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An intelligent and automated Continuous Process Qualification (CPQ) micro-service for rapid monitoring of stability, capability, and performance of manufacturing processes. i4QPQ is based on real-time data with improved smart data analytics and algorithms, for achieving faster process approval and in-line continuous process validation after process reconfiguration.

A collection of optimisation micro-services that use simulation to evaluate different possible scenarios and propose changes in the configuration parameters of the manufacturing line to achieve improved quality targets. i4QLRT AI learning algorithms develop strategies for machine parameters calibration, line setup and line reconfiguration.

A micro-service for providing rapid diagnosis of manufacturing line on the possible cause of failures, evaluating data fidelity, product-quality and process condition, and providing action recommendations for sensor/data processing recalibrations, process line/machine reconfiguration or maintenance actions.

A micro-service consisting of simulation models as a service, taking as input the manufacturing resources, current production planning and process condition, and proposing process configuration parameters, ensuring that non-simulation experts may also exploit the prescriptive analyses.

Provides an audit procedure applied to the manufacturing resources (machine, cell or manufacturing line) for ensuring that the data resulting from the manufacturing processes are accurate and reliable, providing recommendations for process reconfiguration, audit strategies, certificates and regulations. i4QLCP addresses definition and vocabulary, frame and application areas, prerequisites, planning, implementation, controlling, improvement and documentation of data-driven qualification, reconfiguration, and quality control.

A multi-media user guide for adapting the optimisation reasoning rules according to the achieved results for machine parameters calibration, line setup and line reconfiguration. i4QLRG allows to increase productivity and reduce the efforts for manufacturing line reconfiguration through AI, considering both automated approaches and collaboration with humans.