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Figure 2. Proposed research framework. The aim of this research work is to find out the most significant quantitative green supply chain performance measures and predict green supply chain performance in different scenarios. Figure 2 illustrates the methodology of this research. The proposed research consists of five steps as described below.

Figure 2 Proposed research framework. The aim of this research work is to find out the most significant quantitative green supply chain performance measures and predict green supply chain performance in different scenarios. Figure 2 illustrates the methodology of this research. The proposed research consists of five steps as described below.

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Table 1. Green supply chain performance measures and indicators. 3. Bayesian Belief Network
Table 1. Green supply chain performance measures and indicators. 3. Bayesian Belief Network
Figure 1. A simple Bayesian network. bottom-up approach that considers evidence nodes connected to X; through its child node [80] 4, Framework Development
Figure 1. A simple Bayesian network. bottom-up approach that considers evidence nodes connected to X; through its child node [80] 4, Framework Development
Table 2. Data about ‘Amount of Solid Wastes’ performance indicator.
Table 2. Data about ‘Amount of Solid Wastes’ performance indicator.
Figure 3. Cont. Using a histogram, collected data about performance indicators and their corresponding recommended levels are presented in Figure 3.
Figure 3. Cont. Using a histogram, collected data about performance indicators and their corresponding recommended levels are presented in Figure 3.
Figure 3. Histogram of performance indicators.
Figure 3. Histogram of performance indicators.
Table 3. Prior probabilities for green supply chain performance indicators. 5.3. BBN Model Development
Table 3. Prior probabilities for green supply chain performance indicators. 5.3. BBN Model Development
Figure 4. BBN Model for predicting Green Supply Chain performance.
Figure 4. BBN Model for predicting Green Supply Chain performance.
Figure 5. Extreme 1 case when all the performance indicators are in satisfactory states. To validate the model, two extreme conditions are considered in this paper. In extreme case 1, all the performance indicators are in a satisfactory condition. In extreme case 2, all the performance indicators are in an unsatisfactory condition. The results for extreme cases 1 and 2 are shown in Figures 5 and 6, respectively. From Figure 5, it can be found that, when all the performance indexes have a 100 percent probability of being satisfactory, the environmental performance has a 92.8 percent probability of being in a satisfactory condition. Similarly, according to Figure 6, when all the performance indicators have a 100 percent probability of being in an unsatisfactory condition, the environmental performance has only 4.15 percent probability of being in a satisfactory condition. So, the extreme-condition tests show that the proposed environmental performance model works according to expected model behavior.
Figure 5. Extreme 1 case when all the performance indicators are in satisfactory states. To validate the model, two extreme conditions are considered in this paper. In extreme case 1, all the performance indicators are in a satisfactory condition. In extreme case 2, all the performance indicators are in an unsatisfactory condition. The results for extreme cases 1 and 2 are shown in Figures 5 and 6, respectively. From Figure 5, it can be found that, when all the performance indexes have a 100 percent probability of being satisfactory, the environmental performance has a 92.8 percent probability of being in a satisfactory condition. Similarly, according to Figure 6, when all the performance indicators have a 100 percent probability of being in an unsatisfactory condition, the environmental performance has only 4.15 percent probability of being in a satisfactory condition. So, the extreme-condition tests show that the proposed environmental performance model works according to expected model behavior.
Figure 6. Extreme 2 case when all the performance indicators are in unsatisfactory states.
Figure 6. Extreme 2 case when all the performance indicators are in unsatisfactory states.
In scenario 1, where Scrap_Flow_quantity and Hazardous_Toxic_Material are at 90 percent, Environmental Performance is at 61.3 percent. In scenario 2, where the probability of satisfactory states of performance indicators decreases to 80 percent, the nvironmental performance decreases to 57.6 percent. Scenario 3 depicts 70 percent satisfactory probability of performance indicators where Environmental Performance decreases to 54 percent. Similarly, from scenarios 4-8, where the probability of a satisfactory state of Scrap_Flow_quantity and Hazardous_Toxic_Material decreases, the probability of Environmental Performance being in a satisfactory state also decreases. In scenario 8, where the performance indicators are at 10 percent of the probability of being in a satisfactory state, the Environmental Performance has only a 32 percent probability of being in a satisfactory state. Thus, the scenarios show the expected model behavior. Fe ae ey as RR rr fh: a i a i: os i i: i i: a ee. ns fo in i of per i
In scenario 1, where Scrap_Flow_quantity and Hazardous_Toxic_Material are at 90 percent, Environmental Performance is at 61.3 percent. In scenario 2, where the probability of satisfactory states of performance indicators decreases to 80 percent, the nvironmental performance decreases to 57.6 percent. Scenario 3 depicts 70 percent satisfactory probability of performance indicators where Environmental Performance decreases to 54 percent. Similarly, from scenarios 4-8, where the probability of a satisfactory state of Scrap_Flow_quantity and Hazardous_Toxic_Material decreases, the probability of Environmental Performance being in a satisfactory state also decreases. In scenario 8, where the performance indicators are at 10 percent of the probability of being in a satisfactory state, the Environmental Performance has only a 32 percent probability of being in a satisfactory state. Thus, the scenarios show the expected model behavior. Fe ae ey as RR rr fh: a i a i: os i i: i i: a ee. ns fo in i of per i
Figure 7. Scenario analysis of simultaneous change of Scrap_Flow_quantity and Hazardous_Toxic_Material. Table 4. Scenario analysis results of the proposed BBN-based model.
Figure 7. Scenario analysis of simultaneous change of Scrap_Flow_quantity and Hazardous_Toxic_Material. Table 4. Scenario analysis results of the proposed BBN-based model.
Table 5. Posterior probabilities of parent nodes conditioned to Environmental Performance.
Table 5. Posterior probabilities of parent nodes conditioned to Environmental Performance.
Related topics:
Supply Chain Performance ManagementSustainability Performance
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