Evaluation mechanism for structural robustness of supply chain considering disruption propagation

Abstract

This paper aims to develop a novel evaluation mechanism for assessing the structural robustness of a supply chain considering disruption propagation. Disruption propagation means that the impact of risks propagates to the whole supply chain along the connected structure. Based on the propagation model, a structural robustness evaluation mechanism is devised by integrating two quantitative metrics, average path length and in degree-out degree. To validate the proposed mechanism, the result of the quantitative assessment of the structural robustness on random networks is compared with the probability of network disruption due to the random risk. From the results of the statistical verifications and sensitivity analysis, it can be said that the proposed mechanism is better at explaining the robustness of a supply chain. In other words, all components of a network, such as nodes and arcs, and their relationships should be considered altogether, in order to more accurately measure the robustness. It may be possible to apply the proposed mechanism to the very first step of designing the supply chain. Especially, in the case of it being hard to redesign a supply chain structure after practically launching and operating the designed network, the proposed mechanism may be utilised to verify whether the planned supply chain is robust to risks or not.

A relational view of climate adaptation in the private sector: How do value chain interactions shape business perceptions of climate risk and adaptive behaviours?

“…Had there been alternative (or stock-piled) grain supplies, or a substitute value chain in the domestic or international market to supply inputs for feed pellets, the sensitivity of the tilapia value chain and of Aquaculture Jamaica Limited would have been lower. This case illustrates how network structural attributes (such as network connectivity and network redundancies) may influence on risk propagation mechanisms and on the ability of actors and entire value chains to respond to systemic risks -a factor that only very recent explorations have tackled (see Han & Shin, 2016;Helbing, 2013;Ledwoch, Yasarcan, & Brintrup, 2018;Otto et al, 2017;Scheibe & Blackhurst, 2018).…”

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

“…Research focus Disruptions and the resulting ripple effect cause SC structural changes, also referred to as SC structural dynamics (Ivanov, Sokolov, and Kaeschel 2010;Ivanov 2018a). Structural SC properties have been recognised to have a crucial impact on the ripple effect and SC robustness and resilience (Xia et al 2004;Tomlin 2006;Nair and Vidal 2011;Hu, Gurnani, and Wang 2013;Basole and Bellamy 2014;Ivanov, Sokolov, and Dolgui 2014;Ambulkar, Blackhurst, and Grawe 2015;Bode and Wagner 2015;Kim, Chen, and Linderman 2015;Gunasekaran, Subramanian, and Rahman 2015;Kamalahmadi and Mellat-Parast 2016;Han and Shin 2016;Sokolov et al 2016;Tang et al 2016;Chen, Xi, and Jing 2017;Jain et al 2017;Scheibe and Blackhurst 2018;Pavlov et al 2018;Ojha et al 2018).…”

“…Networking complexity in the SC frequently leads to disruption propagations through multiple stages: this is called the ripple effect (Liberatore, Scaparra, and Daskin 2012;Ivanov, Sokolov, and Dolgui 2014;Ivanov, Sokolov, and Pavlov 2014;Han and Shin 2016;Ivanov 2017a;Akkermans and van Wassenhove 2018;Dunke et al 2018;Scheibe and Blackhurst 2018;Dolgui, Ivanov, and Sokolov 2018). Uncertainty prediction and SC restoration or reconfiguration are two common research topics in SC disruption risk management that aim at mitigating the adverse effects of disruptions on SC financial and operational performance (Blackhurst et al 2005;Tang 2006aTang , 2006bCraighead et al 2007;Handfield and McCormack 2008;Blackhurst, Dunn, and Craighead 2011;Gurnani, Mehrotra, and Ray 2012;Sodhi, Son, and Tang 2012;Sodhi, Son, and Tang 2012;Habermann, Blackhurst, and Metcalf 2015;Yildiz et al 2016;Heckmann 2016;Khojasteh 2018;Ivanov 2018a).…”

“…The performance of such a SC is insensitive to the negative impacts of disruptions Han and Shin 2016;Chen, Xi, and Jing 2017). Robustness is typically guaranteed by some redundancy such as structural diversification, flexible response options, and system adaptation condition improvement.…”

Optimal production and inventory decisions under demand and production disruptions

“…Supply chain disruptions are inevitable and take place unpredictably in real life (Ivanov, Sokolov, Dolgui 2014;Han and Shin 2015). They cause huge economic loss to supply chain and to the society (Gupta, He, and Sethi 2015;Yang and Babich 2015).…”