Sourcing strategies for a capacitated firm subject to supply and demand uncertainty. (June 2018)

• Record Type: Journal Article
• Authors: Freeman, Nickolas; Mittenthal, John; Keskin, Burcu; Melouk, Sharif
• Is Part Of: Omega. Volume 77(2018)
• Page Start: 127
• Page End: 142
• Publication Date: 2018-06
• Subjects: Supply chain risk management -- Downward substitution -- Concurrent sourcing -- Stochastic programming; Management -- Periodicals; 658.4005
• DOI: 10.1016/j.omega.2017.06.004
• Languages: English
• ISSNs: 0305-0483

Abstract

Highlights: We consider the simultaneous use of multi-sourcing, in-house production, and downward substitution for mitigating the supply and demand risk faced by a capacitated manufacturer. We develop a stochastic programming formulation that determines a sourcing strategy the maximizes manufacturers expected profit. A large screening experiment reveals that ve of the 63 possible sourcing strategies occur as the optimal sourcing strategy in nearly 75% of problem instances. Additional experiments provide insight into the impact of limited capacity, the value of each available sourcing option, and the evolution of the optimal sourcing strategy as key problem parameters change. Abstract: We study sourcing strategies for a capacitated manufacturer subject to supply and demand uncertainty. The manufacturer produces two products: low- and high-quality versions of the same product that differ in only one critical component. For the low-quality product, one supplier of the critical component is unreliable, failing to deliver ordered components with some positive probability. To mitigate the risk associated with the unreliable supplier, the manufacturer may: i ) order from a perfectly reliable, but expensive secondary supplier (i.e., dual sourcing); ii ) produce some or all of the component in-house (i.e., concurrent sourcing); or iii ) substitute a higher quality component (i.e., downward substitution). All production activities share a limited capacity resource. We develop a stochastic programming formulation in a single period setting to study the optimal combination of these sourcing tactics, which we refer to as sourcing strategies, under a variety of conditions. An extensive screening experiment reveals that five of the 63 possible sourcing strategies occur as the optimal sourcing strategy in nearly 75% of problem instances. Additional experiments provide insight into the impact of limited capacity, the value of each available sourcing option, and the evolution of the optimal sourcing strategy as key problem parameters change. Most interestingly, our experiments reveal that the ability to produce a component in-house is a highly effective means for mitigating supply and demand uncertainty in a wide range of problem environments.