The large amounts of data generated by machines in a production process can be used to certify that a production process leads to quality products and, when this is not the case, diagnose the problems. However, to achieve this, the data must be properly prepared, cast into a model, and analyzed. To that end, in this project a generically applicable toolkit will be developed that consists of techniques and meth-ods from the areas of data science, artificial intelligence and operations research that make this possible. The toolkit is considered to become applicable as the project runs, by implementing and using it at partner organizations to certify production processes and diagnose production problems.
The large amounts of data generated by machines in a production process can be used to certify that a production process leads to quality products and, when this is not the case, diagnose the problems. However, to achieve this, the data must be properly prepared, cast into a model, and analyzed. To that end, in this project a generically applicable toolkit will be developed that consists of techniques and meth-ods from the areas of data science, artificial intelligence and operations research that make this possible. The toolkit is considered to become applicable as the project runs, by implementing and using it at partner organizations to certify production processes and diagnose production problems.

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