Welcome to the Monte Carlo Budgeting Coda Doc. This tool allows one to use a Monte Carlo simulation to determine the likelihood of completing a project within a budget given task time estimates it is provided with. Watch the video below for a demonstration of how to use it or jump straight to

Table to enter your tasks. Each row represents one task and contains information such as the task’s name, the best and worst case estimates for task duration, an indication if the task is optional, and any notes you would like to make for yourself about the task.

table if you would like to use non-default values for the high and low risk factor values, as well as the number of simulations to run. In general 1,000 or less should run in under 45 second. If you are wanting a bit more precision 10,000 takes a couple of minutes; whereas any more than that could take a decent amount of time.

Finally, use the “Generate Reports” button to run the simulation.

At any point you can click “Reset Inputs” to clear any data you have entered

Histogram visually shows you the distribution of time and cost likelihoods.

How it Works

This section gives a step by step overview of how the pack runs through a Monte Carlo Simulation.

Pack

The pack formula runMonteCarlo() takes in the task data (low and high time estimates for each task) and simulation settings (budget, cost per time unit, risk factors, number of samples (N), and number of histogram buckets) provided by the user.

library, a value is sampled from a beta distribution for each task.

The total estimate is calculated by summing the sampled values for each task.

Steps 2 and 3 are repeated N times to form a set of N samples forming a beta distribution

The pack checks how many of these samples fit within the budget to determine the likelihood of completing all of the tasks.

Based on the range of samples and how many histogram buckets are requested (default is 10), the pack calculates the percentage of samples that fall within each bucket.

Separately, various percentiles are calculated to give the user an idea of the likelihood of the project being completed at a variety of price points.