Global Supply Chain and Operations Management

Abstract

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Literature review on disruption recovery in the supply chain

“…Simulation can be considered as a methodology to further enhance the optimization results or it can be even used as simulation-based optimization technique (Gao and Chen 2017). Simulation allows a dynamic consideration of randomness in disruption and recovery policies, real-time analysis, real problem complexity, inventory control policies, dynamic recovery policies, gradual capacity degradation and recovery, impact of changes in sourcing, transportation and production policies on the ripple effect and operational parameter dynamics in time, multiple performance impact dimensions including financial, service level, and operational performance in time (Ivanov 2017).…”

“…. (2010),Schmitt and Singh (2012),Li et al (2013),Ivanov et al ( , 2014b,Sawik (2016),Ivanov et al (2016a,b),Ivanov (2017),Rezapour et al (2017) Supply disruptionsVahdani et al (2011), Hu et al (2013), Iakovou et al (2010), Shao and Dong (2012), Paul et al (2014), Hasani, and Khosrojerdi (2016) Azaron et al (2008), Ivanov et al (2013, 2014b), Kim and Tomlin (2013), Ivanov et al (2016a,b), Ivanov (2017), Rezapour et al (2017) Hou et al (2010), Lim et al (2010), Yang et al (2012), Sawik (2013), Schmitt and Singh (2012), Shao and Dong (2012), Ivanov et al (2013, 2014b), Li et al (2013), Gupta and Sethi (2015), Ivanov et al (2016a,b), Ivanov (2017), Rezapour et al (2017and Singh (2012), Vahdani et al (2011), Ivanov et al (2013, 2014a) Hsu and Li (2011), Hu et al (2013), Shao and Dong (2012), Gupta and Sethi (2015) D. Ivanov, A. Dolgui, B. Sokolov, M. Ivanova. Literature review on disruption recovery in the supply chain…”

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).…”

“…Supply chain (SC) design and planning seek to find a structural-process-parametrical form of a value-adding network and its operation in time subject to some goal criteria, e.g. costs minimisation (Ivanov, Tsipoulanidis, and Schönberger 2017;Calleja et al 2018). SC design is typically related to structural SC formation, and SC planning is concerned with process deployment.…”

Prioritization of Petroleum Supply Chains’ Disruption Management Strategies Using Combined Framework of BSC Approach, Fuzzy Ahp and Fuzzy Choquet Integral Operator

“…The ripple effect determines the impact of a disruption on supply chain performance and the disruption-based domain of changes in the supply chain structures. Managing the ripple effect is closely related to designing and planning robust resilient supply chains (Ivanov et al 2016b). In other words, risk is the relationship between some possible negative outcomes, while the disruption is the corresponding probabilities for each issue.…”