Combining value stream mapping and process mining in production: a systematic literature review

Discover Applied Sciences, vol. 8

Abstract

Abstract Value stream mapping is a widely used and proven method in lean production for gaining transparency into the current state of a production process. To reduce the inefficiency of manually mapping increasingly complex production systems, the growing availability of digital shop floor data offers the potential to digitalize and automate value stream mapping. Approaches based on process mining are considered particularly promising. Therefore, this review article aims to identify, compare, and synthesize existing approaches that combine value stream mapping and process mining in production based on a systematic literature review according to PRISMA 2020. The review includes 21 out of 2682 initially identified records. They are analyzed regarding their metadata, content, and process mining-specific data items. The results are discussed in terms of research gaps and future research directions. While the reviewed approaches offer valuable practical benefits, especially for in-depth analyses, most records present just concepts or case studies without systematic tool support. Further research on novel process mining techniques dedicated to value stream mapping is needed for true integration of value stream mapping and process mining. This review contributes to this effort by identifying open research gaps and suggesting four future research directions.

Authors 4

1. Tim Teriete corresponding

   Fraunhofer Institute for Manufacturing Engineering and Automation

   Affiliation as printed

   Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany

2. Klaus Erlach

   University of Stuttgart · Fraunhofer Institute for Manufacturing Engineering and Automation

   Affiliation as printed

   Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany

   Institute of Industrial Manufacturing and Management (IFF), University of Stuttgart, Stuttgart, Germany

3. Thomas Bauernhansl

   University of Stuttgart · Fraunhofer Institute for Manufacturing Engineering and Automation

   Affiliation as printed

   Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany

   Institute of Industrial Manufacturing and Management (IFF), University of Stuttgart, Stuttgart, Germany

4. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany