Affiliations: [a] School of Electrical Engineering and Computer Science, Washington State University, Richland, WA, USA | [b] College of Business and Economics, University of Idaho, Moscow, ID, USA
Correspondence:
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Corresponding author: Dr. Li Tan, School of Electrical Engineering and Computer Science, Washington State University, Richland, WA 99354, USA. E-mail: [email protected].
Abstract: Supply chain strategy has become an important factor that dictates the successes of companies in today's competitive world. Nowadays more and more companies are tapping into the mergers and acquisitions in hope of getting the synergistic gain in supply chain consolidation. In this paper we use a model-checking-based approach to study the impact of different consolidation strategies on risks in supply chains and compare their capacity of risk reduction. We model stochastic behaviors of supply chains using an extension of Markov Decision Processes and translate the goal of risk analysis into a temporal logic. We then apply probabilistic model checking to analyzing risks inherent in a stochastic supply chain model. In our computational study, we consider three different consolidation strategies initially modeled in [18] and compare their capability of risk reduction in a generic three-echelon supply chain network. Our results reveal some key factors that improve the benefit of supply chain consolidation on risk reduction.
