Lean Logistics and BPM Applied to Inventory Management in a Peruvian SME: Evidence from a Technology Sector Case

Abstract

Inventory management remains a persistent challenge for SMEs in the Peruvian technology sector, where empirical decision-making often leads to stock imbalances and financial losses. Prior studies have addressed these issues using isolated tools, but few have proposed an integrated and replicable model. This research aimed to resolve inefficiencies in inventory control by designing and implementing a model that combines Lean Logistics, BPM, EOQ, ABC classification, and statistical forecasting. The model was applied in a Lima-based SME, involving process standardization, demand forecasting, and optimal order quantity calculations. As a result, average inventory decreased by 50%, turnover improved by 142%, and forecast accuracy increased, with the mean absolute error dropping from 37.5% to 18.7%. These improvements contributed to cost savings and greater service efficiency. Academically, the study offers a structured framework for operational enhancement. Socioeconomically, it empowers small firms to professionalize supply chains. Further exploration is encouraged to adapt the model to other sectors and integrate advanced analytics.