A Mathematical Optimization Framework for Cost-Efficient Inventory Management in MSMEs

Abstract

One of the major difficulties in inventory management that Micro, Small, and Medium Enterprises (MSMEs) encounter is fluctuating market demand, resource limitations, and rising operational costs. In this paper, we present a mathematically optimized formulation aimed at reducing the total inventory and supply chain operational costs while maximizing efficiency within a door hinge manufacturing MSME. The optimization formulation integrates procurement cost, transportation cost (inbound and outbound), facility operational cost, and inventory holding cost together with operational constraints such as supplier capacities, facility production capacities, inventory balance equations, and customer demand requirements. The optimization problem is developed using Mixed Integer Linear Programming (MILP) and solved using Microsoft Excel Solver to achieve an optimal allocation of resources. Real industrial data from a door hinge manufacturer is utilized to validate the model. Comparative benchmark evaluation reveals that the proposed MILP model reduces total supply chain operating costs by 30.2% compared to traditional MSME practices, slashes ending inventory capital by 51.4%, and achieves a 100% customer demand fulfilment service level.