Demand Forecasting for Automobile Spare Parts: A Comparative Study of ARIMA and Exponential Smoothing

Effective inventory management is gaining increasing attention globally as businesses strive to optimize supply chain operations and reduce costs. In today’s competitive environment, where efficiency and sustainability are paramount, proper inventory management plays a critical role. It encompasses a wide range of processes, including inventory classification, prioritization, demand forecasting, and replenishment. This study integrates ABC-FSN analysis with advanced forecasting techniques—ARIMA (AutoRegressive Integrated Moving Average) and Exponential Smoothing—to enhance inventory control in medium-level automobile spare parts stores. ABC analysis categorizes inventory based on annual consumption value, while FSN analysis classifies items according to their movement rates (Fast-moving, Slow-moving, and Non-moving). ARIMA is employed to capture complex and irregular demand patterns, whereas Exponential Smoothing is utilized for stable and consistent demand trends. Using real-world data, the study compares the performance of both forecasting methods, demonstrating that ARIMA excels in handling complex demand patterns, while Exponential Smoothing is more effective for stable demand. The integration of classification and forecasting provides actionable insights for optimizing inventory levels, reducing stockouts, and minimizing holding costs. This framework not only enhances supply chain efficiency but also supports sustainable inventory management practices, contributing to a more resilient and future-ready supply chain.