Prediction of Top Key Performance Indicator in Automotive Production System using Data Mining

Abstract

In the past years, the Automotive industry has faced many challenges such as shifting market, digitalization, increased competition, to recent issues of semiconductor shortages. Technology is transforming the automotive industry at a fast pace, and predictive analytics is at its core if harnessed efficiently. Predictive analytics present significant opportunities by using techniques like data mining and machine learning by aiding decision-makers to optimize production processes in the manufacturing plants. Forecasting the critical key performance indicators also ensures a decision support system for the domain experts to take corrective actions. In this research paper, a case study is carried out at the Volkswagen passenger car manufacturing plant in Germany with three years of data to predict one of the top key performance indicators (HPV -Hours Per Vehicle) from the production system. In the global automotive industry, HPV is considered a dominant controlling indicator in production systems. Predicting this HPV ensures robust production planning and provides transparency about the influencing variables so that necessary measures can be taken proactively to improve HPV compared to the planned HPV budget. Comparison between different machine learning algorithms such as Decision tree, Neural network, Linear Regression etc., is done to find accurate machine learning models for key performance indicator prediction based on historical data. Case study results indicate that a Neural network can predict HPV with 2.5% relative error based on historical data. A higher coefficient of determination (R 2 =0.8) illustrates that the selected model is stable for prediction. The results show that the machine learning algorithms can be effectively used for forecasting the HPV for the Volkswagen plant and understanding the impact of influencing factors on HPV.