Coordination of vendor managed inventory supply chain with price-sensitive demand under consumer balking behaviour

Coordination of vendor managed inventory supply chain with price-sensitive demand under consumer balking behaviour

Chongfeng Lan 

School of Economics, Fuyang Normal University, Fuyang Anhui 236037, China

Anhui Provincial Key Laboratory of Regional Logistics Planning and Modern Logistics Engineering, Fuyang 236037, China

Corresponding Author Email: 
lchfym@sina.com
Page: 
125-140
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DOI: 
https://doi.org/10.3166/JESA.51.125-140
Received: 
| |
Accepted: 
| | Citation

OPEN ACCESS

Abstract: 

The vendor managed inventory (VMI) problem has not been widely studied under consumer balking behaviour (CBB), a ubiquitous phenomenon in economic life. To make up for this gap, the purpose of this paper innovatively introduces the CBB to the VMI supply chain whose demand depends on the retail price, explores the effects of the CBB on the coordination of the VMI supply chain with price-sensitive demand, and discusses the impacts on the CBB on the optimal decision and expected revenue of the supply chain. The research results show that it is impossible to coordinate the VMI supply chain with price-sensitive demand under the CBB through the wholesale price contract; however, the wholesale price contract can perfectly coordinate this supply chain after allocating the unmarketable cost to the retailer. Next, it is proved that the optimal inventory factor, the optimal retail price and the expected revenue of the VMI supply chain all decreases with the growth in the balking threshold under certain conditions. Finally, the optimal supply volume of the system was proved to be positively correlated with the balking threshold through numerical experiments. The conclusions of this paper enrich the theory of the supply chain coordination under CBB and facilitate its application in real life.

Keywords: 

VMI, CBB, supply chain, retail price, coordination

1. Introduction
2. Problem description and parameters assumption
3. Modelling and analysis
4. Model optimization
5. Numerical experiments
6. Conclusions and future research
Acknowledgements
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