Optimal decisions and comparison of VMI and CPFR under price-sensitive uncertain demand

Yasaman Kazemi, Jun Zhang


Purpose: The purpose of this study is to compare the performance of two advanced supply chain coordination mechanisms, Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and Replenishment (CPFR), under a price-sensitive uncertain demand environment, and to make the optimal decisions on retail price and order quantity for both mechanisms.

Design/ methodology/ approach: Analytical models are first applied to formulate a profit maximization problem; furthermore, by applying simulation optimization solution procedures, the optimal decisions and performance comparisons are accomplished.

Findings: The results of the case study supported the widely held view that more advanced coordination mechanisms yield greater supply chain profit than less advanced ones. Information sharing does not only increase the supply chain profit, but also is required for the coordination mechanisms to achieve improved performance.

Research limitations/implications: This study considers a single vendor and a single retailer in order to simplify the supply chain structure for modeling.

Practical implications: Knowledge obtained from this study about the conditions appropriate for each specific coordination mechanism and the exact functions of coordination programs is critical to managerial decisions for industry practitioners who may apply the coordination mechanisms considered.

Originality/value: This study includes the production cost in Economic Order Quantity (EOQ) equations and combines it with price-sensitive demand under stochastic settings while comparing VMI and CPFR supply chain mechanisms and maximizing the total profit. Although many studies have worked on information sharing within the supply chain, determining the performance measures when the demand is price-sensitive and stochastic was not reported by researchers in the past literature.


price-sensitive demand, VMI, CPFR, supply chain profit, simulation optimization, optimal order quantity, optimal retail price

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DOI: https://doi.org/10.3926/jiem.559

Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2024

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience