Process Mining for Six Sigma
Teun Graafmans (),
Oktay Turetken (),
Hans Poppelaars () and
Dirk Fahland ()
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Teun Graafmans: Eindhoven University of Technology
Oktay Turetken: Eindhoven University of Technology
Hans Poppelaars: Eindhoven University of Technology
Dirk Fahland: Eindhoven University of Technology
Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2021, vol. 63, issue 3, No 6, 277-300
Abstract:
Abstract Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management.
Keywords: Process mining; Six Sigma; Define-measure-analyze-improve-control; DMAIC; Design science research; Process mining for Six Sigma; PMSS (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12599-020-00649-w
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