Abstract
This paper studies inventory replenishment planning problems for online retailers able to obtain advance demand information (ADI) in an environment of time-varying demands. We incorporate ADI into dynamic lot-sizing models to formulate the replenishment planning problems for online retailers in three scenarios: (1) companies act as pure-play online retailers with customers homogeneous in demand lead time, (2) online customers are heterogeneous in demand lead time with priorities, and (3) online retailers operate in a bricks and clicks structure, in which demands come from online and offline channels, with either independent or interactive channels. Based on the optimality properties, we design dynamic programming algorithms with polynomial running time to solve the model. We find a method with the computational complexity of O(NT). We conduct numerical experiments for several online retailers and find that the method can significantly reduce the total inventory cost.