Control tower and enterprise management platform with trainable expert agents for value chain networks

Abstract

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

What is claimed is: 
     
         1 . A method for training an expert agent, comprising:
 receiving digital twin data from a set of data sources, the digital twin data including:
 sensor data that is received from a set of sensors that monitor a set of monitored physical entities associated with the enterprise, the sensor data transported by a set of network entities; and 
 enterprise data streams generated by a set of enterprise assets, wherein the enterprise assets include at least one of physical entities associated with the enterprise and digital entities associated with the enterprise; 
   structuring the digital twin data into a set of digital twin data structures that are configured to serve a plurality of different role-based digital twins;   receiving a request for a role-based digital twin from a client application, wherein the role-based digital twin is configured with respect to a defined role within the enterprise;   determining a subset of the structured digital twin data to corresponds to a set of states that are depicted in the role-based digital twin;   providing the subset of the structured digital twin data to the client application;   receiving expert agent training data sets from the client application, each expert agent training data set indicating a respective action taken by a user using the client application and one or more features that correspond to the respective action; and   training an expert agent on behalf of the user based on the expert agent training data sets, wherein the expert agent is configured to determine actions to be performed on behalf of the user, wherein the determined actions are either recommended to the user or automatically performed on behalf of the user.   
     
     
         2 . The method of  claim 1 , wherein the defined role is selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role. 
     
     
         3 . The method of  claim 1 , wherein the defined role is selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role. 
     
     
         4 . The method of  claim 1 , wherein the defined role is selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role. 
     
     
         5 . The method of  claim 1 , wherein the expert agent training data includes interactions training data that indicates a set of interactions with a set of experts by the user during performance of the role. 
     
     
         6 . The method  claim 5 , wherein the set of interactions used to train the expert agent includes interactions of the user with the physical entities. 
     
     
         7 . The method of  claim 5 , wherein the set of interactions used to train the expert agent includes interactions of the user with the role-based digital twin. 
     
     
         8 . The method of  claim 5 , wherein the set of interactions used to train the expert agent includes interactions of the user with the sensor data as depicted in the role-based digital twin. 
     
     
         9 . The method of  claim 5 , wherein the set of interactions used to train the artificial intelligence system includes interactions of the experts with the data streams generated by the physical entities. 
     
     
         10 . The method of  claim 5 , wherein the set of interactions used to train the expert agent system includes interactions of the experts with one or more computational entities. 
     
     
         11 . The method of  claim 5 , wherein the set of interactions used to train the expert agent includes interactions of the user with one or more network entities. 
     
     
         12 . The method of  claim 1 , wherein the expert agent is trained to determine an action selected from the group comprising: selection of a tool, selection of a task, selection of a dimension, setting of a parameter, selection of an object, selection of a workflow, triggering of a workflow, ordering of a process, ordering of a workflow, cessation of a workflow, selection of a data set, selection of a design choice, creation of a set of design choices, identification of a failure mode, identification of a fault, identification of an operating mode, identification of a problem, selection of a human resource, selection of a workforce resource, providing an instruction to a human resource, and providing an instruction to a workforce resource. 
     
     
         13 . The method of  claim 1 , wherein the executive is trained on a training set of outcomes resulting from the actions taken by the executive. 
     
     
         14 . The method of  claim 13 , wherein the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome. 
     
     
         15 . The method of  claim 1 , wherein the expert agent is trained to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource, physically securing equipment, physically securing hardware, cyber-securing a system, cyber-securing a device, cyber-securing a process, cyber-securing a network, cyber-securing a computational resource, cyber-securing equipment, cyber-securing hardware, detecting a threat, detecting a fault, tuning a system, tuning a device, tuning a process, tuning a network, tuning a computational resource, tuning equipment, tuning hardware, optimizing a system, optimizing a device, optimizing a process, optimizing a network, optimizing a computational resource, optimizing equipment, optimizing hardware, monitoring a system, monitoring a device, monitoring a process, monitoring a network, monitoring a computational resource, monitoring equipment, monitoring hardware, configuring a system, configuring a device, configuring a process, configuring a network, configuring a computational resource, configuring equipment, and configuring hardware. 
     
     
         16 . The method of  claim 1 , wherein the expert agent is at least one of trained and configured via feedback from at least one expert in the defined role regarding a set of outputs of the expert agent. 
     
     
         17 . The method of  claim 16 , wherein the set of outputs of the expert agent upon which the expert provides feedback includes at least one of a recommendation, a classification, a prediction, a control instruction, an input selection, a protocol selection, a communication, an alert, a target selection for a communication, a data storage selection, a computational selection, a configuration, an event detection, and a forecast. 
     
     
         18 . The method of  claim 17 , wherein the feedback of the at least one expert is solicited to train the expert agent to replicate the expertise of the expert in the role. 
     
     
         19 . The method of  claim 17 , wherein the feedback of the at least one expert is used to modify a set of inputs to the expert agent. 
     
     
         20 . The method of  claim 17 , wherein herein the feedback of the at least one expert is used to identify and characterize at least one error by the expert agent. 
     
     
         21 . The method of  claim 20 , wherein a report on a set of errors is provided to a user of the expert agent to enable reconfiguring of the expert agent based on the feedback from the expert. 
     
     
         22 . The method of  claim 21 , wherein reconfiguring the artificial intelligence system includes at least one of removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and augmenting the set of inputs to the artificial intelligence system. 
     
     
         23 . The method of  claim 1 , wherein the expert agent is trained learn upon a training set of outcomes and to provide at least one of training and guidance to an individual who is responsible for performing the defined role. 
     
     
         24 . The method of  claim 23 , wherein the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome. 
     
     
         25 . The method of  claim 1 , wherein the defined role is selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.