Abstract:
"Supply chain networks are the backbone of the majority of businesses globally. Due to
the importance of supply chain networks, the entire global trading community is focused on
ensuring supply chain resilience and sustainability. However, there are multiple supply chain
risks that constantly disrupt the smooth functioning of supply chains. Supplier-related risks are
one such major type of risk which could be controlled by organizations in a much more
effective manner, rather than other types of risks, due to the level of control the company holds
in the supplier selection process. Thus, this study has drawn its focus to develop a Supplier
Risk Prediction model, which will benefit the organizations in accurately identifying the levels
of supplier risk in ‘High’, ‘Medium’ or ‘Low’ categories. This model is recommended to be
deployed in a strategic position between the sourcing process and the PO issuance stage to
ensure that the businesses select the most optimal suppliers. The main objective of this study
is to develop a Supplier Risk Prediction model to accurately predict the supplier risk levels. In
order to execute the study a secondary dataset was selected to aid the development of the model.
R Studio is used as the data analysis tool for this quantitative study. Initially the dataset was
preprocessed, then the model was trained and tested. Five main classification techniques
namely, Random Forest, SVM, Decision Tree, Logistic Regression and kNN technique were
used for model development. Based on the measures model comparison, SVM emerged as the
most compatible and predictive model with the highest accuracy in predicting the supplier risk
levels. Afterwards, the SVM Model was applied on an actual dataset to validate the model."
