Monte Carlo Simulation of Project Schedules

Brian Steve Smith, PE, PMP, MBA

Course Outline

The course begins with a description of the general utility of Monte Carlo simulation, and its advantages over “point case” models. The data and tools required for the Monte Carlo simulation are described, and illustrated through a simple project schedule example. The course illustrates the construction of the schedule model, the definition of task duration distributions and the model’s output, and how to set up the parameters required to run the Monte Carlo simulation.

Using screen shots and data tables, the course illustrates how to obtain and interpret the simulation results applied to the sample project schedule. In addition to the stochastic model itself, the course also illustrates the value (and process) of performing a sensitivity analysis on the schedule model. Finally, suggestions are given related to the presentation of the simulation to project stakeholders.

This course includes a multiple-choice quiz at the end, which is designed to enhance the understanding of the course materials.

Learning Objective

At the conclusion of this course, the student will:

• Understand the importance of quantifying project schedule risk;
• Learn to convey project completion schedules in terms of probabilities;
• Recognize the advantages of risk analysis compared to simply adding “fat” to project schedules;
• Be able to identify the project tasks to which the overall project is most sensitive;
• Make informed decisions about how to most effectively mitigate project schedule risk based on statistical analysis;
• Develop proficiency at interpreting and presenting Monte Carlo simulation results; and
• Learn how to use stochastic modeling to create a competitive advantage for your firm.

Intended Audience

This course targets project engineers, project managers, engineering managers, and project sponsors.

Benefits to Attendee

Rather than thinking of project completion schedules in terms of a single date, this course enables the audience to think of project schedules in statistical terms, around probabilities that provide a more realistic view of probable project outcomes.

Course Introduction

The course introduces the application of Monte Carlo simulation techniques to project schedules to estimate a probability distribution of possible completion dates. Assuming a prerequisite knowledge of the basics of project schedule development, participants will learn to move beyond simple “deterministic” project duration estimates and begin applying stochastic models for a more realistic and meaningful analysis of probable project completion dates.

Course Content

The course content is in the following PDF file:

Monte Carlo Simulation of Project Schedules

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Course Summary

Too often, project stakeholders, sponsors, and managers are content to think of project schedules in terms of fixed milestone and completion dates, and measure the success or failure of the project by whether these milestone dates are achieved or not. With the risk analysis tools presented in this course, students can confidently manage project schedule performance using date ranges and probabilities, recognizing and managing the uncertainty inherent in most project tasks.