Data-Driven Lean Warehousing And Hybrid Simulation To Improve Perfect Order Rate In Medical Supply SMEs

Medical supply SMEs in emerging markets face persistent challenges in warehouse efficiency, inventory accuracy, and service reliability, as noted in prior logistics research. This study addresses these constraints by targeting the low Perfect Order Rate (POR) of a Peruvian medical supply importer, affected by inventory discrepancies, picking congestion, and documentation errors. A data-driven Lean Warehousing model was developed, integrating 5S, ABC-based slotting, cycle counting, and poka-yoke mechanisms, and validated through a hybrid method combining a six-week pilot implementation with discrete-event simulation in Arena and FlexSim. Results show improvements in On-Time Delivery from 91.35% to 94.15%, Defect-Free Products from 95.12% to 98.89%, and Process Cycle Efficiency from 5% to 37%, raising the projected POR from 82.51% to 88.30%. These findings highlight the operational and socioeconomic relevance of Lean– Digital practices for SMEs and encourage further exploration of data-driven enhancements in regulated supply chains.