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Supply chain risk analyses performed by applying logistic assistance systems

: Yüzgülec, Gökhan; Wagenitz, Axel; Toth, Michael

Australian Society For Operations Research -ASOR-:
Making the future better by operations research : 20th International Conference of Australian Society for Operations Research incorporating the 5th International Intelligent Logistics System Conference; Holiday Inn Surfers Paradise Gold Coast, Australia, September 27th - 30th, 2009
Brisbane: Queensland University of Technology Printery, 2009
ISBN: 978-0-646-52115-2
Australian Society for Operations Research (National Conference) <20, 2009, Gold Coast>
International Intelligent Logistics System Conference <5, 2009, Gold Coast>
Conference Paper
Fraunhofer IML ()
supply chain management; risk analyses; simulation

Risk occurrence usually leads not only to damages at relevant enterprises but causes further risks at other supply chain partners. Consequently, more and more enterprises force their partners, particularly small and medium sized enterprises, to make statements regarding their risks and provisions, especially regarding the fulfilment of specific orders which are at high risk due to fluctuating system load. Currently, there are no applicable tools and methods known to assess risks quantitatively as well as their interdependencies from a supply chain perspective based on orders regarding the probability of adherence to schedules. In current research projects the process chain instrument is being analysed to be applied in terms of risk assessment regarding unscheduled increase of lead times. Furthermore, a supply chain risk management approach is being developed by implementing the process chain instrument into the new supply chain software generation Logistic Assistance Systems, which allow dynamic and quantitative risk assessment and controlling from an entire supply chain perspective with a simulation based approach. The developed simulation component is able to handle the complexity of multiple risks considering their interdependencies, occurrence probabilities and mutual impact. Thus, risks, provisions and alternatives to control risks can be demonstrated and assessed quantitatively.