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Manufacturing is one of the biggest drivers of a country's economic growth. Nevertheless, due to globalization and flourishing consumer markets, the technological influx in manufacturing evolution poses a significant threat to climate change. To deal with the situation, green manufacturing came forward to play a vital role in lowering the impact of mass production on the global environment. The qualitative research based on expert opinion is used to have viewpoints for the implementation of green manufacturing based on green supply chain manufacturing (GSCMEs) enablers. The study, in this regard, focuses on exploring the key enablers adopted by the manufacturers to embrace green practices by using framework based on Interpretative Structural Modelling and Cross-Impact Multiplication Applied to Classification (MICMAC) analysis. Results indicate that economic constraints and the regulatory framework have high driving power and less dependency power. Researchers provide managers with a new outlook on the future towards building an eco-friendly supply chain and gaining a competitive edge over their competitors.
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Tom
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271--290
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Bibliogr. 34 poz., tab., rys.
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autor
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Bibliografia
- [1] Akabar, M., & Irohara, T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production, 205, 866–883. https://doi.org/10.1016/j.jclepro.2018.09.100.
- [2] Chobar, P., Adibi, A., & M., Kazemi, A. (2021). A novel multi-objective model for hub location problem considering dynamic demand and environmental issues. Journal of Industrial Engineering.
- [3] Ali, S.S., Kaur, R. & Khan, S. (2022) Evaluating sustainability initiatives in warehouse for measuring sustainability performance: an emerging economy perspective. Ann Oper Res . https://doi.org/10.1007/s10479-021-04454-w.
- [4] Ali, S. S.; and Kaur. R. (2021) Effectiveness of corporate social responsibility (CSR) in implementation of social sustainability in warehousing of developing countries: A hybrid approach, Journal of Cleaner Production, 324,129154. https://doi.org/10.1016/j.jclepro.2021.129154.
- [5] Ali, S. S., Paksoy, T., Torgŭl, B., & Kaur, R. (2020a). Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: A fuzzy hybrid multi criteria decision-making approach. Wireless Network, 26, 5759–5782. https://doi.org/10.1007/s11276-019-022, 46-6.
- [6] Ali, S. S., Kaur, R., Ersoz, F., Altaf, B., Basu, A., & Weber, G.-W. (2020b). Measuring carbon performance for sustainable green supply chain practices. Central Journal of European Research, 28(4), 1389–1416. https://doi.org/10.1007/s10100-020-00673-x.
- [7] Ali, Sadia Samar, Kaur, R., Persis, D. J., Saha, R., Pattusamy, M. & Sreedharan, V. R. (2020c), Developing a hybrid evaluation approach for the low carbon performance on sustainable manufacturing environment, Annals of Operations Research, DOI: 10.1007/s10479-020-03877-1.
- [8] Ali, S. S., Kaur, R., & Ersöz, F. (2019a). Evaluation of the effectiveness of green practices in manufacturing sector using CHAID analysis. Journal of Remanufacturing, 9, 3–27. https://doi.org/10.1007/s13243-01 8-0053-y.
- [9] Ali, S. S., Kaur, R., & Marmolejo, J. A. (2019b). Best practices of green supply chain management: A developing countries perspectives. Emerald Global Publications ISBN: 9781787562165, pp. 10, 30, 51.
- [10] Ali, S. S., Kaur, R., & Jarmillo, L. A. B. (2018). An assessment of green supply chain framework in Indian automobile industry using interpretive structural modelling and its validation using MICMAC analysis. International Journal of Service and Operations Management, 30(3), 318–356. https://doi.org/10.1504/ IJSOM.2018.092607.
- [11] Allevi, E., Gnudi, A., Konnov, I. V., & Oggioni, G. (2018). Evaluating the effects of environmental regulations on a closed-loop supply chain network: A variational inequality approach. Annals of Operations Research, 261(1), 1–43.
- [12] Altaf, B. Ali, S. S. and G. W. Weber (2020) Modelling the relationship between organizational performance and green supply chain practices using Canonical correlation analysis, Wireless Networks (S.I Intelligent Resource Management using analytical methods), 26, 5835–5853, http://doi.org/10.1007/s11276-020-02313-3.
- [13] Bendoly, E. 2014. System Dynamics Understanding in Projects: Information Sharing, Psychological Safety, and Performance Effects. Production and Operations Management 23 (8): 1352–1369.
- [14] Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233, 299–312.
- [15] Cardoso, R., R. Leite, and A. de Aquino. (2016) A Graph is Worth a Thousand Words: How Overconfidence and Graphical Disclosure of Numerical Information Influence Financial Analysts Accuracy on Decision Making. PLOS ONE 11 (8): 0160443.
- [16] Damert, M., Feng, Y., Zhu, Q., & Baumgartner, R. J. (2018). Motivating low-carbon initiatives among suppliers: The role of risk and opportunity perception. Resources, Conservation and Recycling, 136, 276-286.
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- [18] Govindan, K., Agarwal, V., & Darbari, J.D. (2019). An integrated decision-making model for the selection of sustainable forward and reverse logistic providers. Annals of Operations Research, 273, 607–650.
- [19] Hashim, M., Nazam, M., Yao, L., Baig, A.S., Zia-ur-Rehman, M., (2017). Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment, Journal of Industrial Engineering and Management, 10(2) 182-212.
- [20] Hughes, D., N. Rana, and Y. Dwivedi. 2019. Elucidation of IS Project Success Factors: An Interpretive Structural Modelling Approach. Annals of Operations Research 285: 35–66.
- [21] IPCC, Intergovernmental Panel on Climate Change available at http://www.ipcc.ch.
- [22] Kumar, A., Mangla, S.K., Luthra, S., & Ishizaka A. (2019). Evaluating the human resource related soft dimensions in green supply chain management implementation. Production Planning & Control, 30(9), 699-715.
- [23] Malesios, C., Dey, P. K., & Abdelaziz, F. B. (2018). Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling. Annals of Operations Research. https://doi.org/10.1007/s10479-018-3080-z.
- [24] Mishra, N., A. Singh, N. P. Rana, and Y. K. Dwivedi. 2017. Interpretive Structural Modelling and Fuzzy MICMAC Approaches for Customer Centric Beef Supply Chain: Application of a big Data Technique. Production Planning & Control 28 (11-12): 945–963.
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- [26] Ning, T., Wang, Z., Zhang, P., & Gou, T. (2020). Integrated optimization of disruption management and scheduling for reducing carbon emission in manufacturing. Journal of Cleaner Production, 263, 121449.
- [27] Oliver, J., Janssens-Maenhout Jeroen, G., Peters, J. (2012). Trends in global CO2 emissions, Publisher: PBL Netherlands Environmental Assessment Agency.
- [28] Rao, P. & Holt, D. (2005). Do green supply chains lead to competitiveness and economic performance? International Journal of Operations & Production Management, 25 (9) 898-916.
- [29] Richards, D.J., (1994). Environmentally conscious manufacturing, World Class Design to Manufacture, 1 (3).
- [30] Smith, L., & Ball, P. (2012). Steps towards sustainable manufacturing through modelling material, energy and waste flows. International Journal of Production Economics, 140(1), 227–238.
- [31] Tachizawa, E.M., Gimenez C., Sierra V., (2015). Green supply chain management approaches: drivers and performance implications, International Journal of Operations & Production Management, 35 (11) 1546- 1566.
- [32] Thamsatitdej, P., S. Boon-itt, P. Samaranayake, M. Wannakarn, and T. Laosirihongthong. (2017) Eco-design Practices Towards Sustainable Supply Chain Management: Interpretive Structural Modelling (ISM) Approach. International Journal of Sustainable Engineering 10 (6): 326–338.
- [33] Validi, S., Bhattacharya, A., & Byrne, P. J. (2014). Integrated low-carbon distribution system for the demand side of a product distribution supply chain: A DoE-guided MOPSO optimiser-based solution approach. International Journal of Production Research, 52(10), 3074–3096.
- [34] Warfield, J. 1974. Developing Interconnected Matrices in Structural Modelling. IEEE Transactions on Systems, Man, and Cybernetics 4 (1): 51–81.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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