Abstract
In the intensely competitive landscape of global manufacturing, achieving operational excellence is paramount for survival and success. Central to this endeavor is the optimization of Overall Equipment Effectiveness (OEE), a key metric that directly influences resource efficiency and product quality. Manufacturing companies face numerous challenges, including maintenance inefficiencies, unplanned downtimes, and product quality variations, which hinder the achievement of optimal OEE levels. The research explores the valuable role of machine learning in OEE enhancement, with a specific focus on bolstering availability, a critical key performance indicator in this context. This paper delves into a comprehensive numerical experiment, elucidating the machinery under scrutiny, data collection intricacies, machine learning model construction, and meticulous validation processes. The chosen machine learning model is Random Forest, selected for its top-notch accuracy and F1 score. The model’s deployment and real-world application are delineated, demonstrating a notable OEE improvement from 49% to 52% between July 2023 and September 2023. This paper provides a structured path for manufacturing optimization, fortified by machine learning, to address the challenges of enhancing OEE. It underscores the critical role of OEE in the manufacturing domain and offers a holistic framework for addressing its complexities.
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References
Saha, D., Syamsunder, M., Chakraborty, S.: Manufacturing Performance Management using SAP OEE. Apress, Berkeley, CA (2016). https://doi.org/10.1007/978-1-4842-1150-2
Dunn, T.: 8 - OEE Effectiveness. In: Dunn, T. (ed.) Flexible Packaging, pp. 77–85. William Andrew Publishing, Oxford (2015)
Ginste, L.V.D., Aghezzaf, E.-H., Cottyn, J.: The role of equipment flexibility in overall equipment effectiveness (OEE)-driven process improvement. Procedia CIRP 107, 289–294 (2022). https://doi.org/10.1016/j.procir.2022.04.047
Mishra, S.: Machine learning applications in subsurface energy resource management: state of the art and future prognosis. CRC Press, Boca Raton (2022)
Clere, A., Bansal, V.: Machine learning with dynamics 365 and power platform: the ultimate guide to apply predictive analytics, 1st edn. Wiley (2022)
Martinelli R, Cristina Martins Queiroz Mariano F, Bertini Martins C (2022) Single machine scheduling in make to order environments: A systematic review. Computers & Industrial Engineering 169:108190. https://doi.org/10.1016/j.cie.2022.108190
Zhai, S., Kandemir, M.G., Reinhart, G.: Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution. Prod. Eng. Res. Devel. 16, 65–88 (2022). https://doi.org/10.1007/s11740-021-01064-0
Stamatis DH (2011) The OEE Primer: understanding overall equipment effectiveness, reliability, and maintainability, 1st ed. Productivity Press
Kennedy, R.K.: Understanding, Measuring, and Improving Overall Equipment Effectiveness: How to Use OEE to Drive Significant Process Improvement, 1st edn. Productivity Press;Taylor & Francis, CRC Press (2018)
Jiang, H.: Machine learning fundamentals: a concise introduction. Cambridge University Press, New (2022)
May, M.C., Fang, Z., Eitel, M.B.M., et al.: Graph-based prediction of missing KPIs through optimization and random forests for KPI systems. Prod. Eng. Res. Devel. 17, 211–222 (2023). https://doi.org/10.1007/s11740-022-01179-y
Xiong, L., Yao, Y.: Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm. Build. Environ. 202, 108026 (2021). https://doi.org/10.1016/j.buildenv.2021.108026
Maataoui S, Bencheikh G, Bencheikh G (2023) predictive maintenance in the industrial sector: a CRISP-DM approach for developing accurate machine failure prediction models. In: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp 223–227
EASY ProductionServer. https://www.schleuniger.com/en/software/digitalization-1/easy-productionserver/. Accessed 27 Oct 2023
Relkar, A.S., Nandurkar, K.N.: Optimizing and analysing overall equipment effectiveness (OEE) through design of experiments (DOE). Procedia Eng. 38, 2973–2980 (2012). https://doi.org/10.1016/j.proeng.2012.06.347
Roosefert Mohan, T., Preetha Roselyn, J., Annie Uthra, R., et al.: Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery. Comput. Ind. Eng. 157, 107267 (2021). https://doi.org/10.1016/j.cie.2021.107267
DeVries, Z., Locke, E., Hoda, M., et al.: Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability. Spine J. 21, 1135–1142 (2021). https://doi.org/10.1016/j.spinee.2021.02.007
Anandan, R., Gopalakrishnan, S., Pal, S., Zaman, N. (eds.): The industrial internet of things (IIoT): Intelligent Analytics for Predictive Maintenance. Wiley (2022). https://doi.org/10.1002/9781119769026
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Maataoui, S., Bencheikh, G., Bencheikh, G. (2024). Elevating Manufacturing Excellence: A Data-Driven Approach to Optimize Overall Equipment Effectiveness (OEE) for a Single Machine. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-031-54318-0_29
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