Abstract

In a supply chain, demands very often don't match with supplies due to the insufficient information between the retailers and wholesalers. To overcome this inefficiency, coordination mechanisms such as Continuous Replenishment Planning (CRP) and Vendor Managed Inventory (VMI) are usually adopted. In this research, decision support models based on mathematical programming techniques and built on the coordination mechanisms of CRP and VMI are developed. With the developed models, the optimal values of decision factors can be decided with the objective of minimizing the total supply chain cost. To verify the models' performance under different scenarios, experimental design and Monte Carlo simulations are conducted, and results are quantified using Response Surface Methodology. The results of the study show that both models result in lower cost and lower order variability in all scenarios than a reference case of no coordination mechanism. In addition, the cost reductions are correlated with the scenario factors of demand correlation, demand variability, lead time and service level. The managerial implications are discussed.