The goal of this research is to investigate interdependency modelling of supply chain risks, and to develop and empirically evaluate a supply chain risk management process that not only integrates all stages of the process but also captures interdependencies between risks and risk mitigation strategies. The proposed process is tailored to the risk management needs of both conventional and project driven supply chains. Project driven supply chains necessitate experimenting untested (unique) strategies depending on the level of project complexity whereas in the case of conventional supply chains, there is generally a consensus in establishing interdependencies between risks and the efficacy of strategies. A systematic literature review methodology was employed to identify research gaps and establish the research agenda. In order to gain an insight into industrial practice, empirical research was conducted in South Australia involving semi-structured interviews with experts in project risk management that resulted in the development of a project complexity and risk management (ProCRiM) process. The research gaps identified and the findings of the empirical research helped in developing dependency based probabilistic supply chain risk measures that can be readily used for assessing and managing risks associated with global supply chains.In order to capture interdependencies between supply chain risks, strategies and performance measures, two case studies were conducted in reputed supply chains involving semi-structured interviews and focus group sessions that resulted in the development of two risk management frameworks: an adapted version of ProCRiM applicable to project driven supply chains and a framework specific to conventional supply chains. The research also focused on investigating the merits and challenges associated with implementing the proposed process. In order to capture the risk appetite of a decision maker, a process namely supply chain risk network management is developed and illustrated through a simulation study.
|Date of Award||5 May 2017|
- University Of Strathclyde
|Supervisor||John Quigley (Supervisor) & Alexander Dickson (Supervisor)|