We develop an inventory control policy for perishable products considering both random demand and random lead time. We consider a B2C retail environment where excess demand is lost. The policy dynamically determines the optimal replenishment quantity under a service level constraint in every period, allowing for order-crossing, a widely disregarded characteristic in the literature. Regarding perishability, we compare the two most extreme issuing policies, first-expired-first-out (FEFO) and last-expired-first-out (LEFO), and evaluate our policy to existing inventory policies for perishables that typically ignore lead time uncertainty.

We obtain several interesting findings. First, we show that ignoring lead time uncertainty and planning based on the expected lead time significantly undershoots the target service level. Even planning with the maximum lead time, under LEFO, the achieved service level would still fall considerably below the target, which the lost-sales structure can explain. On the other hand, under FEFO, the achieved service level would overshoot the target service level, which leads to unnecessary waste. Second, a more reliable lead time can significantly reduce waste, especially under LEFO. Third, our model allows us to distinguish between past, present, and future lead time uncertainty and thus to consider partial lead time information. We show the value of lead time information on outstanding orders. Fourth, we evaluate the impact of a fast but unreliable delivery option and a slow but reliable delivery option on the retailer’s average waste and ordering process. We find that the optimal choice depends on the demand characteristics.

考虑到随机需求和随机提前期，我们为易腐产品制定了库存控制政策。我们考虑一个 B2C 零售环境，其中失去了多余的需求。该政策在每个时期的服务水平约束下动态确定最佳补货数量，允许订单交叉，这是文献中被广泛忽视的特征。关于易腐烂性，我们比较了两种最极端的发行政策，先过期先出 (FEFO) 和后过期先出 (LEFO)，并将我们的政策与通常忽略交货时间不确定性的现有易腐物品库存政策进行评估.

我们获得了几个有趣的发现。首先，我们表明忽略提前期的不确定性和基于预期提前期的计划会大大低于目标服务水平。即使在 LEFO 下计划了最长的提前期，所达到的服务水平仍会大大低于目标，这可以用销售损失结构来解释。另一方面，在 FEFO 下，实现的服务水平会超过目标服务水平，从而导致不必要的浪费。其次，更可靠的交货时间可以显着减少浪费，尤其是在 LEFO 下。第三，我们的模型允许我们区分过去、现在和未来的提前期不确定性，从而考虑部分提前期信息。我们显示未完成订单的提前期信息的价值。第四，我们评估了快速但不可靠的交付选项和缓慢但可靠的交付选项对零售商平均浪费和订购流程的影响。我们发现最优选择取决于需求特征。