Supplier Risk Assessment Based on Best-Worst Method and K-Means Clustering: A Case Study

Abstract

Supplier evaluation and selection is one of the most critical strategic decisions for developing a competitive and sustainable organization. Companies have to consider supplier related risks and threats in their purchasing decisions. In today's competitive and risky business environment, it is very important to work with reliable suppliers. This study proposes a clustering based approach to group suppliers based on their risk profile. Suppliers of a company in the heavy-machinery sector are assessed based on 17 qualitative and quantitative risk types. The weights of the criteria are determined by using the Best-Worst method. Four factors are extracted by applying Factor Analysis to the supplier risk data. Then k-means clustering algorithm is applied to group core suppliers of the company based on the four risk factors. Three clusters are created with different risk exposure levels. The interpretation of the results provides insights for risk management actions and supplier development programs to mitigate supplier risk.