Simulation of Supply Chain Risk

David L. Olson and Desheng Wu
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David L. Olson: University of Nebraska
Desheng Wu: University of Chinese Academy of Sciences

Chapter 5 in Enterprise Risk Management Models, 2023, pp 55-73 from Springer

Abstract: Abstract Many supply chain problem analyses involve uncertainty in the form of statistically measured distributions. Monte Carlo simulation is a highly useful tool to analyze statistical distributions and to model many supply chain decisions involving risk. Inventory management and vendor selection decisions are demonstrated using Crystal Ball software.

Date: 2023
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DOI: 10.1007/978-3-662-68038-4_5

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