Micro, Small, and Medium Enterprises (MSMEs) in the Maintenance, Repair, and Operations (MRO) sector often suffer from operational inefficiencies due to intuition-based inventory management. This study addresses such problems in PT XYZ, an MRO distributor that is experiencing significant overstocking and deadstock. Utilizing a quantitative case study methodology where 32 months of historical data have been used, this study classifies demand patterns based on the Syntetos-Boylan Categorization Matrix and tests the performance of different time-series forecasting models. The study determines the optimal inventory parameters (EOQ, ROP, Safety Stock) according to the most accurate forecasting models. Results indicate that the suggested data-driven system can release approximately Rp 604 million in frozen working capital and reduce annual inventory costs by 85.7% (Rp 113 million).
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