A production system and method may comprise a first production processing machine capable of processing a workpiece and a second production processing machine capable of processing the workpiece. The production system and method may also comprise a workpiece transfer device, the workpiece transfer device moving the workpiece from the first production processing machine to the second production processing machine, an inspection device identifying whether the workpiece meets at least one specification of the workpiece, and a computing device in communication with the inspection device notifying a user whether the workpiece is compliant with the at least one specification where the computing device is operative communication with either or both of the first production processing machine and the second production processing machine whereby the computing device alters operation of either or both of the first and second production processing machines.

Aspects of the application relate to methods, a computer program and a process control device. According to one aspect, a computer-implemented method for determining a multivariate process chart is provided. The multivariate process chart is to be used to control a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The multivariate process chart includes a first trajectory, an upper limit for the first trajectory and a lower limit for the first trajectory.

The present disclosure provides a method and system for predicting a potential future energy consumption of a plurality of energy loads in a built environment. The method includes a step of collecting a first set of statistical data associated with a plurality of energy consuming devices. The method includes another step of accumulating a second set of statistical data associated with each of a plurality of users present inside the built environment. The method includes yet another step of analyzing the first set of statistical data and the second set of statistical data. In addition, the method includes yet another step of predicting a set of predictions associated with the potential future energy consumption of each of the plurality of energy consuming devices.

The present disclosure provides a system and method for recommending one or more potential changes in operating parameters of a built environment. The one or more potential changes are associated with a plurality of energy load sources. The method includes a step of fetching a first set of statistical data associated with each of a plurality of energy consuming devices. The method includes another step of collecting a third set of statistical data associated with one or more ambient parameters of the built environment. The method includes yet another step of analyzing the first set of statistical data, the second set of statistical data and the third set of statistical data. In addition, the method includes yet another step of recommending the one or more potential changes for the energy consumption of each of the plurality of energy consuming devices.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Techniques for qualifying for use in an overall manufacturing process items produced by a bulk manufacturing process that has a plurality of batch effects are presented. The techniques can include obtaining a collection of items produced by a bulk manufacturing process that has a plurality of batch effects; measuring a quantifiable property of a sample of items from the collection of items; developing a linear mixed model for the quantifiable property based on the measuring; determining a statistical process control standard deviation for the collection of items based on the linear mixed model; computing a statistical process control parameter from the statistical process control standard deviation; determining that at least a portion of the collection of items conform to the statistical process control parameter; accepting at least a portion of the collection of items; and using at least a portion of the collection of items in the overall manufacturing process.

Techniques for qualifying for use in an overall manufacturing process items produced by a bulk manufacturing process that has a plurality of batch effects are presented. The techniques can include obtaining a collection of items produced by a bulk manufacturing process that has a plurality of batch effects; measuring a quantifiable property of a sample of items from the collection of items; developing a linear mixed model for the quantifiable property based on the measuring; determining a statistical process control standard deviation for the collection of items based on the linear mixed model; computing a statistical process control parameter from the statistical process control standard deviation; determining that at least a portion of the collection of items conform to the statistical process control parameter; accepting at least a portion of the collection of items; and using at least a portion of the collection of items in the overall manufacturing process.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

A lithographic process is performed on a plurality of semiconductor substrates. The method includes selecting one or more of the substrates as one or more sample substrates. Metrology steps are performed only on the selected one or more sample substrates. Based on metrology results of the selected one or more sample substrates, corrections are defined for use in controlling processing of the substrates or of future substrates. The selection of the one or more sample substrates is based at least partly on statistical analysis of object data measured in relation to the substrates. The same object data or other data can be used for grouping substrates into groups. Selecting of one or more sample substrates can include selecting substrates that are identified by the statistical analysis as most representative of the substrates in their group and/or include elimination of one or more substrates that are identified as unrepresentative.

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.