新鲜产品跨季销售中的动态库存管理策略研究

新鲜产品在跨季销售过程中不可避免地发生变质,给分销商带来较大的风险。本文基于一个两阶段决策模型来帮助分销商动态管理库存,在考虑产品质量随机变化的情形下更好地匹配供给与需求。具体来说,在采摘季节确定采购的总数量;在销售季节结合产品新鲜度的演变以及剩余库存水平的变化动态地决策每周期的销售量,以实现期望利润的最大化。我们采用动态规划模型分析销售期的库存管理决策,刻画了利润函数和最优决策的结构性质,并考察了库存量、新鲜度水平和价格等参数对最优库存决策的影响。数值实验结果表明,当产品变质风险大、需求不确定性低时,动态库存管理策略相比静态策略的利润改进效果更为显著。

Dynamic Inventory Rationing for Cross-Season Sales of Fresh Products

With the development of cold chain and the rise of fresh food e-commerce, inventory management for fresh products gets a great deal of attention both from academic and industry areas. Different from managing durable products, the wholesaler faces a difficult challenge of managing fresh products because of their highly perishable nature. This paper focuses on a specific issue that fresh products are sold across several seasons when the wholesaler meets complicated operational risks. On one hand, the product's quality decreases all the time, resulting in a higher demand uncertainty; on the other hand, there's only one chance to purchase in the harvest period while there are several sales periods afterwards. Unlike the traditional inventory problems solving the optimal ordering quantities and times, this paper aims to figure out when and how many to sell across different periods.Based on the assumption that the product's quality declines in a given stochastic process, a two-stage model is developed to manage the inventory in a dynamic manner. Specifically, in the first stage the wholesaler determines the total procurement quantity for the whole horizon, and in the second stage he determines the sales quantity for each selling period. The dynamic programming approach is used to solve those rationing decisions backwards. By analyzing the structural properties of the profit functions, it is proved that the optimal inventory rationing decision of each period is only determined and can be solved by the KKT condition. Furthermore, the parameter influences on the seller's different rationing decisions are analyzed, for example, with other parameters unchanged, the larger the current inventory amount is, the lower the current price is, the higher the current freshness level is or the less the perishable risk is, the larger the inventory amount rationed to the future selling periods will be. In addition, our numerical experiments show that comparing with the static policy which analyzes the multi-periods as independent markets, the dynamic policy can take the inventory pooling effect on reducing risks and increasing profits, especially when the perishability risk is higher, when the market size is bigger, or when the demand is less uncertain.A dynamic method is presented in this paper to help the wholesaler match limited supply with multi-period demand effectively, and some meaningful insights into the inventory management of fresh products are provided. Further work is required to extend the model in more realistic conditions, such as adding the freshness-keeping effort decisions and so on.