The Development of Manufacturing Process Analysis: Lesson Learned from Process Mining
:
https://doi.org/10.9744/jti.16.2.95-106
Keywords:
Manufacturing process analysis, process mining, business processAbstract
Process analysis is recognized as a major stage in business process reengineering that has developed over the last two decades. In the field of manufacturing, manufacturing process analysis (MPA) is defined as performance analysis of the production process. The performance analysis is an outline from data and knowledge into useful forms that can be broadly applied in manufacturing sectors. Process mining, an emerge tool focusing on process perspective and resource perspective, is a way to analyze system based on the event log. The objective of this study is to extend the existing process analysis framework by considering attribute perspective. This study also aims to learn the lessons from some experiences on process mining in manufacture industries. The result of this study will help manufacturing organizations to utilize the process mining approach for analyzing their respective processes.References
Rozinat, A., de Jong, I.S.M., Gunther, C. W., and van der Aalst, W.M.P., Process Mining Applied to the Test Process of Wafer Scanners in ASML, IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, 39(4), 2009. [CrossRef]
Son, S., Yahya, B.N., and Song, M., A Manufacturing Resource Analysis in Configurable Manufacturing Execution Systems, Proceedings of International Symposium on Green Manufacturing and Applications, Busan, Korea, 2014.
Son, S., Yahya, B.N., Song, M., Choi, S., Hyeon, J., Lee, B., Jang, Y., and Sung, N., Process Mining for Manufacturing Process Analysis: A Case Study, Proceeding of 2nd Asia Pacific Conference on Business Process Management, Brisbane, Australia, 2014.
Aalst, W.M.P., Reijers, H.A., Weijters A.J.M.M., van Dongen B.F., Aalves de Mediros A.K., Song, M.S., and Verbeek, H.M.W., Business Process Mining: An Industrial Application, Information System, 32, 2007, pp. 713-732.[CrossRef]
Aalst, W.M.P., Process Mining: Discovery, Conformance and Enhancement of Business Processes, Berlin: Springer-Verlag, 2011.[CrossRef]
Aalst, W.M.P., Weijters, A., and Maruster, L., Workflow Mining: Discovering Process Models from Eveng Logs, IEEE Transactions on Knowledge and Data Engineering, 16(9), 2014, pp. 1128-1142.[CrossRef]
Aalst, W.M.P., Reijers, H. A., and Song, M., Discovering Social Networks from Event Logs, Computer Supported Cooperative Work (CSCW), 14(6), 2005, pp. 549-593.[CrossRef]
Aaslt, W.M.P., What Makes a Good Process Model?: Lesson learnt from Process Mining, Software and System Modeling, 11(4),2012,pp. 557-569.[CrossRef]
Hornix, P.T.G., Performance Analysis of Business Processes through Process Mining, Master thesis of Eindhoven University of Technology, 2008.
Weijters, A., Aalst, W.M.P. and de Medeiros, A., Process Mining with Heuristic Miner Algorithm, in BETA Working Paper Series, WP 166. Eindhoven University of Technology: Eindhoven, 2006.
Song, M., and van der Aalst, W.M.P., Towards Comprehensive Support for Organizational Mining. Decision Support System, 46(1), 2008, pp. 300-317.
Rozinat, A., Mans, R.S., Song, M., and van der Aalst, W.M.P., Discovering Simulation Models Information Systems, 34(3), 2009, pp. 305-327.
Gunther, C.W., and Aalst, W.M.P., Fuzzy Mining-Adaptive Process Simplification Based on Multi-perspective Metrics, BPM’07, Lecture Notes on Computer Science, 4714, 2007, pp. 328-343, Springer Berlin Heidelberg.
Downloads
Published
How to Cite
Issue
Section
License
Articles published in the Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY).
This work is licensed under a Creative Commons Attribution License (CC BY).
