Optimal decisions and comparison of VMI and CPFR under price-sensitive uncertain demand
Abstract: 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.
Keywords: price-sensitive
demand, VMI, CPFR, supply chain profit, simulation optimization, optimal order
quantity, optimal retail price
Author: Yasaman Kazemi, Jun
Zhang
Journal Code: jptindustrigg130035
