A FlexSim-based simulation–optimization model to improve inbound quality inspections in retail supply chains: A real case study

Maintaining high-quality standards while decreasing operational costs remains a critical challenge in modern retail supply chains. Conventional inbound inspection policies typically rely on static resource allocations, which prove inadequate under fluctuating supplier defect rates and often result in elevated false-acceptance rates and inefficient inspector utilization. To address this, we develop a dynamic simulation–optimization framework that integrates discrete-event simulation with mathematical optimization to allocate inspection resources and adjust inspection efforts in real time based on observed defect patterns. The framework is empirically validated using real-world operational data from a multi-facility distribution network. Extensive scenario testing and sensitivity analyses demonstrate that defect-region thresholds enable proactive scaling of inspection efforts, significantly decreasing defective inflow while optimizing inspector resource utilization. The study yields three generalized insights: (i) inspection policies must be dynamically scaled according to defect-region thresholds rather than using fixed capacity allocations, as the false-acceptance rate rises sharply in high-defect regions without proportional increases in inspection efforts; (ii) warehouse-specific operational configurations (effort-driven vs. distance-driven) necessitate tailored performance metrics; and (iii) persistently high false-acceptance rate levels reveal structural bottlenecks requiring strategic, rather than incremental, interventions. These findings establish a generalizable framework for dynamic quality control, enabling supply chain managers to proactively align inspection resources with real-time variability across complex retail distribution networks.