Supplier Risk Prediction Model

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."