A system includes a memory and a processing device, operatively coupled to the memory, to perform operations including receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing. The data associated with the process comprises at least one of: an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings. The operations further include obtaining an output by applying the data associated with the process to the trained machine learning model. The output is representative of the defect impact with respect to the at least one defect type.

A method controls sheet metal processing of a sheet metal component to be manufactured with a plurality of processing steps. The method includes: A) reading in machine tool data and data of the sheet metal component to be manufactured, which includes (in any order): a) reading in the processing steps of the sheet metal component to be manufactured; b) reading in property parameters of the sheet metal component to be manufactured; c) reading in at least one basic processing capability of the machine tools; and d) reading in capability parameters of the machine tools. The method also includes B) creating a machine tool matrix made from machine tools suitable for manufacturing the sheet metal component; C) determining an optimized processing sequence in a form of a sequence of machine tools from the machine tool matrix; and D) outputting the optimized processing sequence.

A production planning system (6) for a raw materials industry plant (ANL), which determines the production planning data (Pi) thereof and specifies said data to the automation system (1) of the plant (ANL). A state monitoring system (7) determines previous and future anticipated states (Z1) of components of the plant (ANL). A quality determination system (8) determines states (Z2) of output products (Ai) produced and still to be produced by the plant (ANL) and/or past and future states (Z3) of the plant (ANL) as a whole. A maintenance planning system (9) and/or the production planning system (6) receive, from the state monitoring system (7), the states (Z1) of the components of the plant (ANL), determined by the state monitoring system (7) and, from the quality determination system (8), the states (Z2 and Z3) of the output products (Ai) and/or of the plant (ANL) as a whole, determined by the quality determination system (8). They consider the data received from the state monitoring system (7) and from the quality determination system (8) in the determination of maintenance planning data (W) and/or the production planning data (Pi).

A method for controlling a manufacturing of final metal product(s) from intermediate metal product(s) is implemented by an electronic controlling device and comprises, for each intermediate metal product acquiring (110) a set of intermediate characteristic(s) (C.sub.IP) for said intermediate metal product; determining (120) a current estimated set of final characteristic(s) (C.sub.est_cur) with a prediction model, according to the set of intermediate characteristic(s) and a current manufacturing route; comparing (130) the current estimated set of final characteristic(s) with a current target set of final characteristic(s) (C.sub.target_cur); and if a deviation between the current estimated set of final characteristic(s) and target set of final characteristic(s) is above a threshold obtaining (140) new target set(s) of final characteristic(s) (C.sub.target_new) for new final metal product(s); and calculating (150) a new manufacturing route according to the set of intermediate characteristic(s) and to the new target set(s) of final characteristic(s).

A method performed by a control system for a work machine includes receiving machine data indicative of operating parameters on the work machine, receiving a set of performance scores indicative of relative machine performance in a set of different performance categories, each performance score being generated by a different performance score generator based on sensor data associated with the work machine, accessing a set of rules that each map one or more triggering conditions to a corresponding adjustment action on the work machine, identifying a set of potential adjustment actions by evaluating fulfillment of the set of rules based on the operating parameters and performance scores, and correlating each potential adjustment action to one or more of the performance categories, selecting a particular adjustment action, from the set of potential adjustment actions, based on an indication of a selected target performance category, and outputting a control instruction based on the particular adjustment action.

A production planning system (6) for a raw materials industry plant (ANL), which determines the production planning data (Pi) thereof and specifies said data to the automation system (1) of the plant (ANL). A state monitoring system (7) determines previous and future anticipated states (Z1) of components of the plant (ANL). A quality determination system (8) determines states (Z2) of output products (Ai) produced and still to be produced by the plant (ANL) and/or past and future states (Z3) of the plant (ANL) as a whole. A maintenance planning system (9) and/or the production planning system (6) receive, from the state monitoring system (7), the states (Z1) of the components of the plant (ANL), determined by the state monitoring system (7) and, from the quality determination system (8), the states (Z2 and Z3) of the output products (Ai) and/or of the plant (ANL) as a whole, determined by the quality determination system (8). They consider the data received from the state monitoring system (7) and from the quality determination system (8) in the determination of maintenance planning data (W) and/or the production planning data (Pi).

A manufacturing management apparatus includes an operation information acquisition unit, a trace information acquisition unit, a storage unit, a manufacturing device determination unit, and a transfer destination indication unit. The storage unit stores inspection information on each article in association with the trace information. The manufacturing device determination unit creates combinations of the manufacturing devices that can perform manufacturing processes in consideration of operation information and trace information, determines a quality index from the inspection information for each of the created combinations, and determines, based on the quality index determined for each of the combinations, a combination of the manufacturing devices to be used. A transfer destination indication unit indicates a transfer destination of a part to a transfer device based on the determined combination of the manufacturing devices.

The present teachings relate to a method for improving a production process for manufacturing a chemical product using at least one input material at an industrial plant, the industrial plant comprising a plurality of physically separated equipment zones, the method comprising: providing, via an interface, an upstream object identifier comprising input material data; receiving, at a computing unit, real-time process data from one or more of the equipment zones; determining, via the computing unit, a subset of the real-time process data based on the upstream object identifier and a zone presence signal; computing, via the computing unit, at least one zone-specific performance parameter of the chemical product related to the upstream object identifier based on the subset of the real-time process data and historical data; determining, in response to at least one of the performance parameters, a target equipment zone where the input material and/or chemical product is to be sent. The present teachings also relate to a system, and a software program.

A method performed by a control system for a work machine includes receiving machine data indicative of operating parameters on the work machine, receiving a set of performance scores indicative of relative machine performance in a set of different performance categories, each performance score being generated by a different performance score generator based on sensor data associated with the work machine, accessing a set of rules that each map one or more triggering conditions to a corresponding adjustment action on the work machine, identifying a set of potential adjustment actions by evaluating fulfillment of the set of rules based on the operating parameters and performance scores, and correlating each potential adjustment action to one or more of the performance categories, selecting a particular adjustment action, from the set of potential adjustment actions, based on an indication of a selected target performance category, and outputting a control instruction based on the particular adjustment action.

A computer-implemented method for operating a modular technical system that includes a technical module and a module-spanning management system, where the method includes transmitting constraints and targets of an operation of the technical system from the management system to either the technical module or a corresponding computer-implemented representation of the technical module; determining, via the at least one technical module or the corresponding computer-implemented representation of the technical module, an optimal operating point of the at least one technical module based on the previously received constraints and targets; determining at least one performance indicator, relating to the optimal operating point, of the corresponding technical module and transmitting the at least one performance indicator from the technical module or the corresponding computer-implemented representation to the management system, and orchestrating the technical module via the management system by incorporating the at least one performance indicator to operate the modular technical system.