Offline-Channel Planning in Smart Omnichannel Retailing

Jian Chen (), Yong Liang (), Hao Shen (), Zuo-Jun Max Shen () and Mengying Xue ()
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Jian Chen: Research Center for Contemporary Management, Key Research Institute of Humanities and Social Sciences at Universities, School of Economics and Management, Tsinghua University, Beijing 100084, China
Yong Liang: Research Center for Contemporary Management, Key Research Institute of Humanities and Social Sciences at Universities, School of Economics and Management, Tsinghua University, Beijing 100084, China
Hao Shen: School of Business, Renmin University of China, Beijing 100872, China
Zuo-Jun Max Shen: College of Engineering, University of California, Berkeley, California 94704; Faculty of Engineering, The University of Hong Kong, Hong Kong 999077, China; Faculty of Business and Economics, The University of Hong Kong, Hong Kong 999077, China
Mengying Xue: International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China

Manufacturing & Service Operations Management, 2022, vol. 24, issue 5, 2444-2462

Abstract: Problem definition : Observing the retail industry inevitably evolving into omnichannel, we study an offline-channel planning problem that helps an omnichannel retailer make store location and location-dependent assortment decisions in its offline channel to maximize profit across both online and offline channels, given that customers’ purchase decisions depend on not only their preferences across products but also, their valuation discrepancies across channels, as well as the hassle costs incurred. Academic/practical relevance : The proposed model and the solution approach extend the literature on retail-channel management, omnichannel assortment planning, and the broader field of smart retailing/cities. Methodology : We derive parameterized models to capture customers’ channel choice and product choice behaviors and customize a corresponding parameter estimation approach employing the expectation-maximization method. To solve the proposed optimization model, we develop a tractable mixed integer second-order conic programming reformulation and explore the structural properties of the reformulation to derive strengthening cuts in closed form. Results : We numerically validate the efficacy of the proposed solution approach and demonstrate the parameter estimation approach. We further draw managerial insights from the numerical studies using real data sets. Managerial implications : We verify that omnichannel retailers should provide location-dependent offline assortments. In addition, our benchmark studies reveal the necessity and significance of jointly determining offline store locations and assortments, as well as of incorporating the online channel while making offline-channel planning decisions.

Keywords: assortment planning; data analytics; facility location; omnichannel retail; smart-city operations; smart retailing (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:24:y:2022:i:5:p:2444-2462

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