Inventory accuracy is essential for maintaining the reliability of power plant operations. PT X currently relies on annual physical stocktaking as its inventory control method; however, the average inventory accuracy during 2022–2024 was only 67.15%, below the company's target of at least 95%. This condition highlights the need for a more effective inventory control method capable of detecting discrepancies earlier and continuously. This study aims to develop an ABC-XYZ classification-based cycle counting model to improve inventory control in PT X's operational material warehouse. A quantitative approach was employed by analyzing Annual Consumption Value (ACV) and material demand patterns to determine inventory priorities and cycle counting frequencies. The results show that 253 items (10.4%) classified as Category A contributed 80% of the annual consumption value, while Category Z accounted for 66.3% of total items, indicating highly irregular demand patterns. Implementation of the proposed model during the first quarter of 2026 was completed largely according to schedule, with an overall completion rate of 83.0% and an average inventory discrepancy of 2%. The proposed model has the potential to help the company reach its inventory accuracy target, accelerate discrepancy detection without interrupting warehouse operations, and provide a more effective inventory control framework for PT X.
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