A systems approach for modelling supply chain risks
journal contributionposted on 19.09.2013, 14:21 by Abhijeet Ghadge, Samir Dani, Michael Chester, Roy KalawskyRoy Kalawsky
Purpose – With increasing exposure to disruptions, it is vital for supply chains to manage risks proactively. Prediction of potential failure points and overall impact of these risks is challenging. In this paper, systems thinking concepts are applied for modelling supply chain risks. The purpose of this paper is to develop a holistic, systematic and quantitative risk assessment process for measuring the overall risk behaviour. Design/methodology/approach – A framework for supply chain risk management (SCRM) is developed and tested using an industrial case study. A systematically developed research design is employed to capture the dynamic behaviour of risks. Additionally, a system-based supply chain risk model is conceptualized for risk modelling. Sensitivity modelling results are combined for validating the supply chain risk model. Findings – The systems approach for modelling supply chain risks predicts the failure points along with their overall risk impact in the supply chain network. System-based risk modelling provides a holistic picture of risk behavioural performance, which is difficult to realise through other research methodologies commonly preferred in SCRM research. Practical implications – The developed framework for SCRM is tested in an industry setting for its viability. The framework for SCRM along with the supply chain risk model is expected to benefit practitioners in understanding the intricacies of supply chain risks. The system model for risk assessment is a working tool which could provide a perspective of future disruptive events. Originality/value – A holistic, systematic and quantitative risk modelling mechanism for capturing overall behaviour of risks is a valuable contribution of this research. The paper presents a new perspective towards using systems thinking for modelling supply chain risks.
- Mechanical, Electrical and Manufacturing Engineering