The complexity measurement and evolution analysis of supply chain network under disruption risks

Wang, Hua; Gu, Tao; Jin, Maozhu; Zhao, Rong; Wang, Guanxiang

doi:10.1016/j.chaos.2018.09.018

Cited by 33 publications

(16 citation statements)

References 20 publications

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“…(Chand et al, 2020 ; Wang et al, 2018 ) studied the supply chain complexity drivers. Other authors like (Serdarasan, 2013 ) also talked about the supply chain complexity as one of the important drivers of supply chain vulnerability.…”

Section: Research Framework For Supply Chain Vulnerability Modelmentioning

confidence: 99%

Supply chain vulnerability assessment for manufacturing industry

et al. 2021

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In today's business, environment natural and manmade disasters like recent event (Covid 19) have increased the attention of practitioners and researchers to Supply chain vulnerability. Purpose of this paper is to investigate and prioritize the factors that are responsible for supply chain vulnerability. Extant literature review and interviews with the experts helped to extract 26 supply chain vulnerability factors. Further, the relative criticality of vulnerability factors is assessed by analytical hierarchy process (AHP). Critical part supplier; location of supplier; long supply chain lead times; Fixing process owners and mis-aligned incentives in supply chain are identified as the most critical factors among twenty-six vulnerability factors. Research concludes that not only long and complex supply chain but supply chain practices adopted by firms also increase supply chain vulnerability. Relative assessment of vulnerability factors enables professionals to take appropriate mitigation strategies to make the supply chains more robust. This research adds in building a model for vulnerability factors that are internal to supply chain & controllable.

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Section: Research Framework For Supply Chain Vulnerability Modelmentioning

confidence: 99%

Supply chain vulnerability assessment for manufacturing industry

et al. 2021

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“…Behdani et al [9]. Wang et al [10] proposed a quantitative method based on design structure matrix to study the impact of structural complexity on SC disruption. Wang et al [11] developed a petri net model to evaluate the impact of disruption on SCs service level performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach

Hosseini

Ivanov

Dolgui

2019

International Journal of Production Research

149

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The ripple effect could occur when a disruption in supplier base cannot be localised and its consequences propagate downstream the supply chain (SC) adversely affecting the performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in single-echelon-single-event setting is desirable and indeed critical for some firms, modeling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by consideration of both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model based on integrating Discrete-Time Markov Chain (DTMC) and Dynamic Bayesian Network (DBN) model to quantify the ripple effect. We use DTMC to model the recovery and vulnerability of suppliers. The proposed DTMC model is then equalized with a DBN model in order to simulate the propagation behavior of supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of supplier disruption on manufacturer in terms of total expected utility and service level. The ripple effect measure constructed is examined and tested using two case-studies. The findings suggest that our model can be of value in revealing latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritizing the contingency and recovery policies.

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“…A huge body of researches have been done on SC disruption risk management problems considering ripple effect (Ivanov, Pavlov, and Sokolov 2014a;Ivanov et al 2015;Sokolov et al 2016;Kamalahmadi and Parast 2017;Ivanov 2017;Sawik 2017;Ivanov et al 2017aIvanov et al , 2017bWang et al 2018;Hosseini, Ivanov, and Dolgui 2019a;Ivanov, Tsipoulanidis, and Schönberger 2019c;Gupta and Ivanov 2019;He et al 2019;Chen, Das, and Ivanov 2019;Ivanov, Dolgui, and Sokolov 2019b;Kinra et al 2019). The related literature review consists of two parts: (1) SC disruption risk management; and (2) the applications of probability intervals which can portray imperfect probability information.…”

Section: Literature Reviewmentioning

confidence: 99%