Supply Chain Disruption Risk Management:Modeling Risk Mitigation Strategies

It is well established that firms are exposed to the risk of supply chain disruptions. Many firms build risk mitigation strategies in order to increase the resilience of their supply chains. In this dissertation we present three essays that study operational risk mitigation levers such as inventory, reserve capacity and dual sourcing. The first essay outlines an application in the pharmaceutical industry where the risk mitigation levers inventory, dual sourcing and agility capacity are analyzed. We study the relationship between these three levers by modeling a drug manufacturing firm that is exposed to disruptions in its supply chain. The firm determines optimal inventory levels for assumed dual sourcing and agility capacity. We quantify the decrease in inventory levels in the presence of dual sourcing and agility capacity. Furthermore, using an example, we analyze inventory, dual sourcing and agility capacity decisions jointly. It turns out that inventory and agility capacity can be substitutes as long as no dual source is available. Once the dual source is available, agility capacity and dual sourcing appear to be substitutes. We further show that for long disruption times, the optimal dual source production rate may decrease in the disruption time. Within our modeling framework, we introduce an operational metric as a measure for resilience under deterministic demand. In the second essay we study the joint role of inventory and reserve capacity in mitigating supply disruptions. A reserve capacity can be used for production in a reactive fashion when a disruption occurs. We first determine optimal inventory levels and reserve capacity production rates jointly under stochastic customer demand. This allows us to fully characterize three main risk mitigation strategies: inventory strategy, reserve capacity strategy, and mixed strategy. Furthermore, we provide structural insights of optimal risk mitigation strategies. We then combine our analytical results with the well-established (Q,R) policy to perform numerical experiments using Conditional Value at Risk (CVaR) as a resilience measure. Our results suggest that the resilience measure depends on both the reorder point R and the batch size Q. Comparing the resilience measure with the service level, we find that a high service level and high resilience can be conflicting objectives. The third essay is concerned with the role of inventory in mitigating disruptions in two-echelon supply chains under stochastic demand. The research problem is to determine the optimal backup inventories for a serial, assembly and distribution supply chain respectively. For the three supply chain types, disruptions at each production site are described by an infinite-state discrete-time Markov process. We derive structural insights on the optimal inventory levels for the serial supply chain. For the assembly and distribution supply chain we show that under mild assumptions an early commitment to finished goods inventories is optimal. This finding is different from optimal safety inventory policies where often a delayed differentiation in assembly supply chains or risk pooling in distribution supply chains is optimal