On Using Monte Carlo Simulations for Project Risk Management

On Using Monte Carlo Simulations for Project Risk Management

Cristiana Tudor (Bucharest University of Economics, Romania) and Maria Tudor (Bucharest University of Economics, Romania)
DOI: 10.4018/978-1-5225-0335-4.ch008
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Abstract

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.
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Monte Carlo Simulation

Although many books have been written on the subject of Monte Carlo simulation, no consensus has been reached with respect to a formal definition. Some authors (e.g. Hammersley and Handscomb, 1964) distinguish between the Monte Carlo method (referring to a technique employed in order to offer a solution to answer a mathematical problem), and the Monte Carlo simulation (referring to computational algorithms, which rely on repeated random sampling with the goal of studying the behavior of a system). On the other hand, other authors (and this current text) use the two terms interchangeably.

Starting with the 1930s, the Monte Carlo simulation method has been used by physicists and mathematicians such as Fermi, or Metropolis and Ulam, which were involved in the 1940s in the Manhattan Project within which the hydrogen bomb was developed at the Los Alamos nuclear research center in the USA. According to Kwak and Ingall (2007), Metropolis is actually credited with naming the methodology after the casinos of Monte Carlo. After the project completed, Metropolis and Ulam (1949) published the first paper on the Monte Carlo method.

Since then, due to its suitability for modeling and analyzing a vast array of systems, the method began to be used in many other domains and is nowadays applied in fields such us biology, engineering, management, meteorology, computer applications, geophysics, public health studies, operations research, and finance. These are just a few of the numerous applications of Monte Carlo techniques.

The method also began to be mentioned often in project management curricula and standards, a field where it is mostly applied in the context of time management (scheduling) or cost management (budgeting):

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