Process Mining for Six Sigma: Utilising Digital Traces. (March 2021)

• Record Type: Journal Article
• Authors: Kregel, I.; Stemann, D.; Koch, J.; Coners, A.
• Is Part Of: Computers & industrial engineering. Volume 153(2021)
• Publication Date: 2021-03
• Subjects: process mining -- six sigma -- DMAIC -- big data analytics -- data science -- project management; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.107083
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: Process Mining can enhance the data analytics capabilities of Six Sigma. We developed a method to integrate Process Mining into Six Sigma's DMAIC procedure. Refinements were made by Six Sigma experts and during a practical application. The method was successfully tested in a multi case study. Abstract: Six Sigma is one of the most successful quality management philosophies of the past 20 years. However, the current challenges facing companies, such as rising process and supply chain complexity, as well as high volumes of unstructured data, cannot easily be answered by relying on traditional Six Sigma tools. Instead, the Process Mining (PM) technology using big data analytics promises valuable support for 6S and its data analysis capabilities. The article presents a design science research project in which a method for the integration of PM in Six Sigma's DMAIC project structure was developed. This method could be extended, refined and tested during three evaluation cycles: an expert evaluation with Six Sigma professionals, a technical experiment and finally a multi case study in a company. The method therefore was eventually endorsed by 6S experts and successfully applied in a first pilot setting. This article presents the first developed method for the integration of PM and Six Sigma. It follows the recommendations of many researchers to test Six Sigma as an application field of PM as well as using the potential of big data analytics. The method can be used by researchers and practitioners alike to implement, test and verify its design in organisations.