A graph theory-based methodology for vulnerability assessment of supply chains using the life cycle inventory database

Nakatani, Jun; Tahara, Kiyotaka; Nakajima, Kenichi; Daigo, Ichiro; Kurishima, Hideaki; Kudoh, Yasuhiro; Matsubae, Kazuyo; Fukushima, Yasuhiro; Ihara, Tomohiko; Kikuchi, Yasunori; Nishijima, Ayumu; Moriguchi, Yuichi

doi:10.1016/j.omega.2017.03.003

Cited by 38 publications

(27 citation statements)

References 45 publications

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“… I19 Supply chain risk management SC risk management refers to the coordinated approach among the members of a supply chain for identifying and managing supply chain risk in order to reduce supply chain vulnerability Jüttner et al (2003); Munir et al (2020) ; Birkel & Hartmann (2020) ; Roscoe et al (2020) ; Kbah et al (2020) ; Shahbaz et al (2020) ; Baryannis et al (2019) ; Chowdhury et al (2019) ; Snoeck et al 2019 ). Sawik, T. (2019b) ; Mogos et al (2019) ; Stewart & Ivanov (2019) ; Gao et al (2019) ; Shahbaz et al (2019) ; Sawik (2019a) ; Bugert & Lasch (2018) ; Nakatani et al (2018) ; Kumar et al (2018) ; Ledwoch, et al (2018) ; Blackhurst et al (2018) ; Diabat et al (2019) . I20 Supply chain vulnerability SC vulnerability is the susceptibility or exposure to a disruptive event in the supply chain Blackhurst et al (2018) ; Azadegan et al (2020) ; Viljoen & Joubert (2018) ; Nakatani et al (2018) ; Konig & Spinler (2016) ; Chowdhury & Quaddus (2016) ; Chen et al (2015 b); Ethirajan et al (2020) .…”

Section: Resultsmentioning

confidence: 99%

“… Sawik, T. (2019b) ; Mogos et al (2019) ; Stewart & Ivanov (2019) ; Gao et al (2019) ; Shahbaz et al (2019) ; Sawik (2019a) ; Bugert & Lasch (2018) ; Nakatani et al (2018) ; Kumar et al (2018) ; Ledwoch, et al (2018) ; Blackhurst et al (2018) ; Diabat et al (2019) . I20 Supply chain vulnerability SC vulnerability is the susceptibility or exposure to a disruptive event in the supply chain Blackhurst et al (2018) ; Azadegan et al (2020) ; Viljoen & Joubert (2018) ; Nakatani et al (2018) ; Konig & Spinler (2016) ; Chowdhury & Quaddus (2016) ; Chen et al (2015 b); Ethirajan et al (2020) . I21 Sustainability Sustainability in the SC is defined as managing the supply chain functions aligned with the social, environmental, and economic sustainability requirements of the stakeholders to reduce sustainability risks in supply chain and improve market performance Seuring & Müller (2008) ; Sharifi et al (2020) ; Chatterjee & Layton (2020) ; Chen et al (2020) ; Handfield et al (2020) ; Zhu & Krikke (2020) ; Kamble et al (2020) ; Niu et al (2020) ; Quayson et al (2020) ; Ivanov (2020) ; Shareef et al (2020) ; He et al (2020) ; Nayak & Dhaigude (2019) ; Maiyar & Thakkar (2020) .…”

Section: Resultsmentioning

confidence: 99%

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Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

180

119

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Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Bui

Tsai

Tseng

et al. 2021

Sustainable Production and Consumption

180

119

View full text Add to dashboard Cite

show abstract

“…However, this domestic production was sourced entirely from one company that went bankrupt in 2017—subsequent to the 2015 dataset (Cimprich et al., ). For some forms of supply risks, such as earthquake‐related risks (Nakatani et al., ), a facility‐level analysis may be most appropriate. Nonetheless, import tariffs imposed by the United States in June 2018, and the resulting prospects of a global trade war, underscore the relevance of country‐level supply risk factors.…”

Section: Examination Of Methodsmentioning

confidence: 99%

Raw material criticality assessment as a complement to environmental life cycle assessment: Examining methods for product‐level supply risk assessment

Cimprich

Bach

Helbig

et al. 2019

J of Industrial Ecology

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Summary The diversity of raw materials used in modern products, compounded by the risk of supply disruptions—due to uneven geological distribution of resources, along with socioeconomic factors like production concentration and political (in)stability of raw material producing countries—has drawn attention to the subject of raw material “criticality.” In this article, we review the state of the art regarding the integration of criticality assessment, herein termed “product‐level supply risk assessment,” as a complement to environmental life cycle assessment. We describe and compare three methods explicitly developed for this purpose—Geopolitical Supply Risk (GeoPolRisk), Economic Scarcity Potential (ESP), and the Integrated Method to Assess Resource Efficiency (ESSENZ)—based on a set of criteria including considerations of data sources, uncertainties, and other contentious methodological aspects. We test the methods on a case study of a European‐manufactured electric vehicle, and conclude with guidance for appropriate application and interpretation, along with opportunities for further methodological development. Although the GeoPolRisk, ESP, and ESSENZ methods have several limitations, they can be useful for preliminary assessments of the potential impacts of raw material supply risks on a product system (i.e., “outside‐in” impacts) alongside the impacts of a product system on the environment (i.e., “inside‐out” impacts). Care is needed to not overlook critical raw materials used in small amounts but nonetheless important to product functionality. Further methodological development could address regional and firm‐level supply risks, multiple supply‐chain stages, and material recycling, while improving coverage of supply risk characterization factors.

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“…Attempting to investigate the tolerance of supply networks to hypothetical supplier disruptions, various complex network-based supply network models and corresponding performance metrics are also proposed [19][20][21][22]. Yet, to facilitate the analysis, most of these studies describe supply networks as unipartite networks.…”

Section: A Supply Network Models and Performance Metrics Based On Comentioning

confidence: 99%

“…Some suppliers provide rare products, which are supplied by a very few suppliers. Compared with disruption of suppliers supplying general products, disruption of suppliers supplying rare products may inflict greater damage to the system [21,27]. The model without differentiating the products that suppliers supplying will not reflect the importance of suppliers supplying rare products, although disruption of such suppliers would halt the production of manufacturers.…”

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confidence: 99%

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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A graph theory-based methodology for vulnerability assessment of supply chains using the life cycle inventory database

Cited by 38 publications

References 45 publications

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Raw material criticality assessment as a complement to environmental life cycle assessment: Examining methods for product‐level supply risk assessment

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

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