Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

Freiesleben, Johannes; Keim, Jan; Grutsch, Markus (2020). Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement? Quality and Reliability Engineering International, 36(6), pp. 1837-1848. John Wiley & Sons 10.1002/qre.2579

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Machine Learning (ML), or the ability of self-learning computer algorithms to autonomously structure and interpret data, is a methodological approach to solve complicated optimization problems based on abundant data. ML is recently gaining momentum as algorithmic applications, computing potency, and available data sets increased manifold over the past two decades, providing an information-rich environment in which human reasoning can partially be replaced by computer reasoning. In this paper, we want to assess the implications of ML for Design of Experiments (DoE), a statistical methodology widely used in Quality Management for quantifying effects and interactions of factors with influence on the production quality or the process yield. We specifically want to assess the future role and importance of DoE: Will it remain unaltered by ML, will it be made obsolete, or will it be reinforced? With this, we want to contribute to the discussion of the future use of traditional Quality Management methodologies in production, as our ML assessment can in principle be applied to other statistical methodologies as well. While we are convinced that ML will heavily impact the field of Quality Management and its predominant set of statistical methodologies, we find reason to expect that this impact will be a mutual one. As this is the first paper addressing the joint force potential of the two methodologies ML and DoE, we expect a range of follow-up papers being written on the subject and a spark in specialized applications addressing DoE's ML-enhanced vital functionality for process improvements.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Innovation and Strategic Entrepreneurship Business School > Institute for Innovation and Strategic Entrepreneurship > Strategic Entrepreneurship Business School
Name:	Freiesleben, Johannes; Keim, Jan and Grutsch, Markus
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
ISSN:	0748-8017
Publisher:	John Wiley & Sons
Language:	English
Submitter:	Jan Keim
Date Deposited:	11 Aug 2022 14:05
Last Modified:	11 Aug 2022 14:05
Publisher DOI:	10.1002/qre.2579
Uncontrolled Keywords:	Design of Experiments, in-process monitoring, machine learning, optimization, qualityimprovement, quality maintenance
ARBOR DOI:	10.24451/arbor.17205
URI:	https://arbor.bfh.ch/id/eprint/17205