Optimization of Reorder Point Efficiency for Football Jerseys under Normal and Seasonal Demand Patterns Using Discrete Event Simulation

Abstract

Effective inventory management is critical for enterprises navigating volatile demand, particularly during peak seasonal cycles. This study investigates the optimization of the Reorder Point (ROP) for a football jersey retailer in Pathum Thani, which experiences significant demand surges during competition months. Under standard operations, the retailer utilizes a baseline ROP of 300 units; however, a seasonal adjustment to 600 units proved inadequate, resulting in frequent stockouts, lost sales revenue, and diminished customer satisfaction. To address these challenges, a FlexSim simulation was employed to evaluate the efficacy of 14 distinct ROP configurations. The baseline model (Scenario C), featuring a normal ROP of 300 and a seasonal ROP of 600, yielded a service level (fill rate) of 79.39% and a stockout rate of 20.61%. While incrementally raising the seasonal ROP to 1,000 units (Scenario 8) improved the service level to 93.04%, it also resulted in 32 units of residual stock. Further sensitivity analysis revealed that while Scenario 13 (ROP 550/1000) achieved a 100% service level, it produced an undesirable surplus of 72 units. Ultimately, Scenario 14 (ROP 550/950) emerged as the optimal strategy, achieving a 99.87% service level with a negligible 0.13% stockout rate and zero ending inventory. These findings demonstrate that dynamic ROP calibration effectively balances the trade-off between product availability and inventory holding costs.