Research article Special Issues

Evaluation and prioritization of barriers to the implementation of the eco-regenerative supply chains using fuzzy ZE-numbers framework in group decision-making

  • Received: 30 March 2024 Revised: 25 June 2024 Accepted: 01 July 2024 Published: 16 July 2024
  • In today's supply chain management, there is a growing emphasis on transitioning to environmentally sustainable practices. This paper aimed to identify and rank the barriers to the implementation of eco-regenerative supply chains. A novel integrated approach was proposed based on stepwise weighted assessment ratio analysis (SWARA) and the multi-attributive border approximation area (MABAC) method using ZE-fuzzy numbers. This approach aimed to address some of the limitations of the failure mode and effects analysis (FMEA) method, including lack of thorough prioritization and inability to make decisions about the importance of various failure factors in an uncertain environment. By combining fuzzy sets and considering the reliability levels of two distinct groups of decision-makers and experts, this proposed method offers a comprehensive evaluation framework. Following the determination of the risk priority number (RPN) by the FMEA method, risk factors were evaluated using ZE-SWARA, and barriers were ranked using the ZE-MABAC method to identify critical barriers and propose corrective actions. Furthermore, sensitivity analysis was conducted in this study to demonstrate the viability of the proposed method. This research contributes to the advancement of eco-regenerative supply chain management practices by offering a systematic and innovative approach to addressing environmental concerns and improving decision-making processes in uncertain environments.

    Citation: Zeynab Rezazadeh Salteh, Saeed Fazayeli, Saeid Jafarzadeh Ghoushchi. Evaluation and prioritization of barriers to the implementation of the eco-regenerative supply chains using fuzzy ZE-numbers framework in group decision-making[J]. AIMS Environmental Science, 2024, 11(4): 516-550. doi: 10.3934/environsci.2024026

    Related Papers:

  • In today's supply chain management, there is a growing emphasis on transitioning to environmentally sustainable practices. This paper aimed to identify and rank the barriers to the implementation of eco-regenerative supply chains. A novel integrated approach was proposed based on stepwise weighted assessment ratio analysis (SWARA) and the multi-attributive border approximation area (MABAC) method using ZE-fuzzy numbers. This approach aimed to address some of the limitations of the failure mode and effects analysis (FMEA) method, including lack of thorough prioritization and inability to make decisions about the importance of various failure factors in an uncertain environment. By combining fuzzy sets and considering the reliability levels of two distinct groups of decision-makers and experts, this proposed method offers a comprehensive evaluation framework. Following the determination of the risk priority number (RPN) by the FMEA method, risk factors were evaluated using ZE-SWARA, and barriers were ranked using the ZE-MABAC method to identify critical barriers and propose corrective actions. Furthermore, sensitivity analysis was conducted in this study to demonstrate the viability of the proposed method. This research contributes to the advancement of eco-regenerative supply chain management practices by offering a systematic and innovative approach to addressing environmental concerns and improving decision-making processes in uncertain environments.



    加载中


    [1] Ghoushchi SJ (2018) Qualitative and quantitative analysis of Green Supply Chain Management (GSCM) literature from 2000 to 2015. Int J Supply Chain Manag 7: 77–86.
    [2] Galvin R, Healy N (2020) The Green New Deal in the United States: What it is and how to pay for it. Energy Res Soc Sci 67: 101529. https://doi.org/10.1016/j.erss.2020.101529 doi: 10.1016/j.erss.2020.101529
    [3] Howard M, Hopkinson P, Miemczyk J (2019) The regenerative supply chain: A framework for developing circular economy indicators. Int J Prod Res 57: 7300–7318. https://doi.org/10.1080/00207543.2018.1524166 doi: 10.1080/00207543.2018.1524166
    [4] Choudhury NA, Ramkumar M, Schoenherr T, et al. (2023) The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities. Transport Res E-Log 175: 103139. https://doi.org/10.1016/j.tre.2023.103139 doi: 10.1016/j.tre.2023.103139
    [5] Govindan K, Kaliyan M, Kannan D, et al. (2014) Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int J Prod Econ 147: 555–568. https://doi.org/10.1016/j.ijpe.2013.08.018 doi: 10.1016/j.ijpe.2013.08.018
    [6] Alhamali RM (2019) Critical success factors for green supply chain management practices: An empirical study on data collected from food processing companies in Saudi Arabia. Afr J Bus Manag 13: 160–167. https://doi.org/10.5897/AJBM2018.8709 doi: 10.5897/AJBM2018.8709
    [7] Ghoushchi SJ, Asghari M, Mardani A, et al. (2023) Designing an efficient humanitarian supply chain network during an emergency: A scenario-based multi-objective model. Socio-Econ Plan Sci 90: 101716. https://doi.org/10.1016/j.seps.2023.101716 doi: 10.1016/j.seps.2023.101716
    [8] Davis KF, Downs S, Gephart JA (2021) Towards food supply chain resilience to environmental shocks. Nat Food 2: 54–65. https://doi.org/10.1038/s43016-020-00196-3 doi: 10.1038/s43016-020-00196-3
    [9] Baloch N, Rashid A (2022) Supply chain networks, complexity, and optimization in developing economies: A systematic literature review and meta-analysis. South Asian J Oper Log 1: 1–13. https://doi.org/10.57044/SAJOL.2022.1.1.2202 doi: 10.57044/SAJOL.2022.1.1.2202
    [10] Azam W, Khan I, Ali SA (2023) Alternative energy and natural resources in determining environmental sustainability: A look at the role of government final consumption expenditures in France. Environ Sci Pollut R 30: 1949–1965. https://doi.org/10.1007/s11356-022-22334-z doi: 10.1007/s11356-022-22334-z
    [11] Feng Y, Lai KH, Zhu Q (2022) Green supply chain innovation: Emergence, adoption, and challenges. Int J Prod Econ 248: 108497. https://doi.org/10.1016/j.ijpe.2022.108497 doi: 10.1016/j.ijpe.2022.108497
    [12] Lis A, Sudolska A, Tomanek M (2020) Mapping research on sustainable supply-chain management. Sustainability 12: 3987. https://doi.org/10.3390/su12103987 doi: 10.3390/su12103987
    [13] Ghadge A, Jena SK, Kamble S, et al. (2021) Impact of financial risk on supply chains: A manufacturer-supplier relational perspective. Int J Prod Res 59: 7090–7105. https://doi.org/10.1080/00207543.2020.1834638 doi: 10.1080/00207543.2020.1834638
    [14] Ngo VM, Quang HT, Hoang TG, et al. (2024) Sustainability‐related supply chain risks and supply chain performances: The moderating effects of dynamic supply chain management practices. Bus Strateg Environ 33: 839–857. https://doi.org/10.1002/bse.3512 doi: 10.1002/bse.3512
    [15] Eftekharzadeh S, Ghoushchi S, Momayezi F (2024) Enhancing safety and risk management through an integrated spherical fuzzy approach for managing laboratory errors. Decision Sci Lett 13: 545–564. https://doi.org/10.5267/j.dsl.2024.5.006 doi: 10.5267/j.dsl.2024.5.006
    [16] Soleimani H, Mohammadi M, Fadaki M, et al. (2021) Carbon-efficient closed-loop supply chain network: An integrated modeling approach under uncertainty. Environ Sci Pollut R 1–16. https://doi.org/10.1007/s11356-021-15100-0 doi: 10.1007/s11356-021-15100-0
    [17] Azarkamand S, niloufar S (2014) Investigating green supply chain management in Isfahan iron smelting industry and its impact on the development of green performance. Appl Stud Manag Develop Sci 4: 15–28. https://doi.org/10.1016/j.spc.2024.06.006 doi: 10.1016/j.spc.2024.06.006
    [18] Alinejad A, Javad K (2014) Presenting a combined method of ANP and VIKOR in the green supply chain under the gray environment in order to prioritize customers (Case of Study: Fars Oil Products Distribution Company). Bus Manag 10. https://doi.org/10.1007/s11356-020-09092-6 doi: 10.1007/s11356-020-09092-6
    [19] Soon A, Heidari A, Khalilzadeh M, et al. (2022) Multi-objective sustainable closed-loop supply chain network design considering multiple products with different quality levels. Systems 10: 94. https://doi.org/10.3390/systems10040094 doi: 10.3390/systems10040094
    [20] Hafezalkotob A (2015) Competition of two green and regular supply chains under environmental protection and revenue seeking policies of government. Comput Ind Eng 82: 103–114. https://doi.org/10.1016/j.cie.2015.01.016 doi: 10.1016/j.cie.2015.01.016
    [21] Sheng X, Chen L, Yuan X, et al. (2023) Green supply chain management for a more sustainable manufacturing industry in China: A critical review. Environ Dev Sustain 25: 1151–1183. https://doi.org/10.1007/s10668-022-02109-9 doi: 10.1007/s10668-022-02109-9
    [22] Oudani M, Sebbar A, Zkik K, et al. (2023) Green Blockchain based IoT for secured supply chain of hazardous materials. Comput Ind Eng 175: 108814. https://doi.org/10.1016/j.cie.2022.108814 doi: 10.1016/j.cie.2022.108814
    [23] Esfahbodi A, Zhang Y, Watson G (2016) Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance. Int J Prod Econ 181: 350–366. https://doi.org/10.1016/j.ijpe.2016.02.013 doi: 10.1016/j.ijpe.2016.02.013
    [24] Alghababsheh M, Butt AS, Moktadir MA (2022) Business strategy, green supply chain management practices, and financial performance: A nuanced empirical examination. J Clean Prod 380: 134865. https://doi.org/10.1016/j.jclepro.2022.134865 doi: 10.1016/j.jclepro.2022.134865
    [25] Falcó JM, García ES, Tudela LAM, et al. (2023) The role of green agriculture and green supply chain management in the green intellectual capital-sustainable performance relationship: A structural equation modeling analysis applied to the Spanish wine industry. Agriculture 13: 425. https://doi.org/10.3390/agriculture13020425 doi: 10.3390/agriculture13020425
    [26] Ecer F, Ögel İY, Krishankumar R, et al. (2023) The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era. Artif Intell Rev 56: 13373–13406. https://doi.org/10.1007/s10462-023-10476-6 doi: 10.1007/s10462-023-10476-6
    [27] Karimi A, Ghoushchi SJ, Bonab MM (2020) Presenting a new model for performance measurement of the sustainable supply chain of Shoa Panjereh Company in different provinces of Iran (case study). Int J Sys Assur Eng 11: 140–154. https://doi.org/10.1007/s13198-019-00932-4 doi: 10.1007/s13198-019-00932-4
    [28] Chatterjee K, Pamucar D, Zavadskas EK (2018) Evaluating the performance of suppliers based on using the R'AMATEL-MAIRCA method for green supply chain implementation in electronics industry. J Clean Prod 184: 101–129. https://doi.org/10.1016/j.jclepro.2018.02.186 doi: 10.1016/j.jclepro.2018.02.186
    [29] Mondal A, Giri BK, Roy SK, et al. (2024) Sustainable-resilient-responsive supply chain with demand prediction: An interval type-2 robust programming approach. Eng Appl Artif Intel 133: 108133. https://doi.org/10.1016/j.engappai.2024.108133 doi: 10.1016/j.engappai.2024.108133
    [30] Riese J, Fasel H, Pannok M, Lier S. (2024) Decentralized production concepts for bio-based polymers-implications for supply chains, costs, and the carbon footprint. Sustain Prod Consump 46: 460–475. https://doi.org/10.1016/j.spc.2024.03.001 doi: 10.1016/j.spc.2024.03.001
    [31] Ferreira IA, Oliveira J, Antonissen J, et al. (2023) Assessing the impact of fusion-based additive manufacturing technologies on green supply chain management performance. J Manuf Technol Mana 34: 187–211. https://doi.org/10.1108/JMTM-06-2022-0235 doi: 10.1108/JMTM-06-2022-0235
    [32] Hiloidhari M, Sharno MA, Baruah D, et al. (2023) Green and sustainable biomass supply chain for environmental, social and economic benefits. Biomass Bioenerg 175: 106893. https://doi.org/10.1016/j.biombioe.2023.106893 doi: 10.1016/j.biombioe.2023.106893
    [33] Zhang Z, Yu L (2023) Dynamic decision-making and coordination of low-carbon closed-loop supply chain considering different power structures and government double subsidy. Clean Technol Envir 25: 143–171. https://doi.org/10.1007/s10098-022-02394-y doi: 10.1007/s10098-022-02394-y
    [34] de Souza V, Ruwaard JB, Borsato M (2019) Towards regenerative supply networks: A design framework proposal. J Clean Prod 221: 145–156. https://doi.org/10.1016/j.jclepro.2019.02.178 doi: 10.1016/j.jclepro.2019.02.178
    [35] Khalilpourazari S, Soltanzadeh S, Weber GW, et al. (2020) Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study. Ann Oper Res 289: 123–152. https://doi.org/10.1007/s10479-019-03437-2 doi: 10.1007/s10479-019-03437-2
    [36] Fragkos P (2022) Analysing the systemic implications of energy efficiency and circular economy strategies in the decarbonisation context. AIMS Energy 10. https://doi.org/10.3934/energy.2022011 doi: 10.3934/energy.2022011
    [37] Tirkolaee EB, Torkayesh AE (2022) A cluster-based stratified hybrid decision support model under uncertainty: Sustainable healthcare landfill location selection. Appl Intell 52: 13614–13633. https://doi.org/10.1007/s10489-022-03335-4 doi: 10.1007/s10489-022-03335-4
    [38] Tirkolaee EB, Sadeghi S, Mooseloo FM, et al. (2021) Application of machine learning in supply chain management: A comprehensive overview of the main areas. Math Probl Eng 2021: 1–14. https://doi.org/10.1155/2021/1476043 doi: 10.1155/2021/1476043
    [39] Bai C, Rezaei J, Sarkis J (2017) Multicriteria green supplier segmentation. IEEE T Eng Manage 64: 515–528. https://doi.org/10.1109/TEM.2017.2723639 doi: 10.1109/TEM.2017.2723639
    [40] Muthuswamy M, Ali AM (2023) Sustainable supply chain management in the age of machine intelligence: Addressing challenges, capitalizing on opportunities, and shaping the future landscape. Sustain Machine Intell J 3: 1–14. https://doi.org/10.61185/SMIJ.2023.33103 doi: 10.61185/SMIJ.2023.33103
    [41] Kumar V, Pallathadka H, Sharma SK, et al. (2022) Role of machine learning in green supply chain management and operations management. Mater Today Proc 51: 2485–2489. https://doi.org/10.1016/j.matpr.2021.11.625 doi: 10.1016/j.matpr.2021.11.625
    [42] Wu T, Zuo M (2023) Green supply chain transformation and emission reduction based on machine learning. Sci Prog 106. https://doi.org/10.1177/00368504231165679 doi: 10.1177/00368504231165679
    [43] Priore P, Ponte B, Rosillo R (2018) Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. Int J Prod Res 57. https://doi.org/10.1080/00207543.2018.1552369 doi: 10.1080/00207543.2018.1552369
    [44] Ali SS, Kaur R, Ersö z F, et al. (2020) Measuring carbon performance for sustainable green supply chain practices: A developing country scenario. Cent Eur J Oper Res 28: 1389–1416. https://doi.org/10.1007/s10100-020-00673-x doi: 10.1007/s10100-020-00673-x
    [45] Barman H, Pervin M, Roy SK, et al. (2023) Analysis of a dual-channel green supply chain game-theoretical model under carbon policy. Int J Syst Sci-Oper 10: 2242770. https://doi.org/10.1080/23302674.2023.2242770 doi: 10.1080/23302674.2023.2242770
    [46] Lotfi R, Kargar B, Hoseini SH, et al. (2021) Resilience and sustainable supply chain network design by considering renewable energy. Int J Energ Res 45: 17749–17766. https://doi.org/10.1002/er.6943 doi: 10.1002/er.6943
    [47] Goli A, Tirkolaee EB, Golmohammadi AM, et al. (2023) A robust optimization model to design an IoT-based sustainable supply chain network with flexibility. Cent Eur J Oper Res 1–22. https://doi.org/10.1007/s10100-023-00870-4 doi: 10.1007/s10100-023-00870-4
    [48] Aytekin A, Okoth BO, Korucuk S, et al. (2022) A neutrosophic approach to evaluate the factors affecting performance and theory of sustainable supply chain management: Application to textile industry. Manage Decis 61: 506–529. https://doi.org/10.1108/MD-05-2022-0588 doi: 10.1108/MD-05-2022-0588
    [49] Thakur AS (2022) Contextualizing urban sustainability: Limitations, tensions in Indian sustainable-smart urbanism perceived through intranational, international comparisons, and district city Ambala study, Sustainable Urbanism in Developing Countries, CRC. Press, 19–39. https://doi.org/10.1201/9781003131922
    [50] Dhull S, Narwal M (2016) Drivers and barriers in green supply chain management adaptation: A state-of-art review. Uncertain Supply Chain Manag 4: 61–76. https://doi.org/10.5267/j.uscm.2015.7.003 doi: 10.5267/j.uscm.2015.7.003
    [51] Bag S, Viktorovich DA, Sahu AK, et al. (2020) Barriers to adoption of blockchain technology in green supply chain management. J Glob Oper Strateg 14: 104–133. https://doi.org/10.1108/JGOSS-06-2020-0027 doi: 10.1108/JGOSS-06-2020-0027
    [52] Rahman T, Ali SM, Moktadir MA, et al. (2020) Evaluating barriers to implementing green supply chain management: An example from an emerging economy. Prod Plan Control 31: 673–698. https://doi.org/10.1080/09537287.2019.1674939 doi: 10.1080/09537287.2019.1674939
    [53] Alfina KN, Ratnayake RC, Wibisono D, et al. (2022) Analyzing barriers towards implementing circular economy in healthcare supply chains, In: 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE 827–831. https://doi.org/10.1109/IEEM55944.2022.9989999
    [54] Khiewnavawongsa S, Schmidt EK (2013) Barriers to green supply chain implementation in the electronics industry, In: 2013 IEEE international conference on industrial engineering and engineering management, IEEE 226–230. https://doi.org/10.1109/IEEM.2013.6962408
    [55] Heeres TJ, Tran TM, Noort BA (2023) Drivers and barriers to implementing the internet of things in the health care supply chain: Mixed methods multicase study. J Med Internet Res 25: e48730. https://doi.org/10.2196/48730 doi: 10.2196/48730
    [56] Li J, Sarkis J (2022) Product eco-design practice in green supply chain management: A china-global examination of research. Nankai Bu Rev Int 13: 124–153. https://doi.org/10.1108/NBRI-02-2021-0006 doi: 10.1108/NBRI-02-2021-0006
    [57] Okanlawon TT, Oyewobi LO, Jimoh RA (2023) Evaluation of the drivers to the implementation of blockchain technology in the construction supply chain management in Nigeria. J Financ Manag Prop 28: 459–476. https://doi.org/10.1108/JFMPC-11-2022-0058 doi: 10.1108/JFMPC-11-2022-0058
    [58] Shrivastav M (2021) Barriers related to AI implementation in supply chain management. J Glob Inf Manag 30: 1–19. https://doi.org/10.4018/JGIM.296725 doi: 10.4018/JGIM.296725
    [59] Mathiyazhagan K, Datta U, Bhadauria R, et al. (2018) Identification and prioritization of motivational factors for the green supply chain management adoption: Case from Indian construction industries. Opsearch 55: 202–219. https://doi.org/10.1007/s12597-017-0316-7 doi: 10.1007/s12597-017-0316-7
    [60] Bey N, Hauschild MZ, McAloone TC (2013) Drivers and barriers for implementation of environmental strategies in manufacturing companies. Cirp Ann 62: 43–46. https://doi.org/10.1016/j.cirp.2013.03.001 doi: 10.1016/j.cirp.2013.03.001
    [61] Zadeh LA (1965) Fuzzy sets. Inf Control 8: 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [62] Wang F (2021) Preference degree of triangular fuzzy numbers and its application to multi-attribute group decision making. Expert Syst App 178: 114982. https://doi.org/10.1016/j.eswa.2021.114982 doi: 10.1016/j.eswa.2021.114982
    [63] Tešić D, Božanić D, Khalilzadeh M (2024) Enhancing multi-criteria decision-making with fuzzy logic: An advanced defining interrelationships between ranked Ⅱ method incorporating triangular fuzzy numbers. J Intel Manag Decis 3: 56–67. https://doi.org/10.56578/jimd030105 doi: 10.56578/jimd030105
    [64] Zadeh LA (2011) A note on Z-numbers. Inf Sci 181: 2923–2932. https://doi.org/10.1016/j.ins.2011.02.022 doi: 10.1016/j.ins.2011.02.022
    [65] Tian Y, Mi X, Ji Y, et al. (2021) ZE-numbers: A new extended Z-numbers and its application on multiple attribute group decision making. Eng Appl Artif Intel 101: 104225. https://doi.org/10.1016/j.engappai.2021.104225 doi: 10.1016/j.engappai.2021.104225
    [66] Stanujkic D, Karabasevic D, Zavadskas EK (2015) A framework for the selection of a packaging design based on the SWARA method. Eng Econ 26: 181–187. https://doi.org/10.5755/j01.ee.26.2.8820 doi: 10.5755/j01.ee.26.2.8820
    [67] Roy SK, Maity G, Weber GW (2017) Multi-objective two-stage grey transportation problem using utility function with goals. Cent Eur J Oper Res 25: 417–439. https://doi.org/10.1007/s10100-016-0464-5 doi: 10.1007/s10100-016-0464-5
    [68] Savku E, Weber GW (2018) A stochastic maximum principle for a Markov regime-switching jump-diffusion model with delay and an application to finance. J Optimiz Theory App 179: 696–721. https://doi.org/10.1007/s10957-017-1159-3 doi: 10.1007/s10957-017-1159-3
    [69] Özmen A, Kropat E, Weber GW (2017) Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty. Optimization 66: 2135–2155. https://doi.org/10.1080/02331934.2016.1209672 doi: 10.1080/02331934.2016.1209672
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(639) PDF downloads(128) Cited by(0)

Article outline

Figures and Tables

Figures(2)  /  Tables(15)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog