Evidence map›Paper›PMID 38558102›Full record

ArticlePloS one2024

Resilience evaluation of low-carbon supply chain based on improved matter-element extension model.

Xiaochun Luo, Kai Kang, Lin Lu, Changliang Yu, Chaoling Li, Beibei Li, Song Hu, Xia Qi, Yaomei Zhou

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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Xiaochun LuoSchool of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Kai KangSchool of Economics and Management, Guangxi Normal University, Guilin, China.
Lin LuSchool of Economics and Management, Guangxi Normal University, Guilin, China.ORCID 0000-0002-5143-2773
Changliang YuSchool of Economics and Management, Guangxi Normal University, Guilin, China.ORCID 0009-0008-6407-921X
Chaoling LiSchool of Economics and Management, Guangxi Normal University, Guilin, China.
Beibei LiSchool of Economics and Management, Guangxi Normal University, Guilin, China.
Song HuSchool of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China.
Xia QiJose Rizal University, Metro Manila, Philippines.
Yaomei ZhouSchool of Economics and Management, Guangxi Normal University, Guilin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How to evaluate the resilience level and change trend of supply chain is an important research direction in current supply chain management practice. This paper proposes a new method of supply chain resilience assessment based on hesitant fuzzy linguistic term set (HFLTS) and matter element extension theory. Firstly, based on the research status quo at home and abroad, a low-carbon enterprise supply chain resilience assessment index system is established, which includes six first-level indicators and corresponding 21 second-level indicators of product supply resilience, resource resilience, partner resilience, information response resilience, financial resilience and knowledge resilience. Secondly, HFLTS was used to collect expert opinions and Ordered Weighted Arithmetic (OWA) to calculate the expert composite language, by which the fuzzy evaluation matrix of supply chain resilience assessment indicators was obtained. Once again, the resilience indicator weights are determined based on a game-theoretic portfolio assignment method combining the best-worst method (BWM) and the CRITIC method. Finally, the nearness degree function is combined with the extension comprehensive evaluation method to improve the matter element extension model, and the supply chain resilience assessment model of low-carbon enterprises based on the game theory combination assignment-improved matter element extension is established. Taking X low-carbon enterprise as an example, the evaluation results show that the supply chain resilience level of this enterprise is II, and the eigenvalue of the grade variable is 2.69, and the supply chain resilience is shifting to III, and the supply chain resilience is shifting to III, which indicates that the supply chain resilience of this enterprise is being enhanced. Therefore, the improved matter element extension not only ensures the accuracy of the evaluation results, but also has higher prediction accuracy.

Indexed as

Fuzzy LogicResilience, PsychologicalLinguistics

Identifiers

PMID38558102
PMCPMC10984551

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.