Title: Inventory Management of Imported Raw Materials Based on Continuous Review Policy Using EOQ, POQ, and Min-Max Stock Methods: A Case Study: PT XYZ
Authors: Naniek Utami Handayani and Robert
Volume: 9
Issue: 3
Pages: 15-26
Publication Date: 2025/03/28
Abstract:
Inventory management is a critical factor in ensuring the efficiency of supply chain operations, particularly in the Fast-Moving Consumer Goods (FMCG) industry, where demand fluctuations and lead time uncertainties pose significant challenges. This study examines the effectiveness of Economic Order Quantity (EOQ), Periodic Order Quantity (POQ), and Min-Max Stock methods within a continuous review policy framework to optimize raw material inventory management at PT Torabika Eka Semesta. The primary objective is to minimize total inventory costs while maintaining adequate stock levels to prevent shortages and overstocking. A case study approach was employed, utilizing historical inventory data and structured interviews with supply chain managers. The study analyzed safety stock levels, reorder points, inventory turnover, and total inventory costs across the three inventory control models. Statistical methods, including sensitivity analysis, were applied to assess the impact of demand variability on inventory performance. The results indicate that POQ offers the most cost-effective approach for handling fluctuating demand, as it aligns order cycles with consumption patterns, reducing holding and shortage costs. EOQ minimizes total inventory costs under stable demand conditions but struggles with demand unpredictability. The Min-Max Stock method provides a balance between availability and cost efficiency, yet incurs higher holding costs due to pre-set stock limits. Additionally, the integration of Standard Operating Procedures (SOPs) was found to enhance inventory responsiveness and minimize procurement inefficiencies. This study contributes to the existing body of knowledge by providing empirical insights into inventory control optimization for FMCG companies managing imported raw materials. The findings underscore the importance of selecting appropriate inventory control methods based on demand characteristics and supply chain constraints. Future research should explore the integration of predictive analytics and machine learning models to further enhance inventory forecasting accuracy.