Green Supply Chain Performance Prediction Using a Bayesian Belief Network

Sustainability

Md. Rabbi¹  SyedMithun Ali¹  Zuhayer Mahtab²  Golam Kabir³  SanjoyKumar Paul⁴ 

[1] Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh;Department of Industrial and Production Engineering, Military Institute of Science and Technology, Dhaka-1216, Bangladesh;Industrial Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada;UTS Business School, University of Technology Sydney, Sydney, NSW 2007, Australia;

关键词: green supply chain;    performance measurement;    bayesian belief network;    sustainability;   

DOI  :  10.3390/su12031101

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown