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The implementation of robust, stable, and user-centered data analytics andmachine learning models is confronted by numerous challenges in production andmanufacturing. Therefore, a systematic approach is required to develop,evaluate, and deploy such models. The data-driven knowledge discovery frameworkprovides an orderly partition of the data-mining processes to ensure thepractical implementation of data analytics and machine learning models.However, the practical application of robust industry-specific data-drivenknowledge discovery models faces multiple data-- and model-development--relatedissues. These issues should be carefully addressed by allowing a flexible,customized, and industry-specific knowledge discovery framework; in our case,this takes the form of the cross-industry standard process for data mining(CRISP-DM). This framework is designed to ensure active cooperation betweendifferent phases to adequately address data- and model-related issues. In thispaper, we review several extensions of CRISP-DM models and variousdata-robustness-- and model-robustness--related problems in machine learning,which currently lacks proper cooperation between data experts and businessexperts because of the limitations of data-driven knowledge discovery models.

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing