Using process mining to improve productivity in make-to-stock manufacturing
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Author / Producer
Date
2021
Publication Type
Journal Article, Journal Article
ETH Bibliography
yes
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Abstract
This paper proposes a data-driven procedure to improve productivity in make-to-stock manufacturing. By leveraging recent developments in information systems research, the paper addresses manufacturing systems with high process complexity and variety. Specifically, the proposed procedure draws upon process mining to dynamically map and analyse manufacturing processes in an automated manner. This way, manufacturers can leverage data to overcome the limitations of existing process mapping methods, which only provide static snapshots of process flows. By bridging data and process science, process mining can exploit hitherto untapped potential for productivity improvement. The proposed procedure is empirically validated at a leading manufacturer of sanitary products. The field test leads to three concrete improvement suggestions for the company. This research contributes to the literature on production research by demonstrating a novel use of process mining in manufacturing and by guiding practitioners in its implementation.
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Publication status
published
External links
Editor
Book title
Journal / series
Volume
59 (16)
Pages / Article No.
4869 - 4880
Publisher
Taylor & Francis
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Industry 4.0; Process mining; Productivity improvement; Make-to-stock production; Empirical study
Organisational unit
09501 - Netland, Torbjörn / Netland, Torbjörn