Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry

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.