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Green Supply Chain Performance Prediction Using a Bayesian Belief Network

Author

Listed:
  • Md. Rabbi

    (Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh)

  • Syed Mithun Ali

    (Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh)

  • Golam Kabir

    (Industrial Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada)

  • Zuhayer Mahtab

    (Department of Industrial and Production Engineering, Military Institute of Science and Technology, Dhaka-1216, Bangladesh)

  • Sanjoy Kumar Paul

    (UTS Business School, University of Technology Sydney, Sydney, NSW 2007, Australia)

Abstract

Green supply chain management (GSCM) has emerged as an important issue to lessen the impact of supply chain activities on the natural environment, as well as reduce waste and achieve sustainable growth of a company. To understand the effectiveness of GSCM, performance measurement of GSCM is a must. Monitoring and predicting green supply chain performance can result in improved decision-making capability for managers and decision-makers to achieve sustainable competitive advantage. This paper identifies and analyzes various green supply chain performance measures and indicators. A probabilistic model is proposed based on a Bayesian belief network (BBN) for predicting green supply chain performance. Eleven green supply chain performance indicators and two green supply chain performance measures are identified through an extensive literature review. Using a real-world case study of a manufacturing industry, the methodology of this model is illustrated. Sensitivity analysis is also performed to examine the relative sensitivity of green supply chain performance to each of the performance indicators. The outcome of this research is expected to help managers and practitioners of GSCM improve their decision-making capability, which ultimately results in improved overall organizational performance.

Suggested Citation

  • Md. Rabbi & Syed Mithun Ali & Golam Kabir & Zuhayer Mahtab & Sanjoy Kumar Paul, 2020. "Green Supply Chain Performance Prediction Using a Bayesian Belief Network," Sustainability, MDPI, vol. 12(3), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:1101-:d:316239
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    References listed on IDEAS

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    4. Ziyuan Liu & Yingzhao Wu & Tianle Liu & Xiaoxue Wang & Wenzhuo Li & Ying Yin & Xiangfei Xiao, 2021. "Double Path Optimization of Transport of Industrial Hazardous Waste Based on Green Supply Chain Management," Sustainability, MDPI, vol. 13(9), pages 1-19, May.

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