Stochastic supplier selection and order allocation: A conditional value at risk approach

Abstract

In the current business landscape, global supply chain networks are becoming increasingly unpredictable and are focused on risk management. The supplier selection process within a supply chain network is inherently exposed to various risks, and it is essential to minimise these risks and the associated potential losses. This study focuses on modelling risk within the supplier selection process. Thus leading to more informed supply chain procurement decisions. Specifically, the study proposes a bi-objective, risk-averse supplier portfolio model based on Conditional Value-at-Risk (CVaR), a key financial risk management measure. The proposed model aims to optimise the supplier risks in the worst-case scenario corresponding to critical parameters like total procurement cost, supplier quality level, and supplier service levels. Additionally, the proposed model addresses operational uncertainty in supplier parameters using a chance-constrained approach. The proposed methodology is validated through a numerical illustration that demonstrates the practical applicability of the model. Further, the sensitivity analysis is performed through the computational experimental design to assess the robustness of the proposed model and the solution approach. Results demonstrate that the CVaR-based modelling approach effectively addresses the downside risk associated with service and quality objectives while accounting for operational uncertainties in the supplier selection process.