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Volume 13 | Issue 6 | Year 2026 | Article Id. IJME-V13I6P107 | DOI : https://doi.org/10.14445/23488360/IJME-V13I6P107

A Data-Driven Integrated Model for Improving Inventory Turnover in SMEs through Lean Warehousing and Min–Max Policy

Daniel Bedriñana-Marañon, Daniela Alexandra Rendón-Valdivia, S. Nallusamy, Juan Carlos Quiroz-Flores

Received Revised Accepted Published
12 Mar 2026 14 Apr 2026 16 May 2026 27 Jun 2026

Citation :

Daniel Bedriñana-Marañon, Daniela Alexandra Rendón-Valdivia, S. Nallusamy, Juan Carlos Quiroz-Flores, "A Data-Driven Integrated Model for Improving Inventory Turnover in SMEs through Lean Warehousing and Min–Max Policy," International Journal of Mechanical Engineering, vol. 13, no. 6, pp. 80-95, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I6P107

Abstract

This research focused on a Peruvian industrial distribution SME experiencing low inventory turnover, a typical problem of contexts with high demand change and limited inventory control. While prior works have explored Lean Warehousing and inventory policies individually, integrated efforts are scarce. This research aimed to improve inventory turnover through a data-driven model combining 5S, cycle counting, and a Min–Max inventory policy under continuous review. Following an applied case study methodology, we validated the results of the model through a hybrid strategy that involved a pilot implementation and simulation with the software Arena. A reduced logistics cost of 82%, a decrease in immobilized inventory by 76%, an increase in inventory record accuracy from 86% to 95%, and a reduction of not located products from 45% to 0.4% confirm that the integration of Lean practices with structured inventory policies boosts operational performance of SMEs. Future research may develop more sophisticated forecasting methods and explore the implications of the digital integration of inventory management on more value-added decision-making.

Keywords

Inventory Turnover, Lean Warehousing, Min–Max inventory policy, Cycle Counting, Data-Driven Inventory Management.

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