عنوان مقاله [English]
The risk thematic is not a new concept but a recent and growing subject in supply chain management. Global competition, the increasing complexity of the supply chain, the continuous search for competitive advantage and the use of global suppliers have all motivated the study of risk management in the supply chain. Supply chain risks can arise from multiple sources, including political events, demand fluctuation, technological changes, financial instability and
natural disasters, etc. To be able to handle these risks, Supply Chain Risk Management (SCRM) is needed, and specific responses and strategies for the management of risk are required. Supply Chain Risk Management plays a major
role in successfully managing business processes in a proactive manner.
The general trend towards focusing more on core competencies has forced companies to use outsourcing strategies and has led to the appearance of the supply chain. Also, due to the rapid advancement of technology, the basic
supply chain is rapidly evolving into what is known as a Supply Network. The Supply Network is also faced with these risks, so it requires specific and adequate responses such as techniques, attitude and strategies for their
management. In this article, we focus on supply network risk management and propose a fuzzy mixed-integer linear programming model for designing a supply network, including selection of suppliers, manufacturers and distribution
centers among potential choices and the determination of material flow between them. This is done by considering risks in different layers of the network, such as the operational risk of suppliers, the operational and financial risks
of manufacturers and the disruption risk of distribution centers. Value at risk (VaR), generalized extreme value theory (EVT) and cash flow at risk (CFaR) methods are used to present a model for quantification of these risks, and the
fuzzy set theory is used to represent the uncertainty of parameters. Finally, a numerical example is presented to show the application of this model and some computational results are reported.