Shared data induced production process improvement

A System for developing or improving a process for producing a product from a material, the system comprises:

• a processor;
• and
• a memory storing computer-executable instructions that when executed by the processor cause the system to execute operations comprising:
• acquiring first process data from a first production site of a plurality of production sites for a production process of the product, second process data from a second production site of the plurality of production sites for the production process of the product, and parameters of the production process of the product,
• wherein the first process data comprises first defectivity data of the production process at the first production site, the second process data comprises second defectivity data of the production process at the second production site, and the production process comprises a process part;
• retrieving, based at least on the acquired first process data and the acquired second process data, a description of the production process from the first production site and the second production site;
• assigning the acquired first and second process data to respectively corresponding process part of the production process according to the parameters of the production process of the product;
• identifying and validating, based on the acquired first and second process data, according to supervised algorithms including a data analysis framework with a data model, characteristic of performance quality of the production process,
• wherein the characteristic indicates a removal rate of defective products from output of the production process;
• and
• creating, based the identified and validated characteristic, at least the respectively corresponding process part of the production process, thereby improving the performance quality of the production process.

A method, comprising:

• creating training data for training at least one of XGBoost, Random Forest, or artificial neural network by performing operations comprising:
• acquiring first process data from a first source of a plurality of sources for a production process of the product, second process data from a second source of the plurality of sources for the production process of the product, and parameters of the production process of the product,
• wherein the first process data comprises first defectivity data of the production process at the first source, the second process data comprises second defectivity data of the production process at the second source, and the production process comprises a process part;
• retrieving, based at least on the acquired first process data and the acquired second process data, a description of the production process from the first source and the second source;
• assigning the acquired first and second process data to respectively corresponding process part of the production process according to the parameters of the production process of the product;
• and
• creating, based at least on the description and the production process and the assigned acquired first and second process data to the respectively corresponding process part, training data.

A computer-readable non-transitory recording medium storing a computer-executable program instructions that when executed by a processor cause a computer system to execute operations comprising:

• acquiring first process data from a first source of a plurality of sources for a production process of the product, second process data from a second source of the plurality of sources for the production process of the product, and parameters of the production process of the product,
• wherein the first process data comprises first defectivity data of the production process at the first source, the second process data comprises second defectivity data of the production process at the second source, and the production process comprises a process part;
• retrieving, based at least on the acquired first process data and the acquired second process data, a description of the production process from the first source and the second source;
• assigning the acquired first and second process data to respectively corresponding process part of the production process according to the parameters of the production process of the product;
• identifying and validating, based on the acquired first and second process data, by a trained machine learning model previously trained by using the description and defectivity data of a produced product, a characteristic performance quality of the production process, wherein the characteristic indicates a removal rate of defective products from output of the production process;
• and
• creating, based on the identified and validated characteristic, at least the respectively corresponding process part of the production process, thereby improving the performance quality of the production process.