Fee-for-service (FFS) contracts, first introduced in 2004, dramatically changed the way the pharmaceutical distribution supply chains are designed, managed, and operated. Investment buying (IB), forward buying in anticipation of drug price increases, used to be the way the distributors made most of their profits. FFS contracts limit the amount of inventory distributors can carry at any time (by imposing an inventory cap) and require inventory information sharing from the distributors to the manufacturers while compensating the distributors with a per-unit fee. In spite of its widespread popularity, the FFS model has never been rigorously analyzed or its effectiveness carefully tabulated. In this paper, we formulate the multiperiod stochastic inventory problems faced by the manufacturer and the distributor under the FFS and IB models, derive their optimal policies, and develop procedures to compute the policy parameters. We show that FFS contracts can improve the total supply chain profit-the manufacturer and distributor are now able to share a larger pie. Thus, there exists a range of the per-unit fees that leads to Pareto improvement. Simulation results show that such improvement is approximately 107% on average, and as much as 505%, and the improvement increases as the inventory cap decreases. Determining the Pareto-improving per-unit fees is a source of contention in FFS contract negotiation, and we propose a simple yet effective heuristic for computing them. Furthermore, supply chain transparency facilitated by the FFS contracts can significantly reduce the manufacturer's supply-demand mismatch costs (by approximately 3063% on average and as much as 13001%) and we show that the manufacturer should take advantage of this transparency especially when the inventory cap and drug price increase are high and demand variance is low. We believe that these results have the potential to improve the efficiency of pharmaceutical distribution supply chains, thus reducing the healthcare costs that are such a big burden on the U.S. economy.
All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research