INTEGRATION OF BAYESIAN NETWORKS WITH DEMATEL FOR CAUSAL RISK ANALYSIS: A SUPPLIER SELECTION CASE STUDY IN AUTOMOTIVE INDUSTRY
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Bayesian Networks (BNs) are effective tools in analysis of causal relations in uncertain environments. BNs can make probabilistic calculations when a part of their variables are unknown. They can be constructed based on expert knowledge. However, there is not a widely accepted method for building BNs from expert knowledge. A common way of building BNs from expert knowledge is asking experts directions of arcs between nodes. However, this approach is not systematic as experts can be subject to errors and biases about existence and directions of causal relations. This approach is also difficult to apply especially when there are multiple experts with conflicting opinions. This thesis proposes a method to build BN models based on multiple experts’ opinion by using the Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. DEMATEL is a Multi Criteria Decision Making (MCDM) Method to determine cause-effect relationships between multiple criteria. In our method, the causal structure of BN is determined by asking experts pairwise direct influence values of criteria on each other via DEMATEL survey. Then, our method systematically revises the structure based on DEMATEL results and expert opinion. After construction of the BN structure, the BN is parameterized by using ranked nodes. DEMATEL survey is also used to determine the parameters of ranked nodes. Sensitivity analysis of parameters is conducted to measure the robustness of the model. And sensitivity analysis of evidence is conducted to evaluate the consistency of the model by comparing its results with the total relation matrix of DEMATEL. DEMATEL alone is not able to make probabilistic calculations to handle uncertainty. When DEMATEL and BN are integrated with our method, DEMATEL provides the causal structure of BN and then BN makes it possible to analyse risk and uncertainty based on the causal relationship between the decision criteria. They complement each other and integration of them provides a practical decision support tool. We applied our proposed method to a supplier selection case study in a large automotive manufacturer in Turkey. Our proposed method is suitable for the supplier selection problem as it has multiple interrelated decision criteria and uncertainty. In addition to these, buyers usually do not have perfect information regarding their suppliers, and the BN model developed by our approach is also able to deal with that. In the case study, the cause-effect relations between supplier selection criteria were determined by DEMATEL survey and the risks related with the criteria among their interactions were analyzed by BN according to knowledge of 14 experts from the automotive manufacturer. Experts can use the model to estimate the values of supplier selection criteria and analyse decision scenarios. The proposed approach presents a novel way of building BN model from the expert knowledge by using DEMATEL surveys and ranked nodes. Another contribution of the thesis is to provide a practical decision support tool for supplier selection decision analysis in automotive industry.