Mathematical Optimization of an Inventory Model for Deteriorating Items with Time and Reliability-Dependent Demand

Abstract

This study presents a mathematical model to better manage inventory for goods that decay over time. Standard inventory models assume demand stays the same, but this model considers that demand changes depending on time and how reliable people think the product is. Decay is figured as a set amount of the inventory, which is normal for things like medicines and electronics. The main aim is to find the best reordering time that lowers the total cost of inventory, including expenses for ordering, keeping stock, decay, and making the product seem more reliable. Using math, we find a direct answer for the total cost and show that it is cost-effective. Computer tests show that if product reliability is not considered, demand is underestimated, and ordering is not as good as it could be. The results suggest that putting money into system reliability raises initial costs but makes market demand more steady and cuts long-term decay losses. This study gives supply chain managers a helpful way to make choices in unstable markets where product quality and timing are very important.