Increase OEE at Manual Assembly Lines by Data Mining

Peter Dobra; János Jósvai

doi:10.14513/actatechjaur.v13.n2.539

Authors

Peter Dobra Adient Hungary Kft, Hammerstein u. 2, H-8060, Mór, Hungary
János Jósvai Széchenyi István University, Department of Vehicle Manufacturing, Egyetem tér 1, H-9026, Győr, Hungary

DOI:

https://doi.org/10.14513/actatechjaur.v13.n2.539

Keywords:

OEE, data mining, MES, assembly line, KDD

Abstract

The industrial companies often use Key Performance Indicators (KPI) to follow up and evaluate their process and success. One of the KPIs is the Overall Equipment Effectiveness (OEE) which represents the efficiency of the manufacturing area. The high OEE value means good performance of the machines or lines. This paper presents a method in order to increase OEE at the manual assembly lines by data mining. Firstly, a literature review demonstrates scientific relevance. Secondly, a method is introduced for improving the efficiency with the help of the recognised patterns. Using Manufacturing Execution System (MES) data the OEE percentage was increased by 8% in 3 months without any financial investment.

Downloads

Download data is not yet available.

References

R. S. Kaplan, D. P. Norton, The balanced scorecard – Measures that drive performance, Harvard Business Review 1 (1991) pp. 71-79.

R. Hedman, M. Subramaniyan, P. Almström, Analysis of critical factors for automatic measurement of OEE, Procedia CIRP 57 (2016) pp. 128–133. doi: https://doi.org/10.1016/j.procir.2016.11.023

G. Schuh, G. Reinhart, J.P. Prote, F. Sauermann, J. Horsthofer, F. Oppolzer, D. Knoll, Data mining definitions and applications for the management of production complexity, Procedia CIRP 81 (2019) pp. 874–879. doi: https://doi.org/10.1016/j.procir.2019.03.217

C. Gröger, F. Niedermann, B. Mitschang, Data mining-driven manufacturing process optimization, Proceedings of the World Congress on Engineering 2012.

S. LaValle, E. Lesser, R. Shockley, M. S. Hopkins, N. Kruschwitz, Big data, analytics and the path from insights to value, MIT Sloan Management Review 52 (2) (2011) pp. 21-32.

J. A. Harding, M. Shahbaz, Srinivas, A. Kusiak, Data mining in manufacturing: A review, Journal of Manufacturing Science and Engineering 128 (2006) pp. 969–976. doi: https://doi.org/10.1115/1.2194554

B. Denkena, M.A. Dittrich, S. Wilmsmeier, Automated production data feedback for adaptive work planning and production control, Procedia Manufacturing 28 (2019) pp. 18–23. doi: https://doi.org/10.1016/j.promfg.2018.12.004

U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, The KDD process for extracting useful knowledge from volumes of data, Communications of the ACM 39 (11) (1996) pp. 27-34.

U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From data mining to knowledge discovery in databases, AI Magazine 17 (3) (1996) pp. 37-54.

R. Wirth, J. Hipp, CRISP-DM: Towards a standard process model for data mining, In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining (2000) pp. 29-39.

J. A. Harding, M. Shahbaz, Srinivas, A. Kusiak, Data mining in manufacturing: A review, Journal of Manufacturing Science and Engineering 128 (2006) pp. 969–976. doi: https://doi.org/10.1115/1.2194554

S. Huber, H. Wiemer, D. Schneider, S. Ihlenfeldt, DMME: Data mining methodology for engineering applications – a holistic extension to the CRISP-DM Model, Procedia CIRP 79 (2019) pp. 403–408. doi: https://doi.org/10.1016/j.procir.2019.02.106

S. V. Buer, G. I. Fragapane, J. O. Strandhagen, The data-driven process improvement cycle: Using digitalization for continuous improvement, IFAC PapersOnLine 51 (11) (2018) pp. 1035–1040. doi: https://doi.org/10.1016/j.ifacol.2018.08.471

B. Agard, A. Kusiak, Data mining for subassembly selection, Journal of Manufacturing Science and Engineering 126 (2004) pp. 627–631. doi: https://doi.org/10.1115/1.1763182

A. K. Choudhary, J. A. Harding, M. K. Tiwari, Data mining in manufacturing: a review based on the kind of knowledge, Journal of Intelligent Manufacturing 20-5 (2009) pp. 501–521. doi: https://doi.org/10.1007/s10845-008-0145-x

P. Backus, M. Janakiram, S. Mowzoon, C. Runger, A. Bhargava, Factory cycle-time prediction with a data-mining approach, IEEE Transactions on Semiconductor Manufacturing 19 (2006) pp. 252-258. doi: https://doi.org/10.1109/TSM.2006.873400

A. Öztürk, S. Kayalıgil, N. E. Özdemirel, Manufacturing lead time estimation using data mining, European Journal of Operational Research 173 (2006) pp. 683–700. doi: https://doi.org/10.1016/j.ejor.2005.03.015

Y. Meidan, B. Lerner, M. Hassoun, G. Rabinowitz, Data mining for cycle time key factor identification and prediction in semiconductor manufacturing, Proceedings of the 13th IFAC (2009) pp. 217–222. doi: https://doi.org/10.3182/20090603-3-RU-2001.0466

M. Subramaniyan, Production data analytics – To identify productivity potentials, Chalmers University of Technology, Gothenburg, Sweden, 2015

M. Mainea, L. Duta, P. Ciprian, I. Caciula , A method to optimize the Overall Equipment Effectiveness, IFAC Proceedings (2010) pp. 237–241. doi: https://doi.org/10.3182/20100908-3-PT-3007.00046