Performance measurement of a Resilient-Sustainable Supply Chain through fuzzy multi-criteria techniques

Vergara, Javier Isaac Torres; Saucedo-Martínez, Jania Astrid; Salais-Fierro, Tomás Eloy

doi:10.1016/j.cie.2023.109059

Cited by 10 publications

(5 citation statements)

References 51 publications

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“…, 2020). Performance of GSC and SSC practices and evaluation: The specific future research directions in this application area are as follows:Research in the integrated resilient and SSC area is in the initial stage and need in-depth investigation to develop better SC performance strategies (Vergara et al. , 2023).…”

Section: Discussion and Application Area-wise Key Insights For Futurementioning

confidence: 99%

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Application of fuzzy methods in green and sustainable supply chains: critical insights from a systematic review and bibliometric analysis

Nirmal

Kumar

Singh

2023

BIJ

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PurposeThe main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).Design/methodology/approachThe present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.FindingsThe authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.Originality/valueThe authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

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Section: Discussion and Application Area-wise Key Insights For Futurementioning

confidence: 99%

“…Research in the integrated resilient and SSC area is in the initial stage and need in-depth investigation to develop better SC performance strategies (Vergara et al. , 2023).…”

Section: Discussion and Application Area-wise Key Insights For Futurementioning

confidence: 99%

Application of fuzzy methods in green and sustainable supply chains: critical insights from a systematic review and bibliometric analysis

Nirmal

Kumar

Singh

2023

BIJ

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“…Fuzzy-Delphi has the capacity to capture inherent vagueness within data. Many researchers use this method across a wide range of subject areas; these include business resilience and sustainability (Kumar and Anbanandam, 2019;Torres Vergara et al, 2023); Demand forecasting (Khan et al, 2023a, b;Zhu et al, 2023); inventory management (Khan et al, 2023a, b;Saputra, 2023); supplier selection (Ambilkar et al, 2023;Ebrahimi et al, 2023); risk management (Jahanvand et al, 2023;Tuni et al, 2023); production planning and quality control (Rana et al, 2023); supply chain optimization (Khan et al, 2023a, b;Tseng et al, 2022); new product introduction (Lianto, 2023); sustainability and environmental impact (Mukherjee and Pradeep, 2023) and resource allocation (Shao et al, 2023). Fuzzy logic allows for the representation of uncertainty and vagueness in the data (Lin et al, 2018;Zhang et al, 2023).…”

Section: Fuzzy-delphi Methodsmentioning

confidence: 99%

An assessment framework to evaluate the critical success factors to Quality 4.0 transition in developing countries: a case experience of sustainable performance of Indian manufacturers

Joshi,

Sharma,

Luthra

et al. 2024

TQM

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PurposeThe research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.Design/methodology/approachThe authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.FindingsThe study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).Research limitations/implicationsThis research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.Originality/valueThis may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.

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“…The authors aim to contribute to manufacturers that emphasize sustainability issues and rank and select suppliers using dependable and implemented methods (Tavana et al 2021 ). Torres Vergara et al ( 2023 ) determined a developed and integrated method of multi-criteria analysis techniques with fuzzy logic that noted the performance levels in resilient-sustainable supply chains (RSSCs). They assessed sustainability and resilience in supply chains.…”

Section: Research Backgroundmentioning

confidence: 99%

“…The performance elements’ impacts and relationships in RSSC were evaluated using DEMATEL. Moreover, the weight of the selected performance criterion was investigated through an analytical network process (ANP) (Torres Vergara et al 2023 ).…”

Section: Research Backgroundmentioning

confidence: 99%

Selecting Green Suppliers by Considering the Internet of Things and CMCDM Approach

Emroozi

Roozkhosh

Modares

et al. 2023

Process Integr Optim Sustain

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Performance measurement of a Resilient-Sustainable Supply Chain through fuzzy multi-criteria techniques

Cited by 10 publications

References 51 publications

Application of fuzzy methods in green and sustainable supply chains: critical insights from a systematic review and bibliometric analysis

Application of fuzzy methods in green and sustainable supply chains: critical insights from a systematic review and bibliometric analysis

An assessment framework to evaluate the critical success factors to Quality 4.0 transition in developing countries: a case experience of sustainable performance of Indian manufacturers

Selecting Green Suppliers by Considering the Internet of Things and CMCDM Approach

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