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Title: Analytical framework for the management of risk in supply chains
Authors: Gaonkar, R.S. 
Viswanadham, N.
Keywords: Cause-consequence diagrams
Failure analysis
Mean-variance optimization
Partner selection
Risk management
Supply chain design
Supply chain planning
Supply chain risk management
Issue Date: Apr-2007
Citation: Gaonkar, R.S., Viswanadham, N. (2007-04). Analytical framework for the management of risk in supply chains. IEEE Transactions on Automation Science and Engineering 4 (2) : 265-273. ScholarBank@NUS Repository.
Abstract: In this paper, we develop a framework to classify supply chain risk-management problems and approaches for the solution of these problems. We argue that risk-management problems need to be handled at three levels: 1) strategic, 2) operational, and 3) tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions, and disasters. To handle unforeseen events in the supply chain, there are two obvious approaches: 1) to design chains with built-in risk tolerance and 2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and the associated consequences and impacts from these events. Having described these approaches, we then focus our efforts on mapping out the propagation of events in the supply chain due to supplier nonperformance, and employ our insight to develop two mathematical programming-based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design. © 2007 IEEE.
Source Title: IEEE Transactions on Automation Science and Engineering
ISSN: 15455955
DOI: 10.1109/TASE.2006.880540
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