Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization

Zhao, Kang; Scheibe, Kevin P.; Blackhurst, Jennifer; Kumar, Akhil

doi:10.1109/tem.2018.2808331

Cited by 55 publications

(30 citation statements)

References 56 publications

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“…Supply network disruptions are usually defined as unexpected events that hamper the functioning of a supply network [19,20]. They can generally be divided into two types: random disruptions and target disruptions [21][22][23]. Random disruptions correspond to unintentional incidents, like natural disasters, accidents, public health events, etc.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

2021

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With the increasing reliance on global sourcing and the growth in the likelihood of disruptive incidents, today’s supply networks are more prone to unexpected natural and man-made disruptive events. In order to alleviate the losses caused by these disruptive events, when a large-scale event disrupts multiple suppliers simultaneously, a single or several critical suppliers should be selected from the disrupted ones to assist them to recover their production as soon as possible. The selection of these recovery suppliers is of great importance in the recovery process of the entire supply network. Thus, this paper proposes a recovery supplier selection method from the view of the supply network structure. Firstly, a tripartite graph-based supply model is proposed to depict a two-stage supply network, which consists of multiple manufacturers and suppliers as well as the diverse product supply-demand interdependence connecting them. To measure the impacts caused by supplier disruptions and to evaluate the effectiveness of recovery supplier decisions, two supply network performance metrics reflecting product supply availability are also given. Then, the recovery supplier selection problem is described as a combinatorial optimization problem. To solve this problem, a heuristic algorithm, with enhanced variable neighborhood search (EVNS) is designed based on the general framework of a variable neighborhood search. Finally, experiments based on a real-world supply network are conducted. The experimental results indicate that the proposed method is applicable and effective.

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Section: Related Workmentioning

confidence: 99%

Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

2021

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“…Some follow on studies attempted to optimise these features for robustness (e.g. Brintrup and Puchkova 2018;Zhao et al 2018).…”

Section: Relationship Between Topology and Robustness And Resiliencementioning

confidence: 99%

The relationship between nested patterns and the ripple effect in complex supply networks

Chauhan

Perera

Brintrup

2020

International Journal of Production Research

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Supply networks (SNs) play a vital role in fuelling trade and economic growth. Due to their interconnectedness, firm-level disruptions can cause perturbations to ripple through SNs, magnifying initial impact. Contemporary research on ripple effects focussed on understanding various structural features of SNs to predict and control disruption propagation. Our work adds to this body of knowledge by analysing an intriguing topological property that emerges in SNs: 'nestedness', which is defined as a pattern of organisation where products that are supplied by specialist suppliers are a subset of products that are supplied by generalist suppliers. In other words, generalists are also specialists. While previous research examined the emergence of nestedness and its possible reasons, its relationship to SN resilience remained unknown. Here, we develop a cascade model by bringing together the product-supplier-buyer structure; which provides us with fine-grained information on SN dependencies. We simulate disruptions in nested and non-nested organisations of the global automotive SN, and find that nested organisations are significantly more robust to random disruptions but vulnerable to hub disruptions under cascade conditions. However, nested structures are not as resilient; as they do not benefit from a response strategy where buyers seek alternative suppliers; because alternative suppliers do not exist. On the other hand, randomly connected SNs are vulnerable to cascades but can allow network reconfiguration.

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“…The inability to deliver products or materials to those who need them is a failure, which will decrease the performance of the supply network [37]. Based on this consideration, Zhao et al propose supply availability rate which is the percentage of demanders that have access to suppliers in the network [24][25][26]. The expression of supply availability rate is presented as (1).…”

Section: B Performance Metricsmentioning

confidence: 99%

“…In addition, the adaptive behaviors of manufacturers, such as purchasing product from alternative suppliers, are also neglected. Such modeling method is used in many previous studies [20,24,25]. As presented in Fig.…”

Section: B Performance Metricsmentioning

confidence: 99%

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Memetic Algorithm With Local Neighborhood Search for Bottleneck Supplier Identification in Supply Networks

2020

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With the development of lean manufacturing and economic globalization, supply networks increasingly become complex and large-scale, within which thousands of firms inter-depend with each other. Due to these increasing inter-dependencies, disruption of a quite few critical suppliers, namely bottleneck suppliers, can induce high loss to a supply network and even make the whole network dysfunction. Identification of bottleneck suppliers is significantly important for supply network risk management. Thus, in this paper, a method based on a memetic algorithm with local neighborhood search (MALNS) is proposed to identify bottleneck suppliers in a two-stage supply network. Firstly, a model based on multipartite network is designed to describe the product supply-demand relations between multiple manufacturers and suppliers, which considers the different roles of manufacturer and supplier and differentiates the products that suppliers supply. To assess the loss caused by supplier disruptions, two performance metrics of supply networks, average product availability rate and manufacturer functioning rate, are presented. Then, a MALNS-based method is proposed to identify bottleneck suppliers, i.e., suppliers whose disruption will decrease both performance metrics most greatly. Finally, a case study based on a real automobile supply network is presented to validate the applicability and effectiveness of the proposed method.

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Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization

Cited by 55 publications

References 56 publications

Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

The relationship between nested patterns and the ripple effect in complex supply networks

Memetic Algorithm With Local Neighborhood Search for Bottleneck Supplier Identification in Supply Networks

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