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Lean Six Sigma meets data science: Integrating two approaches based on three case studies

Lean Six Sigma meets data science: Integrating two approaches based on three case studies The amount of available data is rapidly increasing, which is an opportunity to the Lean Six Sigma (LSS) methodology. Starting off with a well-established definition of LSS as theoretical foundations we employ theory-generating case-study research. Three successful improvement projects from a large financial services firm in the Netherlands are analyzed. Clear differences to the definition of LSS are observed. The research leads to three recommendations for integrating data science in LSS. Concerning the structure of an improvement organization, skills of employees and, practical modifications to LSS's celebrated DMAIC roadmap to solidify its applicability in the modern age of data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality Engineering Taylor & Francis

Lean Six Sigma meets data science: Integrating two approaches based on three case studies

Lean Six Sigma meets data science: Integrating two approaches based on three case studies

Quality Engineering , Volume 30 (3): 13 – Jul 3, 2018

Abstract

The amount of available data is rapidly increasing, which is an opportunity to the Lean Six Sigma (LSS) methodology. Starting off with a well-established definition of LSS as theoretical foundations we employ theory-generating case-study research. Three successful improvement projects from a large financial services firm in the Netherlands are analyzed. Clear differences to the definition of LSS are observed. The research leads to three recommendations for integrating data science in LSS. Concerning the structure of an improvement organization, skills of employees and, practical modifications to LSS's celebrated DMAIC roadmap to solidify its applicability in the modern age of data.

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References (45)

Publisher
Taylor & Francis
Copyright
© 2018 Taylor & Francis
ISSN
1532-4222
eISSN
0898-2112
DOI
10.1080/08982112.2018.1434892
Publisher site
See Article on Publisher Site

Abstract

The amount of available data is rapidly increasing, which is an opportunity to the Lean Six Sigma (LSS) methodology. Starting off with a well-established definition of LSS as theoretical foundations we employ theory-generating case-study research. Three successful improvement projects from a large financial services firm in the Netherlands are analyzed. Clear differences to the definition of LSS are observed. The research leads to three recommendations for integrating data science in LSS. Concerning the structure of an improvement organization, skills of employees and, practical modifications to LSS's celebrated DMAIC roadmap to solidify its applicability in the modern age of data.

Journal

Quality EngineeringTaylor & Francis

Published: Jul 3, 2018

Keywords: case-study research; CRISP-DM; data science; DMAIC; Lean Six Sigma; process improvement; Six Sigma method

There are no references for this article.