Optimization of an Advanced Integrated Inventory Model Considering Shortages and Deterioration across Varying Demand Functions

Abstract

To determine and emphasize the importance of Internet of Things (IoT)-enabled investment in an inventory model confronted with shortages, storage costs, and deterioration of goods, this study focuses on maximizing maximum stock level while minimizing overall inventory-related expenditures. Conventional inventory models frequently ignore the effect of digital evaluation on sustaining inventory levels and preventing deterioration, resulting in inefficient decision-making. An enhanced inventory model is offered, which uses internet of things (IoT) technology to track inventory factors in real time, hence lowering degradation, shortages and holding costs. To account for the influence of demand fluctuation, three distinct demand structures are investigated: (i) linear price and stock-dependent demand, (ii) a price function with a negative power of a constant, and (iii) an exponential function of price. These demand structures explain several competitive scenarios in which demand is influenced by costs and availability of inventory. To assess the efficacy of the developed IoT-based model, a comparative investigation is carried out under these three demand situations. Secondary data from Abu Hashan Md Mashud’s research are used to support the numerical analysis. Results shows that the maximum inventory level per cycle for the Cases I, II and III are 188.584482, 402.584988, 303.434275 and the total costs for the Cases I, II and III are $1108.00326, $786.214411, $1373.11204 respectively. Amongst the three demand variations, the demand model that involves raising the price to a negative power of a constant outperforms the others, resulting in the highest optimum stock levels. The numerical research’s findings reveal that IoT integration not only improves operational effectiveness, but also leads to a substantial rise in maximum stock level every cycle. The research’s key innovation resides in its integration of IoT technology with inventory models in a variety of demand situations, an approach that has yet to be completely explored in the existing literature. The findings indicate that IoT-based inventory models are exceptionally successful at controlling stock, reducing degradation, and enhancing profitability, particularly when demand follows nonlinear patterns such as the negative power form.