Paper Type
Research-in-Progress Paper
Abstract
As today’s supply chain (SC) networks are globalized complex systems planning and optimizing SC processes is harder than ever. Unexpected deviations and disruptions can lead to devastating and far-reaching consequences. Hence, the management of SC risk (SCR) is of fundamental importance. The literature on SCR is, however, mostly of anecdotic nature; only few authors present empirical research. So far no unanimous framework has been developed to explicitly quantify SCR and the underlying SC vulnerability drivers. To ensure that SCR management can be realized as a continuous process, SC managers, seek solutions that integrate SCR analysis in their routines and planning processes. Therefore, the challenge is to model SCR in a way that is both transferable to models of proprietary operational SC planning engines and suitable for quantitative SCR analysis. In our work, we combine simulation and operational SC planning to identify factors that enhance the vulnerability of complex SC systems.
Recommended Citation
Heckmann, Iris and Comes, Tina, "Modeling Supply Chain Risk for Operational Supply Chain Planning" (2013). AMCIS 2013 Proceedings. 1.
https://aisel.aisnet.org/amcis2013/eGovernment/RoundTablePresentations/1
Modeling Supply Chain Risk for Operational Supply Chain Planning
As today’s supply chain (SC) networks are globalized complex systems planning and optimizing SC processes is harder than ever. Unexpected deviations and disruptions can lead to devastating and far-reaching consequences. Hence, the management of SC risk (SCR) is of fundamental importance. The literature on SCR is, however, mostly of anecdotic nature; only few authors present empirical research. So far no unanimous framework has been developed to explicitly quantify SCR and the underlying SC vulnerability drivers. To ensure that SCR management can be realized as a continuous process, SC managers, seek solutions that integrate SCR analysis in their routines and planning processes. Therefore, the challenge is to model SCR in a way that is both transferable to models of proprietary operational SC planning engines and suitable for quantitative SCR analysis. In our work, we combine simulation and operational SC planning to identify factors that enhance the vulnerability of complex SC systems.