Combining Bayesian Networks and Total Cost of Ownership method for supplier selection analysis

In this study, we analyze the supplier selection process by combining Bayesian Networks (BN) and Total Cost of Ownership (TCO) methods. The proposed approach aims to efficiently incorporate and exploit the buyer’s domain-specific information when the buyer has both limited and uncertain information regarding the supplier. This study examines uncertainty from a total cost perspective, with regards to causes of supplier performance and capability on buyer’s organization. The proposed approach is assessed and tested in automotive industry for tier-1 supplier for selecting its own suppliers. To efficiently facilitate expert opinions, we form factors to represent and explain various supplier selection criteria and to reduce complexity. The case study in automotive industry shows several advantages of the proposed method. A BN approach facilitates a more insightful evaluation and selection of alternatives given its semantics for decision making. The buyer can also make an accurate cost estimation that are specifically linked with suppliers’ performance. Both buyer and supplier have clear vision to reduce costs and to improve the relations.

► We analyze the supplier selection process by combining BN and TCO methods.
► Uncertainties due to causes of supplier performance on buyer are examined.
► The model allows a system of reasoning under the absence of complete information.
► Relations and intensities among supplier selection criteria and costs are identified.
► Flexibility, delivery, and price are among the most critical factors.