Mitigating supply-demand mismatch: The relationship between inventory sharing and demand learning

Liqun Wei, Wanying Wei, Yunchuan Liu, Jianxiong Zhang, Xuanhua Xu

Research output: Contribution to journalArticlepeer-review


By mitigating supply-demand mismatch through advanced forecast technology, demand learning has attracted widespread attention and is increasingly adopted in conjunction with inventory sharing. However, this combination is not necessarily efficient given the unclear relationship between the two strategies. Therefore, crucially, this article investigates the strategic relationship between inventory sharing and demand learning, that is, when and whether they are substitutes or complements. We develop a theoretical game model consisting of two firms facing uncertain demand, and both of them need to determine their production quantity before demand is realized. Contrary to the intuition that demand learning is a substitute for inventory sharing, we find that these two strategies can be complements when the production cost is relatively low or high. Moreover, when forecast accuracy is relatively low, the substitutability will be weakened while the complementarity will be enhanced as forecast accuracy increases. Additionally, the substitutability first weakly decreases and then weakly increases, while the complementarity first weakly increases and then weakly decreases with the transfer price.

Original languageEnglish (US)
JournalDecision Sciences
StateAccepted/In press - 2023
Externally publishedYes


  • demand learning
  • inventory sharing
  • supply-demand mismatch

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation


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