Coordinated inventory policies for meeting demands from both store and online BOPS channels

• Published in: Computers & industrial engineering Vol. 145; p. 106542
• Main Authors: Lu, Jye-Chyi, Yang, Yifan, Han, Shu-Yi, Tsao, Yu-Chung, Xin, Yuxuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2020
• Subjects: Coordinated inventory; Dual-channel competition; Optimal order quantity; Service constraint; Store demand satisfaction probability; Store demand satisfaction probability; Coordinated inventory; Optimal order quantity; Dual-channel competition; Service constraint
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2020.106542

•Coordinated inventory-allocation policies for in-store and online channels.•Single- and multi-variable studies of six model parameter-value changes.•Online demand, cross-selling-effect and %store-keep have larger impacts.•Joint-inventory but separated-order policy leads to larger profits. When Internet and eCommerce become more popular, an increasing number of people prefer to shop online rather than in-store. As a result, many local stores are being shut down. To help keep retailer’s store open and improve its competitiveness, many stores at present incorporate the “buy online, pick up in-store” (BOPS) service. This has given rise to new challenges in deciding how to allocate the limited store shelf-spaces to meet both store and BOPS demands. Our optimal decisions of product order-quantity and inventory-allocation for meeting dual-channel demands include constraints on probability in satisfying in-store demands and limitation of total inventory-space. This study develops four models representing the traditional situation without the BOPS service: benchmark model; separated inventory and separated order for two channels; joint inventory but separated order for two channels; and joint inventory and joint order for two channels. Extensive single- and multiple-variable experiments explores impact of changing model-parameter values (e.g., BOPS demand, cross selling effect) to expected total profits and optimal order-quantities. Conditions of which model leads to larger profits are discussed.