Various embodiments are described for dynamic value stream management. A computing environment is directed to receive a stream of metrics from station computing devices each positioned at a station in a manufacturing process, where individual ones of the station computing devices have a sensor configured to generate metrics. The computing environment may determine an optimal allocation of resources for each of the stations in the manufacturing process based at least in part on the metrics. If a cycle time of a station falls below a threshold, personnel from another satisfactorily-performing station may be reassigned to the station based on cross-training metrics. A recommended action for the stations may be determined and presented in a display device.

Various embodiments are described for dynamic value stream management. A computing environment is directed to receive a stream of metrics from station computing devices each positioned at a station in a manufacturing process, where individual ones of the station computing devices have a sensor configured to generate metrics. The computing environment may determine an optimal allocation of resources for each of the stations in the manufacturing process based at least in part on the metrics. If a cycle time of a station falls below a threshold, personnel from another satisfactorily-performing station may be reassigned to the station based on cross-training metrics. A recommended action for the stations may be determined and presented in a display device.

Provided is a method and an arrangement for enabling a processing step for an object that is to be processed, wherein an availability result, which indicates an availability of the processing step, is determined for the processing step on the basis of rules.

A management method for objects supply and a management system using the same are provided. The management method is adapted to an object-supply network model including a start point, an end point, multiple sub-points and multiple sub-routes, and includes following steps: obtaining a plurality of object states and corresponding probability distributions of each supply sub-route and connection relationships among the supply sub-routes; listing a plurality of object supply routes corresponding to each of the object states, and calculating supply route reliability values of the respective object supply routes by a network reliability algorithm; and, managing the input objects and output objects according to the object supply route with the maximum supply route reliability value.

Provided is a method and an arrangement for enabling a processing step for an object that is to be processed, wherein an availability result, which indicates an availability of the processing step, is determined for the processing step on the basis of rules.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.

A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.

A digital twin system includes a library of different types of robot operating unit digital twins stored in a storage system. The digital twin system includes one or more interfaces through which information associated with a physical robot operating unit corresponding to an instance of the robot operating unit digital twins is communicated. The digital twin system includes a set of processors that execute a set of computer-readable instructions to collectively operate one or more execution environments for executing instances of a portion of the different types of robot operating unit digital twins. The digital twin system also generates digital twin instances for individual robot operating units, a team of robot operating units, or a fleet of robot operating units. The digital twin system simulates operation of a physical robot by executing an instance of a digital twin generated for the physical robot based on information communicated through the interfaces.