An Inventory Model with Learning for Perishable Items Under Inflation and Trade Credit Financing

In this chapter, a buyer inventory model for perishable items has been enriched with the impact of learning under inflationary conditions. The ordering and holding costs are determined by the learning effect environment. Generally, the trade credit policy helps to increase the sales of items and mostly involves between seller and buyer during ordering policies. In this paper the seller offers the trade credit policy to his buyer for the increasing of the perishable items and the retailer receives a policy concerning trade credit from the seller. The demand of the perishable items is calculated by their selling price and lifespan and the buyer offers return policy to the customer. Customers have the opportunity to return items to the store if they’re not happy with them. Ultimately, the retailer refused to reimburse the client for the returned goods. The exchanged products are resold by the merchant at the same asking price. When the lot size of demanded items is same in each delivery then it is more beneficial for the seller and buyer. The learning effect is more effective mathematical tool which minimizes the inventory cost. The learning effect involves in the holding cost, deterioration cost, and ordering cost of the buyer. It is permissible to have a partial backlog of shortages, with the quantity of shortages occurring at a pace dictated by the time it takes for the next lot to arrive following the replenishment of the previous lot. The main goal is to calculate the ideal order size, best selling price, and the ideal refill schedule to optimize the merchant's profit margin. An EOQ is constructed to examine the sample and determine the best response. When items are perishable, preservation should be performed to control the rate of deterioration. Finally, the current mathematical model minimizes total inventory cost in relation to cycle length. The numerical examples demonstrate the applicability of the current model. The model's sensitivity analysis has been examined, and some useful conclusions have been drawn by taking into account the model's various parameters.