Document Type : Research Paper


Department of Industrial & Production Engineering, Military Institute of Science & Technology, Dhaka-1216, Bangladesh.


In managing a supply chain, the green approach has become pivotal for the sake of environmental, economic, and social sustainability. In this paper, we consider the environmental performance prediction in managing sourcing of a textile industry supply chain. Specifically, this research focuses on the dying sector of an emerging economy. We identify eleven green supply chain performance indicators and four performance measures and perform both qualitative and quantitative analyses. The performance is predicted using a probabilistic model based on a Bayesian belief network (BBN). The robustness of the findings is validated through a sensitivity analysis. The outcomes suggest that ‘total suspended solids’ (TSS) and ‘volatile organic compounds’ (VOC) are the most important indicators for the case company in this study with the highest entropy reduction. Also, ‘air emission’ was found to be the most impactful performance measure for entropy reduction. This research work will help improve the decision-making capability of the managers and practitioners considering the total environmental performance of the green supply chain. The improved decision-making will also improve overall organizational performance of a green supply chain.


Main Subjects

  • Abad‐Grau, M. M., & Arias‐Aranda, D. (2006). Operations strategy and flexibility: modeling with Bayesian classifiers.Industrial management & data systems106(4), 460-484.
  • Ahmed, S. S., Akter, T., & Ma, Y. (2018). Green supply chain management (GSCM) performance implemented by the textile industry of Gazipur district, Dhaka. Logistics2(4), 21.
  • Akter, M., & Uddin, M. H. (2017). Supply chain operation model in terms of raw material in Bangladesh apparel industry. International journal of textile science6(2), 43-48.
  • AL-Khatib, A. W., & Shuhaiber, A. (2022). Green intellectual capital and green supply chain performance: does big data analytics capabilities matter?. Sustainability14(16), 10054.
  • Ali, M., & Habib, M. M. (2012). Supply chain management of textile industry: a case study on Bangladesh. International journal of supply chain management1(2), 35-40.
  • Ali, Z. (2022). Predicting SMEs performance through green supply chain practices: a mediation model link of business process performance. Asia pacific journal of marketing and logistics.
  • Alzubi, E., & Akkerman, R. (2022). Sustainable supply chain management practices in developing countries: An empirical study of Jordanian manufacturing companies. Cleaner production letters2, 100005.
  • Baesens, B., Verstraeten, G., Van den Poel, D., Egmont-Petersen, M., Van Kenhove, P., & Vanthienen, J. (2004). Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. European journal of operational research156(2), 508-523.
  • LightCastle Analytics Wing. (n.d.). Bangladesh RMG sector - difficult relationship with sustainability.
  • The Revelator (n.d.). Changing the fabric of our clothes to cut climate emissions.
  • Daddi, T., Heras‐Saizarbitoria, I., Marrucci, L., Rizzi, F., & Testa, F. (2021). The effects of green supply chain management capability on the internalisation of environmental management systems and organisation performance. Corporate social responsibility and environmental management28(4), 1241-1253.
  • Daud, S., Yusof, N., & Mokhtar, M. (2019). The effectiveness of the environmental management system (EMS) implementation in green supply chain: a case study. KnE social sciences, 3(22), 943–962.
  • Dey, S., & Islam, A. (2015). A review on textile wastewater characterization in Bangladesh. Resources and environment5(1), 15-44.
  • Dube, A., Gawande, R. R., & Coe, D. B. (2011). Green supply chain management–a literature review. International journal of computer applications975, 8887.
  • Farhan Shahriar, M., Banik Pathik, B., Mamun Habib, M., & Bangladesh, D. (2014). A research framework of supply chain management in ready made garments industry of Bangladesh. International journal of business and economics research. 3(6-1), pp. 38-44. DOI: 11648/j.ijber.s.2014030601.16
  • The Financial Express. (2019). Air pollution: identifying the reasons behind declining AQI. Retrieved from
  • Gambelli, D., & Bruschi, V. (2010). A bayesian network to predict the probability of organic farms’ exit from the sector: a case study from Marche, Italy. Computers and electronics in agriculture71(1), 22-31.
  • Gomes, D., & Daud, D. (2020). Implementation of green supply chain management in ready-made garment (RMG) sector of Bangladesh. IOP conference series: materials science and engineering(Vol. 780, No. 7, p. 072017). IOP Publishing.
  • Gönlügür, M. E. (2019). Sustainable production methods in textile industry. In Textile Industry and Environment. IntechOpen. DOI: 5772/intechopen.84316
  • Haque, M. S., Nahar, N., & Sayem, S. M. (2021). Industrial water management and sustainability: development of SIWP tool for textile industries of Bangladesh. Water resources and industry, 25, 100145.
  • Hazen, B. T., Russo, I., Confente, I., & Pellathy, D. (2020). Supply chain management for circular economy: conceptual framework and research agenda. The international journal of logistics management, 32(2), 510-537.
  • Herrmann, F. F., Barbosa-Povoa, A. P., Butturi, M. A., Marinelli, S., & Sellitto, M. A. (2021). Green supply chain management: conceptual framework and models for analysis. Sustainability13(15), 8127.
  • Hossain, M. U., & Roy, I. (2016). Supply chain management for sustainable RMG growth in Bangladesh. International journal of science and research5(4), 1242-1248.
  • Islam, M. R., & Mostafa, M. G. (2018). Textile dyeing effluents and environment concerns-a review. Journal of environmental science and natural resources11(1-2), 131-144.
  • Juanga-Labayen, J. P., Labayen, I. V., & Yuan, Q. (2022). A review on textile recycling practices and challenges. Textiles2(1), 174-188.
  • Kabir, G., Balek, N. B. C., & Tesfamariam, S. (2018). Consequence-based framework for buried infrastructure systems: a Bayesian belief network model. Reliability engineering & system safety180, 290-301.
  • Khan, K. I., Babar, Z., Sharif, S., Iqbal, S., & Khan, M. I. (2021). Going green? Investigating the role of GSCM practices on firm financial and environmental performance through green innovation. International journal of procurement management14(6), 681-701.
  • Mansory, A., Nasiri, A., & Mohammadi, N. (2021). Proposing an integrated model for evaluation of green and resilient suppliers by path analysis, SWARA and TOPSIS. Journal of applied research on industrial engineering8(2), 129-149.
  • Mia, R., Selim, M., Shamim, A. M., Chowdhury, M., Sultana, S., Armin, M., ... & Naznin, H. (2019). Review on various types of pollution problem in textile dyeing & printing industries of Bangladesh and recommandation for mitigation. Journal of textile engineering & fashion technology5(4), 220-226.
  • Micheli, G. J., Cagno, E., Mustillo, G., & Trianni, A. (2020). Green supply chain management drivers, practices and performance: a comprehensive study on the moderators. Journal of cleaner production259, 121024.
  • Mohammad, P., & Kazemipoor, H. (2020). An integrated multi-objective mathematical model to select suppliers in green supply chains. International journal of research in industrial engineering9(3), 216-234.
  • Nasiri, A., Mansory, A., & Mohammadi, N. (2022). Evaluating of effective factors on green supply chain management using statistical methods and SWARA approach. International journal of research in industrial engineering11(2), 165-187.
  • Niazi, A., & Nikbakht, M. (2015). Identification and prioritization of barriers to implement green supply chain management in industry: a case study in Petrochemical Company of South Pars. Journal of applied research on industrial engineering2(1), 34-51.
  • Fiber2Fashion (n.d.). Pollution by textile industry - pollutants of water, air, land, environmental pollution by textile industry.
  • Rabbi, M., Ali, S. M., Kabir, G., Mahtab, Z., & Paul, S. K. (2020). Green supply chain performance prediction using a Bayesian belief network. Sustainability12(3), 1101.
  • Roh, T., Noh, J., Oh, Y., & Park, K. S. (2022). Structural relationships of a firm's green strategies for environmental performance: the roles of green supply chain management and green marketing innovation. Journal of cleaner production356, 131877.
  • Roy Choudhury, A. K. (2014). Environmental impacts of the textile industry and its assessment through life cycle assessment. In Roadmap to sustainable textiles and clothing(pp. 1-39). Springer, Singapore.
  • Sadat, M. A., Xames, M. D., & Azeem, A. (0000). A multi-objective mathematical optimization approach to a three-echelon green supply chain management subject to disruption at plant. International conference on industrial and mechanical engineering and operations management (IMEOM) (pp. 44-61). Institution of Engineers Bangladesh (IEB), Dhaka, Bangladesh.
  • Sahoo, S., & Vijayvargy, L. (2020). Green supply chain management practices and its impact on organizational performance: evidence from Indian manufacturers. Journal of manufacturing technology management, 32(4), 862-886.
  • Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review.International journal of management reviews9(1), 53-80.
  • Xames, D., Tasnim, F., Mim, T. I., & Kiron, A. (2022). COVID-19 and food supply chain disruptions in Bangladesh: impacts and strategies. International journal of research in industrial engineering11(2), 155-164.