Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry
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METHODOLOGIES AND APPLICATION
Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry Choumicha EL Mazgualdi1 • Tawfik Masrour1
•
Ibtissam El Hassani1 • Abdelmoula Khdoudi1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Key performance indicators are tools for management, decision support and forecasting; they reflect the strategy and vision of the company in terms of objectives and allow to always staying in step with the expectations of the stakeholders. Accurate forecasting of the indicators allows decisions to be reoriented to ensure performance optimization while reducing both cost and effort. This paper aims to apply different machine learning methods, namely support vector regression, optimized support vector regression (using genetic algorithm), random forest, extreme gradient boosting and deep learning to predict the overall equipment effectiveness as a case study. We will make use of several configurations of the listed models in order to provide a wide field of comparison. The data used to train our models were provided by an automotive cable production industry. The result shows that the configuration in which we used cross-validation technique, and we performed a duly splitting of data, provides predictor models with the better performances. Keywords Machine learning Key performance indicators Overall equipment effectiveness Prediction Improvement
1 Introduction 1.1 Key performance indicators (KPI) Today, dashboards have become an indispensable tool. It must provide managers, from operational management to top management, with the information they need in order to make decisions. It consists of a set of indicators designed to allow managers to see the progress of their systems, and it Communicated by V. Loia. & Tawfik Masrour [email protected] Choumicha EL Mazgualdi [email protected] Ibtissam El Hassani [email protected] Abdelmoula Khdoudi [email protected] 1
Laboratory of Mathematical Modeling, Simulation and Smart Systems (L2M3S), Artificial Intelligence for Engineering Sciences Team, ENSAM-Meknes, Moulay ISMAIL University, B.P. 15290, Marjane 2, Al-Mansor, 50500 Meknes, Morocco
reflects the company’s strategy and vision in terms of objectives. So, it allows to follow both the targeted results and the actions, both corrective and preventive ones that achieve the objectives set. Key performance indicators are most often the result of a long chain of information gathering and aggregation, and they generally allow responsiveness and decisions are made more and more quickly. But that is not enough in the current context. Today, in order to maintain or gain competitive advantage, organizations place the search for ‘‘proactivity’’ at the forefront of their concerns: the precise forecasts of the indicators make possible to redirect decisions in order to guarantee an optimization of the performances, and reducing costs and effort
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