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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

4 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)419-431
Journal / PublicationQuality Engineering
Volume30
Issue number3
Online published21 Mar 2018
Publication statusPublished - 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.

Research Area(s)

  • case-study research, CRISP-DM, data science, DMAIC, Lean Six Sigma, process improvement, Six Sigma method

Citation Format(s)

Lean Six Sigma meets data science : Integrating two approaches based on three case studies. / Zwetsloot, Inez M.; Kuiper, Alex; Akkerhuis, Thomas S.; de Koning, Henk.

In: Quality Engineering, Vol. 30, No. 3, 2018, p. 419-431.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review