Performance Prediction of an Automotive Assembly Line Based on ARMA-ANN Modeling

Abstract

This research paper aims to predict the automotive Body-In-White (BIW) robotic welding assembly line performance. A combinational prediction model based on the Autoregressive Moving Average (ARMA) and Artificial Neural Networks (ANN) is developed. Classical methods are often used to predict the assembly line throughput, but not ideal. A combinational prediction model is applied for comprehensive analysis and prediction of the assembly line throughput. The various case studies presented in this paper indicate that the precision of the model is better than the other models. This research has significant practical value to the assembly plant because, based on the prediction, plant can make commitment to achieve the production to meet the market demand. Unpredictable performance of the assembly line in the plant leads to more overtime, less time for maintenance and eventually hurting the company bottom line.