Optimizing Spare Parts Safety Stock: Integrating Demand Variability and Operational Criticality in Smelting Operation

Abstrak

This quantitative study applies Monte Carlo simulation with 5,000 iterations to optimize inventory policies for Maintenance, Repair, and Operations (MRO) spare parts with intermittent demand at the Smelting & Refining Unit of PT Freeport Indonesia. Using 24 months of historical demand data (January 2024 to December 2025) extracted from the SAP ERP system, the study analyzes 45 critical Material Numbers (MN), consisting of 15 Mechanical, 15 Electrical, and 15 Instrumentation spare parts. The proposed approach integrates intermittent demand classification using the Average Demand Interval (ADI) and Coefficient of Variation Squared (CV²), operational criticality assessment, and Monte Carlo simulation to determine optimal safety stock and reorder point values. The simulation reduced total safety stock from 261 units to 105 units, a reduction of 59.77%, while average safety stock decreased from 5.80 to 2.33 units per Material Number. The proposed policy achieved an average service level of 95.92% and an average stockout probability of 4.08% across the 45 Material Numbers. Under the assumed holding cost scenario, the reduction in safety stock was estimated to generate potential holding cost savings of USD 62,045.54. Overall, integrating intermittent demand classification, operational criticality, and Monte Carlo simulation provides a practical decision support framework for recalibrating SAP Material Requirements Planning (MRP) parameters to improve inventory efficiency and operational reliability in continuous process industries.