EconPapers    
Economics at your fingertips  
 

Retailer vs. vendor managed inventory with considering stochastic learning effect

Qian Wei, Jianxiong Zhang, Guowei Zhu, Rui Dai and Shichen Zhang

Journal of the Operational Research Society, 2020, vol. 71, issue 4, 628-646

Abstract: Extending the research on the impact of learning effect on inventory management is of particular importance, this paper studies two different inventory management models with considering stochastic learning effect, one is retailer-managed inventory (RMI) scenario, and another is vendor-managed inventory (VMI) scenario. We find that inventory exists in equilibrium provided that the holding cost is under a respective threshold both in the RMI and VMI scenarios, also, the threshold in the RMI scenario is significantly larger than that in the VMI scenario. Moreover, the RMI scenario is Pareto dominant over the VMI scenario except for a very large holding cost, and the advantage in enhancing profit is highlighted in the RMI scenario as the variability of the learning rate increases. Furthermore, the traditional double marginalization effect is weakened by a large variability in the RMI scenario while intensified in the VMI scenario. The results obtained in this paper can provide guidance for the inventory management with considering stochastic learning effect.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1581407 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:71:y:2020:i:4:p:628-646

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2019.1581407

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tjorxx:v:71:y:2020:i:4:p:628-646