Models for supplier selection and risk mitigation: a holistic approach

Abstract

With growing emphasis on supply risk, consideration of risk aspects in supplier selection has become an important issue faced by firms. While current literature has proposed a variety of tools and techniques for effective supplier selection, few approaches, if any, are proposed in incorporating risk mitigation strategies in supplier selection decisions. To this end, this paper fills this gap, by considering a variety of risk factors in supplier selection, which are both quantitative and qualitative in nature, and tests the efficacy of alternative risk mitigation strategies in this context. Moreover, we argue that both upstream and downstream risk mitigation strategies should be used simultaneously rather than focusing on a single type of strategy. We further argue that alignment between upstream and downstream risk mitigation strategies is critical in reducing risk. We utilize multi-objective optimization based simulation in building a decision model in the context of this problem setting. We consider data from an automotive parts manufacturer in demonstrating the application of our approach.

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

“…Research in SC reaction to disturbances is related to the semantic network analysis level with a focus on structural properties, complexity roles, and node/arc criticality . The studies (Basole and Bellamy 2014;Dolgui 2014a, 2014b;Kim, Chen, and Linderman 2015;Brintrup, Wang, and Tiwari 2015;Sawik 2017;Macdonald et al 2018;Yoon et al 2018;Scheibe and Blackhurst 2018;Pavlov et al 2018;Ojha et al 2018;Giannoccaro, Nair, and Choi 2017;Ivanov 2018Ivanov , 2019Dolgui, Ivanov, and Sokolov 2018;Li et al 2019;Pavlov et al 2019b) recognised the structural SC properties as crucial determinant to maintain stability and robustness and to achieve resilience. Another important observation in literature is a linkage of SC complexity and resilience (Blackhurst et al 2005;Nair and Vidal 2011;Bode and Wagner 2015;Dubey et al 2019a;Tan, Cai, and Zhang 2020).…”

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

“…In this section, the ripple effect control framework is presented (Ivanov, Sokolov, and Dolgui 2014b;Ivanov, Sokolov, and Pavlov 2014c;Dolgui, Ivanov, and Sokolov 2018) which will be used in Section 4 to match the digital technologies impacts on different SC disruption risks. Ripple effect analysis in the SC is positioned in literature on SC resilience and disruption management (Blackhurst et al 2005(Blackhurst et al , 2011Simangunsong, Hendry, and Stevenson 2012;Ivanov, Sokolov, and Dolgui 2014b;Ivanov, Sokolov, and Pavlov 2014c;Ho et al 2015;Gunasekaran, Subramanian, and Rahman 2015;Gupta, He, and Sethi 2015;Tukamuhabwa et al 2015;Ivanov et al 2016aIvanov et al , 2016bSnyder et al 2016;Chen, Xi, and Jing 2017;Ivanov 2017a;Jain et al 2017;Yoon et al 2018). In addition, the literature on 'grey swans' is related to the interrelations of disruptive innovations and disruption risk propagation in SCs (Akkermans and van Wassenhove 2018).…”

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

“…A body of literature has been established that examines the impacts of different structural variations on SC performance for various risk attitudes in a decision maker, ranging from risk neutral to risk averse (Ho et al 2015;Rangel, de Oliveira, and Alexandre 2015;Yang and Babich 2015;Snyder et al 2016;Ivanov, Dolgui, et al 2017;Kumar, Basu, and Avittathur 2018;Sawik 2017;Reyes Levalle and Nof 2017;Yoon, Talluri, et al 2018;Carbonara and Pellegrino 2017;Namdar et al 2018). This literature at the structural level targets semantic network analysis in order to identify underlying interdependencies between network graph forms and SC robustness, flexibility, adaptability, and resilience (Basole and Bellamy 2014;Zobel and Khansa 2014;Kim, Chen, and Linderman 2015;Ivanov 2017a;Giannoccaro, Nair, and Choi 2017;Ivanov, Tsipoulanidis, and Schönberger 2017;Ivanov, Pavlov, et al 2017;Dolgui, Ivanov, and Sokolov 2018).…”

“…Like Lim et al (2010) and Peng et al (2011), Sawik's studies used the probabilities of the disruption scenarios instead of standard probability distributions which have a very restrictive application to lowfrequency disruptive events. Along with the study by Yoon, Talluri, et al (2018), Sawik's studies suggested models that integrate supplier selection and risk mitigation strategy selection. An alternative approach was taken in the study by Yu, Li, and Yang (2017) that applies robust stochastic optimisation to SC design.…”