Abstract
We study the impact of three well-known inventory allocation mechanisms, including proportional, linear, and uniform, on the ordering behavior of retailers serviced from a central distribution center. Based on the allocation mechanism, retailers may have an incentive to adjust (either inflate or deflate) their orders to gain a more favorable allocation, a behavior that may reduce allocation efficiency from a system perspective. We find that while all three mechanisms are centrally optimal under common knowledge of local demands, only the uniform allocation incentivizes retailers to set orders truthfully. Consistent with theory, our experimental results show that using proportional or linear allocation results in larger and more frequent order adjustments, with the degree of strategic ordering being largest under the linear mechanism. Across all mechanisms, order adjustments decrease both allocation efficiency and local retail profits. While uniform allocation results in smaller and less frequent adjustments overall, it may not be feasible to implement in more general settings. Hence, we propose and test a new mechanism, tailored uniform, that leverages the uniform principle while overcoming some practical limitations. It provides more flexibility by allowing for differences in the allocated quantities among retailers, while still providing no incentive for order manipulation. The tailored uniform mechanism performs similarly to uniform in terms of order adjustments, and further increases allocation efficiency when retailers have heterogeneous demands.
Original language | English |
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Pages (from-to) | 422-441 |
Journal | Production and Operations Management |
Volume | 31 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2022 |
Keywords
- Ordering behavior
- Inventory allocation
- Behavioral operations
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Replication Data for: Ordering Behavior and the Impact of Allocation Mechanisms in an Integrated Distribution System
Spiliotopoulou, E. (Creator), Donohue, K. (Creator) & Çagri Gürbüz, M. (Creator), DataverseNL, 16 Mar 2022
DOI: 10.34894/gycfqh, https://dataverse.nl/citation?persistentId=doi:10.34894/GYCFQH
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