Optimal policies and heuristics to match supply with demand for online retailing

• Main Authors: DENG, Qiyuan, LI, Xiaobo, LIM, Yun Fong, LIU, Fang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: online seasonal sales; product ordering; inventory allocation; order fulfillment; multiple periods; Business; E-Commerce; Operations and Supply Chain Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/4528; https://ink.library.smu.edu.sg/context/lkcsb_research/article/5527/viewcontent/yflim_MSOM2024b_ORG.pdf
• Institution: Singapore Management University

Problem definition: We consider an online retailer selling multiple products to different zones over a finite horizon with multiple periods. At the start of the horizon, the retailer orders the products from a single supplier and stores them at multiple warehouses. The retailer determines the products’ order quantities and their storage quantities at each warehouse subject to its capacity constraint. At the end of each period, after random demands in the period are realized, the retailer chooses the retrieval quantities from each warehouse to fulfill the demands of each zone. The objective is to maximize the retailer’s expected profit over the finite horizon. Methodology/results: For the single-zone case, we show that the multi-period problem is equivalent to a single-period problem and the optimal retrieval decisions follow a greedy policy that retrieves products from the lowest-cost warehouse. We design a non-greedy algorithm to find the optimal storage policy, which preserves a nested property: Among all non-empty warehouses, a smaller-index warehouse contains all the products stored in a larger-index warehouse. We also analytically characterize the optimal ordering policy. The multi-zone case is unfortunately intractable analytically and we propose an efficient heuristic to solve it, which involves a non-trivial hybrid of three approximations. This hybrid heuristic outperforms two conventional benchmarks by up to 22.5% and 3.5% in our numerical experiments with various horizon lengths, fulfillment frequencies, warehouse capacities, demand variations, and demand correlations.