Online Demand Fulfillment Under Limited Flexibility

Zhen Xu (), Hailun Zhang (), Jiheng Zhang () and Rachel Q. Zhang ()
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Zhen Xu: School of Management, Fudan University, Shanghai 200433, China;
Hailun Zhang: Institute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen 518172, China;
Jiheng Zhang: Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong S.A.R., China
Rachel Q. Zhang: Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong S.A.R., China

Management Science, 2020, vol. 66, issue 10, 4667-4685

Abstract: We study online demand fulfillment in a class of networks with limited flexibility and arbitrary numbers of resources and request types. We show analytically that such a network is both necessary and sufficient to guarantee a performance gap independent of the market size compared with networks with full flexibility, extending the previous literature from the long chains to more general sparse networks. Inspired by the performance bound, we develop simple inventory allocation rules and guidelines for designing such network structures. Numerical experiments including one using some real data from Amazon China are conducted to confirm our findings as well as some of the flexibility principles conjectured in the literature.

Keywords: process flexibility; online retailing; dynamic resource allocation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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