Supply chains and their associated logistics networks are becoming increasingly complex with companies facing business challenges such as globalization, omni-channel commerce, supply chain risk mitigation as well as mergers and acquisitions. Many companies have deployed primarily point-based solutions to solve specific operational issues, generating ever more data that proves increasingly difficult to effectively consolidate and analyze from an overall network perspective. In this environment, strategic and operational scenario management is now central to creating a robust, resilient and profitable logistics network. Unfortunately, the tools that are currently available that are designed to address this challenge often prove inadequate for the complexities involved. These tools typically utilize simplified models of the logistics network and operate on estimated aggregate costs using historical data. Not surprisingly, this type of analysis usually results in policies that do not translate effectively to real-world cond itions and are often simply not implementable.