Demand Forecasting and Budget Planning for Automotive Supply Chain

Abstract

Over the past 20 years, there have been significant changes in the supply chain business. One of the most significant changes has been the development of supply chain management systems. It is now essential to use cutting-edge technologies to maintain competitiveness in a highly dynamic environment. Restocking inventories is one of a supplier’s main survival strategies and knowing what expenses to expect in the next month aids in better decision-making. This study aims to solve the three most common industry problems in Supply Chain – Inventory Management, Budget Fore-casting, and Cost vs Benefit of every supplier. The selection of the best forecasting model is still a major problem in much research in literature. In this context, this article aims to compare the performances of Auto-Regressive Integrated Moving Average (ARIMA), Holt-Winters (HW), and Long Short-Term Memory (LSTM) models for the prediction of a time series formed by the dataset of Supply Chain products. As performance measures, metric analysis of the Root Mean Square Error (RMSE) is used. The main concentration is on the Automotive Business Unit with the top 3 products under this segment and the country United States being in focus. All three models, ARIMA, HW, and LSTM obtained better results regarding the performance metrics.