Abstract
We consider a problem of quantifying risk factors and identifying most informative (or vulnerable) components of the supply chain in terms of the amount of information about the risks and corresponding losses. This knowledge is beneficial for the selection of risk-prevention decisions. Shannon’s entropy is shown to be a powerful tool for risk management in hierarchical supply chains. An efficient algorithm is proposed that permits to reduce the size of the supply chain model without a loss of essential information about the risks and their economic consequences. A case study is presented to demonstrate the validity of the entropy-based approach.
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