Training inventory management robots using digital twins, trained machine learning models, and human feedback

A computing system including one or more processors and one or more memories configured to perform operations comprising:

• receiving, by a computing device, information associated with a value chain network, wherein:
• the information includes observations of a human performing a set of tasks within the value chain network, and
• the information is generated by at least one of:
• a set of sensors of a set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities;
• providing, by the computing device, the information to a set of machine learning models, wherein:
• the set of machine learning models includes a modular neural network,
• the modular neural network includes a set of independent neural networks moderated by an intermediary, and
• each neural network of the set of independent neural networks is associated with a respective value chain entity of the set of value chain network entities;
• training, by the computing device, each machine learning model of the set of machine learning models on a training data set to classify at least one of:
• an operating state, a fault condition, an operating flow, or a behavior of the value chain network, wherein the training data set includes:
• the information associated with the value chain network, including the observations of the human performing the set of tasks within the value chain network,
• digital twin simulations of the human performing the set of tasks within the value chain network, and
• operating data associated with the set of value chain network entities;
• automatically generating, by the computing device executing the trained set of machine learning models, a task traditionally performed by a human to be completed for the value chain network from the set of tasks based on a classification generated by the trained set of machine learning models, by:
• receiving, by the intermediary, inputs from the set of independent neural networks, and
• aggregating, by the intermediary, the inputs to generate the task;
• configuring, by the computing device executing the trained set of machine learning models, a robotic process automation system to train a set of physical robots to execute the task to facilitate an improvement in the value chain network;
• training, by the computing device executing the robotic process automation system, at least one robot of the set of physical robots to execute the task;
• instructing, by the computing device, the at least one robot of the set of physical robots, to execute the task;
• gathering, by the at least one robot of the set of physical robots, feedback on an outcome of the executed task;
• providing, by the at least one robot of the set of physical robots, the feedback to the computing device;
• and
• retraining, by the computing device, at least one trained member of the trained set of machine learning models on the feedback.