Should Only Popular Products Be Stocked? Warehouse Assortment Selection for E-Commerce Companies

Xiaobo Li (), Hongyuan Lin () and Fang Liu ()
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Xiaobo Li: Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117576
Hongyuan Lin: Faculty of Business in SciTech, University of Science and Technology of China, Hefei 230026, People’s Republic of China; School of Management, University of Science and Technology of China, Hefei 230026, People’s Republic of China
Fang Liu: Durham University Business School, Durham University, Durham DH1 3LB, United Kingdom

Manufacturing & Service Operations Management, 2024, vol. 26, issue 4, 1372-1386

Abstract: Problem definition : This paper studies the single-warehouse assortment selection problem that aims to minimize the order fulfillment cost under the cardinality constraint. We propose two fulfillment-related cost functions corresponding to spillover fulfillment and order splitting. This problem includes the fill rate maximization problem as a special case. We show that although the objective function is submodular for a broad class of cost functions, the fill rate maximization problem with the largest order size being two is NP-hard. Methodology/results : To make the problem tractable to solve, we formulate the general warehouse assortment problem under the two types of cost functions as mixed integer linear programs (MILPs). We also provide a dynamic programming algorithm to solve the problem in polynomial time if orders are nonoverlapping. Furthermore, we propose a simple heuristic called the marginal choice indexing (MCI) policy that allows the warehouse to store the most popular products. This policy is easy to compute, and hence, it is scalable to large-size problems. Although the performance of MCI can be arbitrarily bad in some extreme scenarios, we find a general condition under which it is optimal. This condition is satisfied by many multi-purchase choice models. Managerial implications : Through extensive numerical experiments on a real-world data set from RiRiShun Logistics, we find that the MCI policy is surprisingly near optimal in all the settings we tested. Simply applying the MCI policy, the fill rate is estimated to improve by 9.18% on average compared with the current practice for the local transfer centers on the training data set. More surprisingly, the MCI policy outperforms the MILP optimal solution in 14 of 25 cases on the test data set, illustrating its robustness against demand fluctuations.

Keywords: warehouse assortment selection; demand choice models; submodular functions; marginal choice probability; service operations; retailing; supply chain management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:4:p:1372-1386

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