value translator 1470 may be offered loyalty points that may be convertible to airline seats and / or refund policies for staying in a hotel room. In some examples, different types of entities may be connected as having native pricing or cost functions that do not always use the same currency or any currency. In another example, the value translator 1470 may be used in conjunction with network prioritization or cost-based routing, where the points system in these cost-based routing systems arises from non-monetary priorities in the network.

[0284] Extensive management platform 28 , additional details of an embodiment of the platform 604 are provided, particularly related to the overall architecture of the platform 604. These may include employing a microservices architecture for the cloud-based management platform 604, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 at various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808. The platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases, and applications) to enable an enterprise to manage a set of value chain network entities 652, such as from the point of origin of a product 1510, which may be an intelligent product, to the customer's point of use.

[0285] Accordingly, provided herein are methods, systems, components, and other elements for information technology systems that may include a cloud-based management platform with a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities, and a set of applications to enable an enterprise to manage a collection of value chain network entities from origin to customer point of use.

[0286] A cloud-based management platform having a microservices architecture, the platform having: a set of interfaces for accessing and configuring the platform's functions; a set of network connectivity facilities for enabling a set of value chain network entities to connect to the platform; a set of adaptive intelligence facilities for automating the set of platform's functions; a set of data storage facilities for storing data collected and processed by the platform; and a set of monitoring facilities for monitoring the value chain network entities; wherein the platform hosts a set of applications for enabling an enterprise to manage the set of value chain network entities from the enterprise's product point of origin to the customer's point of use.

[0287] Broad Management Platform - Details 29 , additional details of an embodiment of the platform 604 are provided, particularly related to the overall architecture of the platform 604. These may include employing a microservices architecture for the cloud-based management platform 604, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808. The platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases, and applications) to enable an enterprise to manage a set of value chain network entities 652, such as from the point of origin of a product 1510, which may be an intelligent product, to the customer's point of use.

[0288] In an embodiment, the set of interfaces 702 may include a demand management interface 1402 and a supply chain management interface 1404 .

[0289] In an embodiment, the set of network connectivity facilities 642 for enabling the set of value chain network entities 652 to connect to the platform 604 may include a 5G network system 1410, such as one deployed in supply chain infrastructure facilities operated by an enterprise.

[0290] In an embodiment, the set of network connectivity facilities 642 for enabling the set of value chain network entities 652 to connect to the platform 604 may include Internet of Things systems 1172, such as those deployed within supply chain infrastructure facilities operated by the enterprise, within the value chain network entities 652, on or near the value chain network entities 652, within a network system, and / or in a cloud computing environment (such as when a data collection system 640 is configured to collect and organize IoT data).

[0291] In an embodiment, the set of network connectivity facilities 642 for enabling the set of value chain network entities 652 to connect to the VCNP 102 may include cognitive networking systems 1420 deployed in supply chain infrastructure facilities operated by the enterprise.

[0292] In an embodiment, the set of network connection facilities 642 for enabling the set of value chain network entities 652 to connect to the VCNP 102 may include a peer-to-peer network system 1430, such as one deployed in a supply chain infrastructure facility operated by an enterprise.

[0293] In an embodiment, the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating the set of capabilities of the platform 604 may include edge intelligence systems 1420, such as those deployed in supply chain infrastructure facilities operated by an enterprise.

[0294] In an embodiment, the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating the set of capabilities of the platform 604 may include a robotic process automation system 1442 .

[0295] In embodiments, the set of adaptive intelligent facilities or adaptive intelligent systems 614 for automating the set of capabilities of the platform 604 may include or integrate with self-configuring data collection systems 1440, such as those deployed at supply chain infrastructure facilities, networks, and / or cloud computing environments operated by the enterprise. This may include elements of data collection systems 640 in the data processing layer 608 that interact with or integrate with elements of the adaptive intelligent systems 614.

[0296] In an embodiment, a set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating a set of capabilities of the platform 604 may include a digital twin system 1700, such as one that represents attributes of a set of value chain network entities, such as those controlled by an enterprise.

[0297] In embodiments, the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating the set of capabilities of the platform 604 may include a smart contract system 848, such as for automating a series of interactions or transactions between a set of value chain network entities 652 based on status data, event data, or other data processed by the data processing layer 608.

[0298] In an embodiment, a set of data storage facilities or systems 624 for storing data collected and processed by platform 604 uses a distributed data architecture 1122 .

[0299] In an embodiment, a set of data storage facilities for storing data collected and handled by the platform uses blockchain 844.

[0300] In an embodiment, the set of data storage facilities for storing data collected and processed by the platform uses a distributed ledger 1452.

[0301] In an embodiment, the set of data storage facilities for storing data collected and handled by the platform uses a graph database 1124 that represents a set of hierarchical relationships of value chain network entities.

[0302] In an embodiment, the set of monitoring equipment 614 for monitoring the value chain network entities 652 includes an Internet of Things monitoring system 1172, such as for collecting data from IoT systems and devices deployed throughout the value chain network.

[0303] In an embodiment, the set of monitoring equipment 614 for monitoring the value chain network entities 652 includes a set of sensor systems 1462, such as those deployed within the value chain environment or within, at or near the value chain network entities 652, such as within or on the products 1510.

[0304] In an embodiment, the set of applications 614 includes a set of applications that may include various types from the set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510, and enterprise resource management applications 1520, for example.

[0305] In an embodiment, the set of applications includes an asset management application 1530.

[0306] In embodiments, value chain network entities 652 referred to throughout this disclosure may include, for example, but are not limited to, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, seaports, airports, airways, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand drivers, supply drivers, distribution systems, floating assets, origins, destinations, storage locations, 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, material handling facilities, drones / robots / AVs, waterways, port infrastructure facilities, or others.

[0307] In an embodiment, the platform 604 manages a set of demand factors 1540, a set of supply factors 1550, and a set of value chain infrastructure facilities 1560.

[0308] In embodiments, supply factors 1550 referred to throughout this disclosure may include, for example, but are not limited to, those related to component availability, material availability, component location, material location, component pricing, material pricing, taxes, duties, levies, tariffs, import restrictions, export restrictions, border controls, trade restrictions, customs, navigation, traffic, congestion, vehicle capacity, vessel capacity, container capacity, package capacity, vehicle availability, vessel availability, container availability, cargo 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, labor availability, labor capacity, labor location, commodity pricing, fuel pricing, energy pricing, route availability, route distance, route cost, route safety, and many others.

[0309] In embodiments, demand factors 1540 as referred to throughout this disclosure may include, for example, but are not limited to, 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 consumables, taste, preference, inferred need, inferred desire, group demand, individual demand, family demand, business demand, workflow need, process need, procedure need, treatment need, improvement need, diagnostic need, system fit, product fit, style fit, brand fit, demographic, psychographic, geolocation, etc. The information may include information related to: location, indoor location, destination, route, home location, visited locations, work location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchase history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interests, inferred interests, and many others.

[0310] In embodiments, supply chain infrastructure facilities 1560 referred to throughout this disclosure may include, for example, but are not limited to, ships, container ships, boats, barges, seaports, cranes, containers, container handling, shipyards, marine docks, warehouses, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drones, robots, autonomous vehicles, aircraft, automobiles, trucks, trains, lifts, forklifts, material handling equipment, conveyors, loading docks, waterways, bridges, tunnels, airports, rail yards, train stations, weigh stations, inspections, roads, railways, highways, customs, border control, and other facilities.

[0311] In an embodiment, the set of applications 614 referred to throughout this disclosure may include, for example, but not limited to, supply chain, asset management, risk management, inventory management, demand management, demand forecasting, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contracts, infrastructure management, facilities management, analytics, finance, trading, tax, regulation, identity management, commerce, e-commerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote operations, automation, self-configuring, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, Applications may include demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, parts supply policy management, product design, product configuration, product updates, product maintenance, product support, product testing, warehousing management, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updates, warehousing management, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updates, kit maintenance, kit modifications, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipment management, opportunity matching, search, advertising, entity discovery, entity search, distribution, delivery, enterprise resource planning, and other applications.

[0312] Control Tower 30 , one embodiment of a platform 604 is provided. The platform 604 may employ a microservices architecture having various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of the various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808. The platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases, and applications) to enable an enterprise to manage a set of value chain network entities 652, such as from the point of origin of a product 1510, which may be an intelligent product, to the customer's point of use.

[0313] In embodiments, the platform 604 may include a user interface 1570 that provides a unified view for a set of demand management information and supply chain information for a category of goods, such as one that displays status information, event information, activity information, analysis, reports, or other elements of the set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510, and enterprise resource management applications 1520 that monitor and / or manage the value chain network and set of value chain network entities 652. Thus, the unified view interface 1570, in embodiments, may provide a control tower for an enterprise across a range of assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 involved in moving a product 1510 from its point of origin, through distribution and retail channels, to the environment in which it is used by a customer. These may include views of demand factors 1540 and supply factors 1550, enabling a user to develop insights into orchestrating the connections between elements and controlling one or both of them with coordinated intelligence. The collection of the unified set of views may be adapted over time, such as by learning about the results 1040 or other operations of the adaptive intelligent system 614, to determine which views of the interface 1570 provide the most impactful insights, control functions, etc.

[0314] In an embodiment, the user interface includes a voice-activated assistant 1580.

[0315] In an embodiment, the user interface includes a set of digital twins 1700 for presenting a visual representation of a set of attributes of a set of value chain network entities 652.

[0316] In embodiments, the user interface 1570 may include functionality for configuring the adaptive intelligent system 614 or adaptive intelligent facility, such as allowing a user to select attributes, parameters, data sources, inputs to learning, feedback to learning, views, formats, arrangements, or other elements.

[0317] Value Chain Management Platform - Control Tower UI for Demand Management and Supply Chain Accordingly, provided herein are methods, systems, components, and other elements for an information technology system: a cloud-based management platform having a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities tailored for monitoring and managing a set of value chain network entities; a set of applications for enabling an enterprise to manage the set of value chain network entities from point of origin to point of customer use; and a user interface that provides a unified set of views for a set of demand management information and supply chain information for a category of goods.

[0318] Integrated Database 31 , one embodiment of a platform 604 is provided. As with other embodiments, the platform 604 may employ a microservices architecture having various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect a set of interfaces 702 of the various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808. The platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases, and applications) to enable an enterprise to manage a set of value chain network entities 652, such as from the point of origin of a product 1510, which may be an intelligent product, to the customer's point of use.

[0319] In embodiments, the platform 604 may include a unified database 1590 that supports a set of multiple types of applications, such as those among the set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510, and enterprise resource management applications 1520, that monitor and / or manage a value chain network and a set of value chain network entities 652. In this manner, the unified database 1590, in embodiments, may provide unification of data storage, access, and handling for an enterprise across a range of assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 involved in moving a product 1510 from its point of origin, through distribution and retail channels, to its customer usage environment. This centralization may provide many benefits, including reduced data entry needs, consistency between applications 630, reduced latency (and improved real-time reporting), reduced need for data transformation and integration, and so forth. These can include data related to demand factors 1540 and supply factors 1550 so that the application 630 can benefit from information collected, processed, or generated by other applications 630 on the platform 604, and the user can develop insights into the connections between the factors and control one or both of them with coordinated intelligence. The unified database 1590 population can adapt over time by learning about the results 1040 or other operations of the adaptive intelligent system 614, etc., to determine which elements of the database 1590 should be made available to which applications, which data structures will provide the most benefit, which data should be saved or cached for immediate retrieval, which data to discard or preserve, which data is most beneficial to support the adaptive intelligent system 614, and for other uses.

[0320] Accordingly, provided herein are methods, systems, components, and other elements for information technology systems that may include: a cloud-based management platform having a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities tailored for monitoring and managing a set of value chain network entities; a unified database supporting a set of applications to enable an enterprise to manage the set of value chain network entities from point of origin to point of customer use, and at least two types of sets of applications from a set of demand management applications for a category of goods, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications.

[0321] In an embodiment, the unified database supporting a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods is a distributed database.

[0322] In embodiments, a unified database supporting a set of demand management applications for a category of goods, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications uses a graph database architecture. In embodiments, the set of demand management applications includes a demand forecasting application. In embodiments, the set of demand management applications includes a demand aggregation application. In embodiments, the set of demand management applications includes a demand activation application.

[0323] In embodiments, the set of supply chain management applications includes a vendor search application. In embodiments, the set of supply chain management applications includes a routing application. In embodiments, the set of supply chain management applications includes a logistics scheduling application.

[0324] Integrated Data Collection System 32 , one embodiment of a platform 604 is provided. As with other embodiments, the platform 604 may employ a microservices architecture having various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of the various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 1160, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808. The platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases, and applications) to enable an enterprise to manage a set of value chain network entities 652, such as from the point of origin of a product 1510, which may be an intelligent product, to the customer's point of use.

[0325] In embodiments, the platform 604 may include a set of unified data collection and management systems 640 for a set of monitoring facilities or systems 808 that support a set of various types of applications 614, including a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510, and enterprise resource management applications 1520, that monitor and / or manage a value chain network and a set of value chain network entities 652. The unified data collection and management systems 640 may thus, in embodiments, provide unified data monitoring, search, discovery, collection, access, and handling for an enterprise or other user across a range of assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 involved in moving a product 1510 from its point of origin, through distribution and retail channels, to the environment in which it is used by a customer. This centralization may provide many benefits, including reduced data entry requirements, consistency between applications 630, reduced latency (and improved real-time reporting), reduced need for data transformation and integration, and so forth. These may include the collection of data related to demand factors 1540 and supply factors 1550 so that the application 630 can benefit from information collected, processed, or generated by other applications 630 on the platform 604, allowing users to develop insights into the connections between the factors and control one or both of them with coordinated intelligence.The unified data collection and management system 640 may adapt over time, such as by learning about the results 1040 or other operations of the adaptive intelligent system 614, to determine which elements of the data collection and management system 640 should be made available to which applications 630, which data types or sources will provide the most benefit, which data should be saved or cached for immediate retrieval, which data can be discarded for storage, which data is most beneficial for supporting the adaptive intelligent system 614, and for other uses. In an exemplary embodiment, the unified data collection and management system 640 may use a unified data schema that associates data collection and management for various applications. This may be a tightly coupled single point of truth database or a set of distributed data systems that adhere to a schema with enough commonality to allow a wide variety of applications to consume the same data received. For example, sensor data may be drawn from a smart product, which may be consumed by a logistics application, a financial application, a demand forecasting application, or a genetic programming artificial intelligence (AI) application to modify the product. All of these applications may consume data from the data framework. In one example, this may occur from a distributed ledger or blockchain that may contain transaction data for buying and selling, or an indication of whether an event occurred. In some exemplary embodiments, as data moves through the supply chain, this data flow may occur through distributed databases, relational databases, any type of graph database, etc. that may be part of the unified data collection and management system 640. In other examples, the unified data collection and management system 640 may utilize dedicated memory on the asset, memory in the tag, or memory that may be part of the memory structure of the device itself, that may come from a robust pipeline tied to value chain network entities.In other examples, the unified data collection and management system 640 can use classic data integration functions, including adapting protocols so that a final unified system or schema can be arrived at.

[0326] Accordingly, provided herein are methods, systems, components, and other elements for information technology systems that may include: a cloud-based management platform having a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities tailored for monitoring and managing a set of value chain network entities, and a set of applications for enabling an enterprise to manage the set of value chain network entities from the point of origin to the customer's point of use; and a unified set of data collection systems supporting at least two types of sets of applications from a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a product category.

[0327] In embodiments, the unified set of data collection systems includes a set of crowdsourced data collection systems. In embodiments, the unified set of data collection systems includes a set of Internet of Things data collection systems. In embodiments, the unified set of data collection systems includes a set of self-configuring sensor systems. In embodiments, the unified set of data collection systems includes a set of data collection systems that interact with network-connected products.

[0328] In embodiments, the unified set of data collection systems includes a set of mobile data collectors deployed in a set of value chain network environments operated by an enterprise. In embodiments, the unified set of data collection systems includes a set of edge intelligence systems deployed in a set of value chain network environments operated by an enterprise. In embodiments, the unified set of data collection systems includes a set of crowdsourcing data collection systems. In embodiments, the unified set of data collection systems includes a set of Internet of Things data collection systems. In embodiments, the set of unified data collection systems includes a set of self-configuring sensor systems. In embodiments, the set of unified data collection systems includes a set of data collection systems that interact with network-connected products. In embodiments, the unified set of data collection systems includes a set of mobile data collectors deployed in a set of value chain network environments operated by an enterprise. In embodiments, the unified set of data collection systems includes a set of edge intelligence systems deployed in a set of value chain network environments operated by an enterprise.

[0329] Integrated IoT Monitoring System

[0330] 33 , one embodiment of a platform 604 is provided. As with other embodiments, the platform 604 may employ a microservices architecture having various data processing layers 608, a set of network connectivity facilities 642 (which may include or connect a set of interfaces 702 of the various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 1160, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808. The platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases, and applications) to enable an enterprise to manage a set of value chain network entities 652, such as from the point of origin of a product 1510, which may be an intelligent product, to the customer's point of use.

[0331] In an embodiment, the platform 604 may include a set of unified Internet of Things systems 1172 that provide coordinated monitoring of various value chain entities 652 in service of a set of multiple applications 630 of various types, such as a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510, and enterprise resource management applications 1520, that monitor and / or manage the value chain network and the set of value chain network entities 652.

[0332] Thus, a unified set of Internet of Things systems 1172 can, in embodiments, provide unified monitoring of and communication with a wide range of facilities, devices, systems, environments, and assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652, involved in the movement of a product 1510 from its point of origin, through distribution and retail channels, to the environment where it is used by a customer. This unification can provide many benefits, including reduced data entry needs, consistency between applications 630, reduced latency, real-time reporting and awareness, reduced need for data transformation and integration, etc. These can include Internet of Things systems 1172 used in connection with demand factors 1540 and supply factors 1550, allowing the applications 630 to benefit from information collected, processed, or generated by the unified set of Internet of Things systems 1172 for other applications 630 of the platform 604, enabling users to develop insights into the connections between the factors and control one or both of them with coordinated intelligence. The unified set of Internet of Things systems 1172 may adapt over time, such as by learning about the results 1040 or other operations of the adaptive intelligent system 614, such as determining which elements of the unified set of Internet of Things systems 1172 should be made available to which applications 630, which IoT systems 1172 will most benefit, which data should be stored or cached for immediate retrieval, which data can be discarded for storage, which data is most beneficial to support the adaptive intelligent system 614, and for other uses. In some examples, the unified set of Internet of Things (IoT) systems 1172 may be IoT devices that may be installed in a variety of environments.One purpose of a unified set of Internet of Things systems 1172 may be city- or town-wide coordination, including city-wide deployments, where a set of IoT devices may collectively be connected by a wide area network protocol (e.g., a longer range protocol). In another example, a unified set of Internet of Things systems 1172 may include connecting a mesh of devices across several different power distribution facilities. IoT devices may identify a collection of warehouses, and the warehouses may: communicate with each other using IoT devices; and the IoT devices may be configured to process data without using the cloud.

[0333] Accordingly, provided herein are methods, systems, components, and other elements for information technology systems that may include: a cloud-based management platform having a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities tailored for monitoring and managing a set of value chain network entities, and a set of applications integrated with the platform to enable enterprise users of the platform to manage the set of value chain network entities from origin to point of customer use; and a set of unified Internet of Things systems that provide coordinated monitoring of at least two types of sets of applications from a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a commodity category.

[0334] In an embodiment, the unified Internet of Things system includes a set of smart home Internet of Things devices to enable monitoring of a set of demand factors, and a set of Internet of Things devices deployed in proximity to a set of supply chain infrastructure facilities to enable monitoring of a set of supply factors.

[0335] In an embodiment, the unified set of Internet of Things systems includes a set of Internet of Things devices in the workplace to enable monitoring of a set of demand factors for a set of business customers, and a set of Internet of Things devices deployed in proximity to a set of supply chain infrastructure facilities to enable monitoring of a set of supply factors.

[0336] In an embodiment, a collection of unified Internet of Things systems includes a collection of Internet of Things devices monitoring a collection of consumer goods stores to enable monitoring of a collection of demand factors for a collection of consumers, and a collection of Internet of Things devices located in proximity to a collection of supply chain infrastructure facilities to enable monitoring of a collection of supply factors.

[0337] In embodiments, Internet of Things systems referred to throughout this disclosure include, for example, but are not limited to, camera systems, lighting systems, motion detection systems, metering systems, inspection systems, machine vision systems, environmental sensor systems, on-board sensor systems, on-board 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, access control systems, through-wave sensing systems, radiography systems, LIDAR-based monitoring systems, pressure monitoring systems, vibration monitoring systems, temperature monitoring systems, heat flow monitoring systems, biological measurement systems, chemical measurement systems, ultrasound monitoring systems, radiography systems, LIDAR-based monitoring systems, access control systems, through-wave sensing systems, SONAR-based monitoring systems, radar-based monitoring systems, computed tomography systems, magnetic resonance imaging systems, network monitoring systems, and many others.

[0338] Machine Vision Feeding Digital Twin

[0339] 34 , one embodiment of a platform 604 is provided. Similar to other embodiments, the platform 604 may employ a microservices architecture and include various data processing layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 1160, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808. The platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases, and applications) to enable an enterprise to manage a set of value chain network entities 652, such as from the point of origin of a product 1510, which may be an intelligent product, to the customer's point of use.

[0340] In an embodiment, the platform 604 can include a machine vision system 1600 and a digital twin system 1700, where the machine vision system 1600 feeds data to the digital twin system 1700 (which can be enabled by a set of adaptive intelligent systems 614, including artificial intelligence 1160, and can be used as an interface or component of interface 702, such as one that allows an operator to monitor the twins 1700 of various value chain network entities 652). The machine vision system 1600 and digital twin system 1700 can operate in concert for a set of multiple applications 630 of various types, such as a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510, and enterprise resource management applications 1520 that monitor and / or manage the value chain network and the set of value chain network entities 652.

[0341] Thus, in embodiments, machine vision system 1600 and digital twin system 1700 can provide image-based monitoring (with automated processing of image data) of a wide range of facilities, devices, systems, environments, and assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 involved in moving product 1510 from its point of origin, through distribution and retail channels, and into customer usage environments, as well as representation of the images and data extracted from the images in digital twin 1700. This integration can provide many benefits, such as improved monitoring, improved visualization and insights, and improved visibility. These can include machine vision system 1600 and digital twin system 1700 used in conjunction with demand factors 1540 and supply factors 1550, allowing applications 630 to benefit from information collected, processed, or generated by machine vision system 1600 and digital twin system 1700 for other applications 630 of platform 604, enabling users to develop insights into the connections between factors and control one or both of them with coordinated intelligence. The machine vision system 1600 and / or digital twin system 1700 may adapt over time, such as by learning about the results 1040 or other operations of the adaptive intelligent system 614, to determine which elements collected and / or processed by the machine vision system 1600 and / or digital twin system 1700 should be made available to which applications 630, which elements and / or content will provide the most benefit, which data should be stored or cached for immediate retrieval, which data can be discarded for storage, which data is most beneficial to support the adaptive intelligent system 614, and for other uses.

[0342] Therefore, provided herein is a cloud-based management platform having a microservices architecture, a set of interfaces, network connection facilities, intelligence facilities, data storage facilities, and monitoring facilities tailored for monitoring and managing an adaptive set of value chain network entities; an application set for enabling an enterprise to manage a set of value chain network entities from the point of origin to the customer's point of use; and at least two types of application sets from a supply chain application set, a demand management application set, an intelligent product application set, and an enterprise resource management application set, the application set having a machine vision system and a digital twin system, and the machine vision system supplies data to the digital twin system.

[0343] In an embodiment, the set of supply chain applications and demand management applications are any of those described throughout this disclosure or in the documents incorporated by reference herein.

[0344] In an embodiment, the set of supply chain applications and demand management applications may include, for example, without limitation, inventory management, demand forecasting, demand aggregation, pricing, blockchain, smart contracts, positioning, placement, promotion, analytics, finance, trading, arbitrage, customer identity management, store planning, shelf planning, customer route planning, customer route analysis, commerce, e-commerce, payment, ...

Claims

1. 1. A method for transmitting a predictive model of a data stream from a first device to a second device, comprising: receiving, by a first device, a plurality of data values ​​of a data stream, the received plurality of data values ​​including sensor data collected from one or more sensor devices; generating, by the first device, a predictive model for predicting future data values ​​of the data stream based on the received data values, wherein generating the predictive model includes determining a plurality of model parameters; transmitting, by the first device, the plurality of model parameters to the second device; receiving, by the second device, the plurality of model parameters; parameterizing, by the second device, a predictive model using the plurality of model parameters; and and predicting, by the second device, the future data values ​​of the data stream using the parameterized predictive model.

2. 2. The method of claim 1, wherein the plurality of model parameters comprises a vector.

3. 3. The method of claim 2, wherein the vectors are motion vectors associated with a robot.

4. The method of claim 3 , wherein the future data values ​​in the data stream include one or more predicted future positions of the robot.

5. The forecasting model forecasts inventory levels of items, the method further comprising: Detecting a future supply shortage of an item based on said future data values; and 2. The method of claim 1, further comprising taking action to avoid running out of the item.

6. 2. The method of claim 1, wherein the predictive model is a behavioral analysis model, and the future data values ​​indicate a predicted behavior of an entity.

7. 2. The method of claim 1, wherein the predictive model is an augmented model, and the future data values ​​correspond to a non-operating sensor.

8. 2. The method of claim 1, wherein the predictive model is a classification model and the future data values ​​indicate a predicted future state of a system that includes the one or more sensor devices.

9. 2. The method of claim 1, wherein the one or more sensor devices are RFID sensors associated with a cargo item, and the future data values ​​indicate a future location of the cargo item.

10. 2. The method of claim 1, wherein the one or more sensor devices are security cameras, and the data stream includes motion vectors extracted from video data captured by the security cameras.

11. 2. The method of claim 1, wherein the one or more sensor devices are vibration sensors that measure vibrations generated by a machine, and the future data values ​​indicate a potential need for maintenance on the machine.

12. receiving, by the first device, additional data values ​​of the data stream; improving, by the first device, the predictive model using the additional data values, the improving the predictive model comprising adjusting the plurality of model parameters; and 10. The method of claim 1, further comprising transmitting the adjusted model parameters to the second device.

13. receiving, by the second device, the adjusted model parameters; reparameterizing the predictive model using the adjusted model parameters; and 13. The method of claim 12, further comprising: generating additional future data values ​​using the reparameterized predictive model.

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