Perakendeciler için tersine lojistik üzerine makaleler

As the retail industry expands and evolves, particularly with the growth of e-commerce, retailers are increasingly faced with higher levels of product returns. With this increase, it is no longer feasible to dispatch returned products to outlets or landfills; hence, retailers must re-evaluate returns in their inventories to maximize profits while minimizing the environmental impact of unsold merchandise. In the fast fashion retail sector, handling product returns has become a significant challenge due to rapidly changing consumer preferences and high product return rates. In Chapter 2, we aim to study a retailer's optimal inventory control policy under product returns to maximize expected profit. We model a period's returns to be stochastically dependent on the previous period's sales quantity. Using dynamic programming formulation, we solve for the optimal periodic review inventory policy and provide structural results on the optimal policy of the final period. Through numerical studies, we show that incorporating detailed sales-dependent returns could increase a retailer's expected profit by 23%. Ignoring this dependency in determining the optimal inventory policy results in increased order frequency, higher levels of backorders, and more leftovers, but above all could lead to a significant overestimation of the resulting profit. In managing inventories, retailers must consider product return flows arriving from various sales periods and are obligated to track detailed sales and return data meticulously. In Chapter 3 we present an optimal inventory control policy for a retailer facing stochastic product returns over multiple periods to maximize expected profit during a single selling season consisting of finite periods. The problem is formulated using dynamic programming, and due to its computational complexity, we propose an Approximate Dynamic Programming value iteration algorithm using basis functions. Our proposed algorithm reduces the solution time drastically without a significant sacrifice from optimality and generates solutions for instances that are not solvable exactly otherwise. We assess the significance of utilizing detailed return information and show that our proposed model enhances retailers' profits compared to a model aggregating return information. We also show the increase in profit improvement under conditions of mark-down pricing or order capacity constraints. Retailers often employ various sales channels, concurrently managing both brickand- mortar and online stores. Implementing an omnichannel approach aims to harmonize the customer experience across all platforms; hence, customers encounter uniform pricing, service quality, and return conditions whether they purchase in-store or online. In Chapter 4, we present the optimal solution for the finite horizon, lost sales model, considering the complex structure of the omnichannel system and the distinctive dynamics of the fast fashion retail sector. In our model, the retailer has a warehouse for online sales and a brick-and-mortar store for offline sales. Customers can purchase items online and return them to the warehouse or the brick-and-mortar store. In the case of offline sales, returns are restricted to the brick-and-mortar store, resulting in two distinct return flows with different return rates for the brick-andmortar store. We aim to determine the optimal pre-season order quantity for the warehouse, considering the sales of both channels, along with the transshipment order quantity from the warehouse to the brick-and-mortar store during the season. We formulate the problem using dynamic programming and present a sensitivity analysis that provides valuable business insights. This dissertation introduces an inventory control model where stochastic returns depend on previous sales. The model is enhanced by integrating returns from multiple periods and multiple channels with various extensions. Through an extensive computational study, we propose several managerial insights regarding optimal inventory policies.