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

Abstract

Purpose: The purpose of this study is to make optimal decisions on retail price and order quantity for two advanced supply chain coordination mechanisms, Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and Replenishment (CPFR), under a pricesensitive uncertain demand environment, and to compare the performance of VMI and CPFR. Sensitivity analysis is also conducted to gain managerial insight. 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. Sensitivity analysis is also conducted to show how production cost and inventory holding cost affect optimal decisions and the total profits in VMI and CPFR. Findings: The optimal decisions and expected total profits are impacted by the demand patterns, production cost, inventory holding cost, and internal transfer price. In addition, the results of the case study reveal that CPFR outperforms VMI in terms of higher expected total profit and lower retail price. Research limitations/implications: This study only considers a single vendor and a single retailer in the supply chain structure. Practical implications: Knowledge obtained from this study about the performance of each coordination mechanism and decision making under uncertainty are critical to managers and industry practitioners who may apply the coordination mechanisms considered. Originality/value: This study includes the production cost in the mathematical model and combines it with price-sensitive demand under stochastic settings to maximize the total profit. Many studies have worked on information sharing within the supply chain; however, determining the optimal retail price and order quantity and comparing the performance of VMI and CPFR when the demand is price-sensitive and stochastic were not reported by the past literature.