Abstract
Supply networks are complex and suffer always from various risks. An effective supply chain management requires suitable strategies to mitigate them. In previous literature, there has been a range of research into risk in firms but little in supply networks. This can be explained due to the huge number of risk variables and their direct and indirect interrelations that may suffer all supply chain partners (firms). Therefore, for better risk mitigation, a risk prioritization step is vital. To this end, the purpose of this paper is to propose a new integrated approach based on two structural modeling tools. Firstly, interpretive structural modeling has been used to present a hierarchical model showing the interrelationships between the risk sources. Secondly, MICMAC analysis has been used to quantify and classify the risk variables based on their mutual influence and dependence. The objective is to ascertain the key risk variables and theirs relationships. These prioritized risk variables provide a useful tool to supply network managers to focus on those key variables that are most essential for effective risk management strategies. A real case study in food industry is provided in order to illustrate the application of the proposed approach. The findings may be useful to the practitioners in risk management and may also interest academicians, since the method used here can be applied in other areas of industrial management as well.