ANALYZING THE IMPACT OF PRICE FORECASTING ON INVENTORY MANAGEMENT: A CASE STUDY IN ELECTRONIC MANUFACTURE

Abstract

Effective inventory management is crucial in industries dealing with volatile material prices, such as polypropylene. This study explores the challenges faced by electronic manufacturing companies, emphasizing the intricate balance required in Days of Inventory Outstanding (DIO) standards. Inaccurate price forecasting and fluctuating material costs pose operational risks, impacting financial stability. The study addresses these challenges by proposing a nuanced inventory management strategy, integrating an optimal price forecasting methodology tailored for polypropylene. The chosen method, random forest, demonstrates superior performance with a low Mean Squared Error (MSE) of 214.16 and Mean Absolute Error (MAE) of 9.26. This methodology ensures precise price predictions, enabling proactive decision-making. Implementation of the forecasting method successfully maintains DIO within the optimal range of 14 to 21 days, contributing to seamless material flow and efficient warehouse management. The study also highlights tangible financial benefits, with a 6.43% reduction in unit costs, showcasing the economic impact of accurate price predictions on cost-saving initiatives.