Optimizing Spare Parts Inventory Management of Truck Dealer Services using Forecasting Methods and Continuous Review System Approach

Abstract

A truck dealership company, specializing in maintenance, repair, and spare parts sales, faces stockout challenges that hinder its ability to meet demand for moving code 1 spare parts. The absence of effective forecasting methods and safety stock policies exacerbates these issues. This study aims to optimize inventory management by identifying suitable forecasting methods and implementing the Continuous Review System (CRS) to establish safety stock and reorder points as the parameter for procurement planning. The results indicate that the Double Exponential Smoothing (DES) method effectively predicts demand, while the Monte Carlo simulation method performs better for spare part 493051110L. The CRS approach improves the fill rate and reduces stockout risks, ensuring better inventory management for the company. These findings provide a framework for the company to enhance its spare parts inventory strategy, contributing to improved service reliability and operational efficiency.