Stochastic Multi-Objective Sales and Operations Planning with Plan Stability Objectives and Supply Order Allocation Using Simulation–Optimization

Sales and operations planning (S&OP), despite its importance in balancing demand and supply, faces significant challenges due to system complexity, uncertainty, and conflicting objectives. While previous research has primarily examined the effects of integration, flexibility, and inventory control on cost and customer service level under demand uncertainty, it has often overlooked the simultaneous consideration of plan stability, procurement time uncertainty, and supplier capacity constraints. This study addresses these gaps by developing a multi-objective S&OP simulation–optimization model that jointly considers plan stability objectives and capacity-constrained supply order allocation under both demand and procurement time uncertainty. Computational results from an automotive industry case study show that frozen horizon length has a more significant impact on customer service, delivery time, and plan stability than on total profit. A bi-sourcing policy proved more advantageous than single sourcing even under unlimited capacity and lower unit costs, while moderate procurement lead-time uncertainty outperformed deterministic lead times under long supply intervals. This reveals counterintuitive temporal supply dynamics in which lead-time overlaps and order crossings yield favorable logistics cost trade-offs. The results also indicate that sourcing policies, supply intervals and lead-time uncertainty influence plan stability even when frozen horizon length is fixed. These findings provide manufacturers with insights to balance flexibility and stability in S&OP, while managing multiple uncertainty sources and multi-sourcing strategies in dynamic supply chain environments.