Evidence map›Paper›PMID 34722848›Full record

ArticleSustainable production and consumption2021

Prioritising risk mitigation strategies for environmentally sustainable clothing supply chains: Insights from selected organisational theories.

Abhijit Majumdar, Sanjib Kumar Sinha, Kannan Govindan

Abstract read
In one paragraph

Article in Sustainable production and consumption, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Abhijit MajumdarDepartment of Textile and Fibre Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.
Sanjib Kumar SinhaDepartment of Textile and Fibre Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.
Kannan GovindanChina Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai, 201306, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent outbreak of COVID-19 pandemic has provided strong impetus to supply chain resilience research. In a volatile and uncertain business environment, resilience can be incorporated by developing and implementing effective risk mitigation strategies. In this research, risk mitigation strategies for environmentally sustainable clothing supply chain have been prioritised by considering their efficacy to mitigate various risks. Twelve risks and thirteen mitigation strategies, identified through literature review and experts' opinion, are considered as decision criteria and alternatives respectively. Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (fuzzy TOPSIS) is implemented under a group decision making scenario for prioritising the strategies. Developing supply chain agility; multiple green sourcing and flexible capacities; adoption of green practices; building trust, coordination and collaboration; and alignment of economic incentives and revenue sharing are found to be dominant risk mitigation strategies for environmentally sustainable clothing supply chain. These strategies have been viewed through the lens of resource dependence, change management and transaction cost theories. Organisation desirous to build resilience in their supply chain can prioritise the risk mitigation strategies and adopt a portfolio of strategies based on the outcome of this research.

Indexed as

Clothing supply chainEnvironmental sustainabilityFuzzy TOPSISResource dependenceRisk mitigation strategiesSupply chain risks

Identifiers

PMID34722848
PMCPMC8536944

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.