Dynamic reorder point replenishment strategies for a capacitated supply chain with seasonal demand

• Framework to dynamically adjust and re-optimize the replenishment decision variables.
• Developed simulation model and performed simulation–optimization experiments.
• Comparative evaluation of static and dynamic strategies under seasonal demand patterns.
• Analyzed the effect of dynamic adjustment on the transient behavior of the system.
• Average finished goods inventory decreases drastically with dynamic adjustments.

Managing inventory and service levels in a capacitated supply chain environment with seasonal demand requires appropriate selection and readjustment of replenishment decision variables. This study focuses on the dynamic adjustment of decision variables within supply chains using continuous-review reorder point (ROP) replenishment. A framework is proposed to adjust reorder points and lot sizes based on optimal settings within different regions of a seasonal demand cycle. This framework also includes the optimal timing of adjustments defining these regions. A discrete-event simulation model of a simple, capacity-constrained supply chain is developed and simulation–optimization experiments are performed, the objective being to minimize the total supply chain inventory subject to a target delivery service level. The performance of ROP systems with optimal static and optimal dynamic decision variable settings are compared using two different seasonal demand patterns. The results confirm that performance with dynamic decision variable adjustment is better. For a given delivery service level, average work-in-process inventory levels are almost the same for both systems. However average finished goods inventory levels decrease significantly and are more stable under dynamic adjustment. The practical implication is that both finished goods holding costs and maximum storage capacity requirements are reduced.