Development of integrated approach for supply chain risk management

Abstract

Risk management (RM) in Supply Chain (SC) is vital for sustaining and gain competitive advantage. The present Supply Chain Risk Management (SCRM) practices are inadequate and incomplete in assessing the risks in SC. Existing SCRM models are complex and lacks in addressing the interrelationship between the SC risks in the entire SC. Also, the capability to predict the risks in future and to mitigate them are not addressed much in the literature. Hence the development of integrated approach is required for SCRM for effective implementation and to aid the SC decision makers to mitigate the risks. In this research work, a systematic and integrated approach (ICAMP model) consisting of Identification, Classification, Assessment, Mitigation and Prediction is proposed for effective SCRM. Initially, various risks related to SC are identified from literature and from experts through Fuzzy Delphi method. The 54 risks identified are clustered under 14 major risks categories using Exploratory Factor Analysis (EFA) and are confirmed through Confirmatory Factor Analysis (CFA). Considering the basic occurrence probability, probability of impact and interdependence of risks, the impact of each risk is computed using Expected Monetary Value (EMV). A Structural Equation Model (SEM) was developed to establish the inter relationship between the SC risks using the hypothesis framed with the help of literature and experts. System Dynamic Model (SD) for mitigating the risks in the current scenario was developed using the interrelationship identified through SEM. Bayesian based risk prediction model considering three states (low, medium and high for each node) was developed to predict the magnitude of each risks in future. To test the robustness of the developed ICAMP model in SCRM practices, it has been applied to three case studies namely automobile, leather and sugar industries SC. The obtained results shows that the developed ICAMP model helps in understanding the interrelationship between the risks, analyzing the impact of each risk