155 Quantitative Risk Management Models for Newsvendor Supply Chains Dimitrios Vlachos Aristotle University of Thessaloniki, Greece Chapter 7 ABSTRACT As the practices of offshoring and outsourcing force the supply chain networks to keep on expanding geographically in the globalised environment, the logistics processes are becoming more exposed to risk and disruptions. Thus, modern supply chains seem to be more vulnerable than ever. It is clear that efficient logistics risk and security management emerges as an issue of pivotal importance in such competitive, demanding and stochastic environment and is thus vital for the viability and profitability of a company. In this context, this chapter focuses on a set of stochastic quantitative models that study the impact of one or more supply chain disruptions on optimal determination of single period inventory control poli- cies. The purpose of this research is to provide a critical review of state-of-the-art methodologies to be used as a starting point for further research efforts. INTRODUCTION During the last decade globalization has provided new challenges for supply chain management and the logistics industry. As the practices of offshoring and outsourcing force the supply chain networks to keep on expanding geographically in the glo- balized environment, the logistics processes are becoming more exposed to risk and are ever more prone to disruption. Almost all industries strive DOI: 10.4018/978-1-61520-633-9.ch007 for making their business processes and supply chains either more efficient or more responsive by outsourcing many core business activities, like transportation, warehousing, research and development (R&D), etc. Although these initia- tives have great potential to make operations agile, leaner and more efficient in a low risk and variability environment, at the same time they tend to increase the vulnerability of supply chains to disruptions. However, the traditional decision- making processes and software tools used most often by industry and the methodological models Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.