Optimizing procurement and inventory strategies with Solver

Abstract

In the field of physical logistics, efficient and precise control of material flow is regarded as a crucial aspect of modern manufacturing management. To address the challenges of limited transport capacity and variable demand, three alternative inventory and procurement strategies have been examined through Solver-based optimization. The first approach, a Reorder Point model with fixed order quantity, has been evaluated for its simplicity and responsiveness. The second, a Min–Max inventory policy, has been assessed for its adaptive replenishment and stability under uncertainty. The third, a Fixed Interval Ordering model, has been analyzed for its predictable scheduling and reduced logistics costs. Each method has been tested under identical cost structures, including purchase price, transportation, and shortage penalties. The obtained results are intended to demonstrate how optimized selection among these models can contribute to improved cost efficiency and reliability in constrained industrial supply systems.