考虑退货的电商企业承诺到货期与努力水平决策

Promised delivery lead time and effort level decisions for e-commerce enterprise with product returns consideration

许多电商企业通过承诺较短的到货期来吸引消费者,但是忽略了其对产品退货的影响:一旦晚于承诺时间到达,将会在一定程度上导致退货。因此,本文假设承诺到货期不仅影响顾客的需求,并且对产品退货也产生影响。在实际到货时间服从随机分布的条件下,本文分别讨论电商企业单一承诺到货期决策与承诺到货期-努力水平联合决策两种情况,其中努力水平可以影响实际到货时间的分布并产生一定成本。结果表明:单一决策中,当退货对到货延迟较不敏感时,电商企业会采取最短承诺到货期策略;当该敏感度高于阈值后,商家则会逐渐的延长承诺到货期。情况二中,企业的最优联合决策呈双阈值形式。当延迟到达对退货的影响较小时,会率先采用最短承诺到货期-最低努力水平策略;当退货对于延迟到货的敏感度达到第一个阈值后,企业则会提高努力水平并继续保持最短承诺到货期;当该敏感度高于第二个阈值时,企业则会同时提高努力水平并延长承诺到货期。最后,文章通过数值算例进一步揭示了各参数,尤其是退货率、退货对于到货延迟的敏感系数,对于企业决策和利润的影响和作用机理。

Competing only on price is no longer enough in today’s fierce economy, which means e-commerce enterprises should provide not only low prices but also faster deliveries to respond to customers quickly. Many firms are offering promised delivery lead time (PDL) as a marketing strategy to attract customers in a time-sensitive market. However, the previous study ignores PDL’s effect on the product returns: the occurrence of returns depends on the delays in the delivery of orders to consumers relative to the initial promises made by the enterprises. Therefore, in this paper, we assume not only the market demand but also the product returns are PDL-sensitive and investigate the impact of returns on an enterprise’s decisions and profit. In this paper, we assume the enterprise use the full-refund policy with an exogenous price. Two scenarios are investigated: one in which the enterprise only decide the optimal PDL and the other in which the enterprise should decide both PDL and the effort level. We analyze the general model where the realized lead time t is a random variable in scenario 1. Further, we consider a special case where t follows uniform distribution for more specific analyses in scenario 2. We conduct numerical examples to better illustrate the impact of the returns sensitivities on the optimal decision and the profit. Our findings suggest that:(1) Depending on the interrelationships of parameters, such as the return rate to sales (demand), the return sensitivity to delay and cost sensitivity, E-commerce enterprises will choose different strategies in both scenarios. In scenario 1, the enterprise will use shortest-PDL strategy when the returns sensitive to delay (β) is relatively low, and lengthen PDL as β is larger than a threshold value. In scenario 2, the enterprise would take the strategies of shortest PDL- lowest effort level, shortest PDL- medium effort level and increasing PDL and effort level respectively while β increases.(2) It is quite crucial for enterprises to take delay dependent product returns into consideration while making PDL and other related decisions. Though the enterprise could make exact optimal decisions even without considering the possible returns from PDL in the case β is relatively low, the shortest PDL is no longer optimal in other cases. Furthermore, the enterprise with a high returns rate itself would be more susceptible to increase PDL for the same β than other enterprises. In summary, this paper gives some managerial insights for e-commerce enterprises to make decisions while considering delay dependent product returns. To our best knowledge, this paper is the first to incorporate the delay-dependent product returns into the mathematical model and illustrate the impact of returns on PDL and other decisions and profit.