Monte Carlo Project Risk Analysis

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Author(s): Ruchi Agarwal (University of Edinburgh Business School, UK)and Lev Virine (Intaver Institute, Canada)
Copyright: 2017
Pages: 21
Source title: Handbook of Research on Leveraging Risk and Uncertainties for Effective Project Management
Source Author(s)/Editor(s): Yuri Raydugin (Risk Services & Solutions Inc., Canada)
DOI: 10.4018/978-1-5225-1790-0.ch005

Keywords: Business & Management / Business Science Reference / Management Science / Risk Management & Analysis

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Abstract

Monte Carlo simulations of project schedules have become one of the foundations of quantitative project risk analysis. Monte Carlo method helps to determine the chance that project will be completed on time and on budget, expected project cost and finish time given risks and uncertainties, as well as identify critical risks and crucial tasks. There are a number of ways how Monte Carlo schedule risk analysis can be conducted. “Traditional” Monte Carlo schedule analysis is performed based on statistical distributions of task duration, cost and other input parameters. Event-based quantitative risk analysis incorporates risk events, which can affect project schedules. The chapter discusses a number of important concepts related to Monte Carlo simulations: statistical distribution, sampling process, convergence monitoring, sensitivity analysis, probabilistic and conditional branching and others.

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