Offline-Channel Planning in Omnichannel Retail 

Speaker: Hao Shen, Assistant Professor, School of Business, Renmin University of China

Time: Jan 7, 2022, 09:00

Venue: Tencent Meeting: 938 546 083

Bio: Hao Shen is an assistant professor of Management Science at School of Business, Renmin University of China. He received his Ph.D. in Management Science and Engineering, and a B.E. in Engineering Mechanics, both from Tsinghua University. His research interests include supply chain management and data-driven decision methods. His work has been published in Manufacturing & Service Operations Management, Production & Operations Management, and INFORMS Journal on Applied Analytics. He also has industry experience in JD.com and DiDi (as an Operations Research/Data Analytics intern). A previous version of this work has been recognized as the Winner of the POMS-JD.com Best Data-Driven Research Paper Competition. 

Abstract: 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. The objective is 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.  This work extends the literature on retail-channel management, omnichannel assortment planning, and the broader field of smart retailing.

In particular, we first derive a parameterized model to capture customers’ channel choice and product choice behaviors, and customize a corresponding parameter estimation approach employing the expectation-maximization method. We then establish an optimization model for optimal offline-channel decisions, and develop a branch-and-cut solution approach based on a mixed integer second-order conic programming reformulation. We numerically validate the efficacy of the proposed parameter estimation and solution approaches.

We further draw managerial insights from the numerical studies using real data sets. 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.

Consumer Choice Modeling via Operational Data Analytics

Speaker: Mengying Xue Associate Professor, School of Management, University of Science and technology of China

Time: January 7, 2022, 10:00

Venue: Tencent Meeting: 795 332 094

Bio:

Mengying Xue is an associate professor at School of Management, University of Science and technology of China. She was a postdoc at Krannert School of Management, Purdue University and got her PhD from Tsinghua University. Her research focuses on integrating theoretical modeling and data analytics in supply chain management. Her research interests span the following areas: data-driven analytical approach, optimization modeling and algorithm, economic and game-theoretical analysis, and their applications in operations management, marketing and energy interfaces.

Abstract:

Choice models are widely applied in psychology, economics, transportation, marketing, and operations studies. An operational data analytics (ODA) framework is presented to estimate the general consumer choice model using data. This framework, generalizing the existing estimation methods for specific structural models, strikes a delicate balance between the (likely imprecise) structural knowledge and the data. This is achieved by articulating the domain of validation through extending the structural knowledge and by formulating the data-integration model based on the associated structural properties. We demonstrate the implementation of the ODA framework to identify the appropriate consumer choice models. The ODA estimate outperforms the existing parametric and nonparametric methods, particularly over the choice sets that are not covered in the data. We also discuss potential future research of developing ODA approaches to study the related aspects of consumer choice models.