Risk Management in Biopharmaceutical Supply Chains
- Author(s): Ma, Yao
- Advisor(s): Leachman, Robert
- et al.
Biopharmaceutical supply chains present considerable complexity issue for the formulation of optimal plans due to significant uncertainty in the supply chain. The primary goal of biopharmaceutical supply chain planning is to provide reliable supply to patients while coping with various supply chain risks. In chapter 1 first I discuss the key elements and basic characteristics of the biopharmaceutical supply chain . Then I present the major challenges in biopharmaceutical supply chain planning and divide them into two main categories: deterministic complexity problems and stochastic uncertainty problems. In the end of chapter 1 I briefly discuss the most recent work in solving the deterministic complexity problems.
The planning of biopharmaceutical supply chain operations faces risks from various sources. These include customer demand fluctuations, regulatory requirement changes, long quality assurance cycle time, etc. In chapter 2, I review the major risks in biopharmaceutical supply chain and current practice to hedge against these risks. The impact of these risks is evaluated in terms of a cumulative supply and demand perspective. Furthermore I analyze two main risk mitigation tools in supply chain risk management: safety stock and safety time. Then I use simulation to show that safety stock is a preferred approach for risk mitigation in biopharmaceutical supply chains.
In chapter 3, first I focus on stochastic lead time risk and discuss conventional as well as crossover based approaches for safety stock planning. Also I demonstrate the benefit of a proposed approach to safety stock planning with numerical examples and simulation. Then the proposed model is extended to consider batch rejection risk and excursion risk. Batch rejection risk represents the possibility that a batch fails to meet regulatory requirements. The excursion risk reflects potential major disruptions in biopharmaceutical supply chain. Examples of such events include contamination of the production facility, earthquake, etc. These three risks cover most of the major uncertainties in biopharmaceutical supply chain. The model determines the necessary safety stock level to prevent stock outs given these risks as a function of the target service level.
In chapter 4, I discuss the implementation of the proposed model in a multi-echelon biopharmaceutical supply chain. Also I use sensitivity analysis to evaluate the impact of improving key supply chain parameters. Then the model is applied to determine the stock level supplying a regional market of an actual biopharmaceutical supply chain and significant potential savings are demonstrated. In the end I identify a few important potential research problems in biopharmaceutical supply chain management.