关键词: 产品体验性, 退货策略, 库存管理

Abstract: There is a gap between the experiential products that customers acquire online through text and photographs and the experiential products that they experience in actual stores, which results in returns for experiential products that need customers to determine their value through sensory experience. This discrepancy can prevent customers from returning their purchases. Therefore, coordinating the optimal storage decisions of retailers and suppliers through customers’ experience preferences and retailers’ return strategies has become a vital management problem that must be handled in the omnichannel operation of experiential products. This problem needs to be solved as soon as possible. This paper investigates the impact of product experience and consumer experience preferences on the optimal storage level decisions and expected inventory costs of retailers and suppliers under three return strategies: NAR (no accept returns), ARNR (accept returns and not-sell returned products), and ARR (accept returns and sell returned products). NAR stands for “no accept returns” and ARNR stands for “accept returns and not-sell returned products”. ARR stands for “accept returns and sell returned products”.The findings contribute to optimizing the inventory costs of retailers and suppliers in the supply chain of experiential products, complement the research on the purchasing experience behavior of consumers in uncertain markets, and provide proof of value for applying AR/VR technology to the multichannel operations of experiential products.
The provider of experiential products places orders with the producer of the products and then wholesales those products to the retailer’s offline experience store, where those products are then sold to customers. At the same time, the provider implements channel cooperation with the retailer and directly distribute orders that the retailer’s main web store fulfills at the same price, whether they are made offline or online. In this study, we assume that the upstream manufacturer has adequate production capacity, and we investigate how offline experience stores and suppliers manage and control inventory levels to maximize profits while satisfying market demand from experiencing customers.
Based on the characteristics of experiential products, it is assumed that the experience coefficient of the product is expressed as β. The larger β is, the more experience service effort the retailer needs to put in. The service effort level provided by retailers operating experiential products and the market demand function of experiential products is constructed on this basis. Due to the product’s experiential nature, the consumers’ channel choice preference is analyzed. Assuming that the preference degree of consumer behavior influenced by online interactivity is i, i.e., consumers will choose to buy directly from the flagship store online with a preference probability of the preference probability of consumers choosing to buy from the offline experience store is (1-i). Constructed for retailers’ choice of NAR, ARNR, and ARR, suppliers’ expected inventory cost models, and offline experience stores under two categories of consumers’ full offline experience preference and incomplete offline experience preference are based on the two base variables of experience coefficient and preference degree. These variables measure the degree customers prefer complete or incomplete offline experiences. Ordering, storing, losses due to consumer channel shifts that result in out-of-stock conditions, and return fees are the components that make up the anticipated inventory costs. A numerical simulation of genuine examples is used to simulate and analyze the optimum approach for matching retailers and customer experience preferences while keeping supply chain inventory costs minimal. This is done to determine which strategy results in the lowest overall cost.
The findings indicate that the ideal storage amount of suppliers is unrelated to the return strategy of the retailers or the experiential product they offer; Instead, it is solely tied to the primary market demand. Nevertheless, the inventory cost is connected to the experience product and the return plan. When a retailer selects the ARR, they will find the maximum value of the ideal storage volume for the same product experience and preference for an incomplete offline experience. On the other hand, when a retailer selects the ARNR, they will find the lowest value of optimal storage volume for the same product experience and preference for an incomplete offline experience. The expected inventory cost of offline experience stores is relatively the highest when the retailer chooses the ARR, and the expected inventory cost of offline experience stores is relatively the lowest when the retailer chooses the ARNR under the same conditions regarding product experiential and incomplete offline experience preferences.
In order to make the analysis more manageable, many essential presumptions have been included in the model: (1)Retailers of experience products typically have both online and physical locations for their stores, as the market demand follows a consistent distribution. (2)The provider has the highest supply capacity necessary to fulfill the market’s requirements. Research can be conducted in the future using a wholly randomized setting by computer simulation of a more realistic market demand distribution function. This will allow for a better exploration of the impact of product experience and consumer choice preferences on retailers’ return problems. Additionally, research can be gradually extended to the entire supply chain to more effectively guide inventory management issues that arise within the supply chain.

Key words: products experience, return policy, inventory cost management