DIGITAL TWIN FOR CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM MANAGING ENTITY REPLICAS AND E-COMMERCE SYSTEMS

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.

Claims

1. An information technology system for leveraging digital twins in a value chain having a plurality of value chain entities, the information technology system comprising: a plurality of sensors positioned at least one of in, on, and near a set of value chain entities of the value chain entities and configured to collect sensor data related to the set of value chain entities, the sensor data being substantially real-time sensor data; and an adaptive intelligence system connected to the plurality of sensors and configured to receive the sensor data from the plurality of sensors, the adaptive intelligence system including: an artificial intelligence system configured to input the sensor data into a machine learning model such that the sensor data is used as training data for the machine learning model, and the machine learning model is configured to transform the sensor data into simulation data; and a digital twin system configured to create a digital replica of the set of value chain entities based on the simulation data, wherein the digital replica of the value chain entities is configured to be used to provide a substantially real-time representation of the value chain entities and provide a simulation of a possible future state of the value chain entities via the simulation data.

2. The information technology system of claim 1, wherein the machine learning model is configured to learn which types of sensor data are relevant to dynamics of each value chain entity of the value chain entities and simulation thereof.

3. The information technology system of claim 1, wherein the machine learning model is configured to make suggestions to a user of the information technology system via an interface regarding potential changes to the plurality of sensors that would improve simulation of the value chain entities via the digital twin system.

4. The information technology system of claim 1, wherein the machine learning model is configured to prioritize collection and transmission of sensor data that are relevant to dynamics of the value chain entities and simulation thereof.

5. A value chain network management platform, comprising: a machine learning system that trains one or more machine-learned models to output one or more e-commerce recommendations to a value chain network customer via an interface using training data that includes product features and outcomes; and an artificial intelligence system that receives a request for e-commerce from an e-commerce system, wherein the artificial intelligence is configured to determine and generate an e-commerce recommendation based on the one or more machine-learned models and the request, and the artificial intelligence is configured to leverage one or more product digital twins and one or more customer digital twins to execute a simulation based on the one or more customer digital twins, the one or more product digital twins, and the e-commerce recommendation.

6. The value chain network management platform of claim 5, wherein the machine learning system integrates with a model interpretability system, and wherein the model interpretability system is configured to implement Testing with Concept Activation Vectors (TCAV) functionality, whereby the model interpretability facilitates learning of human-interpretable concepts by the machine-learned model.

7. The value chain network management platform of claim 5, wherein the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more customer profile digital twins.

8. A value chain network management platform comprising: a machine learning system that trains one or more machine-learned models to output one or more risk management decisions using training data that includes component features and outcomes; and an artificial intelligence system that receives a request for risk management from a risk management system, wherein the artificial intelligence system is configured to determine and generate a risk management decision based on the one or more machine-learned models and the request, and the artificial intelligence system is configured to leverage one or more component digital twins and one or more environment digital twins to execute a simulation based on the one or more component digital twins, the one or more environment digital twins, and the risk management decision.

9. The value chain network management platform of claim 8, wherein the risk management decision relates to a condition of a component.

10. The value chain network management platform of claim 8, wherein the one or more machine-learned models are at least one of trained and retrained using simulation data from one or more simulations involving one or more components.