A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks

Abstract

Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that need to be considered and the interdependencies that can exist between multiple partners across the globe.In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts' knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making.

Roles of inventory and reserve capacity in mitigating supply chain disruption risk

“…The impact on the supply chain networks of supply disruptions is widely studied by different scholars (Schmitt et al, 2015;Liberatore et al, 2012;Berger et al, 2004;Ruiz-Torres and Mahmoodi, 2007;Li et al, 2010;Yu et al, 2009;Sarkar and Kumar, 2015;Niknejad and Petrovic, 2016). Schmitt et al (2015) analyze the role of inventory to safeguard against supply chain disruptions in a multi-location supply chain.…”

“…They find that supply chain disruptions may cause higher order variability compared to the base case without disruptions. Niknejad and Petrovic (2016) propose a risk evaluation method for global production networks that is based on a dynamic fuzzy model. However, this research stream lacks the optimality structures for the joint use of RMI and reserve capacity.…”

Analysis of a Nuclear Power Plant Failure Using Temporal Networks

“…The classic IIM is a static model and so it is not able to manage dynamic dependencies. Many authors overcame this limitation with extensions of the original IIM [11][12][13], while in the proposed approach the IIM has been modified using temporal networks. In literature, there are several studies on temporal networks, which are summarized in Holme and Saramäky's work [14].…”

Predicting the behavior of attackers and the consequences of attacks against cyber‐physical systems

“…Niknejad et al [32] have proposed a risk evaluation method for global production networks based on fuzzy theory. They have presented a fuzzy multi-criteria approach to identify inter-dependencies between nodes in these networks by using experts' knowledge.…”