Monte Carlo Simulation

Quantitative Risk Analysis for Project Management

In this article we are going to discuss using Monte Carlo simulation to perform quantitative risk analysis for project management.  The three primary risks to a project are schedule, financial, and technical.  Two of these three risks readily lend themselves to be quantified with Monte Carlo simulation: schedule risk and financial risk. If you’re unfamiliar …

Input Correlation Methods in Simulation Master

In this article, we will discuss input correlation methods that can be used with Simulation Master.  In a previous article, we discussed why it is important to account for correlation among input random variables when performing Monte Carlo simulation. There are two input correlation methods available in Simulation Master: Rank order correlation Bivariate copulas Rank …

Monte Carlo Simulation Checklist

This is a Monte Carlo simulation checklist to ensure proper model set-up when working with Simulation Master.  Unless stated otherwise, each item refers to both the standard and premium editions. Monte Carlo Simulation Checklist If you want to record random variable values in the simulation data sheet, the random variables must be in the same …

Setting Up a Project Critical Path Model

Determining project schedule risk through simulation can be achieved by constructing a critical path model in Excel.  This might sound tedious, especially for large projects, but we will walk through the process and show that it’s pretty simple.  Simulating a critical path model is worth the effort and was discussed in a previous article.  We’ll …

Assessing Distribution Fit

When fitting a probability distribution to a set of data, we would like to know how well our distribution fitting has done to represent the underlying data.  We will dive into the nuts and bolts of assessing distribution fit. There are several methods used for assessing distribution fit.  We will divide these methods into two …

An Example of Probabilistic Design

Traditional engineering design employs the concept of a safety factor to prevent failure.  In this article we are going to discuss, with a simple example, how probabilistic design can be used as an alternative to safety factors. Background on Safety Factors In engineering design, uncertainties in material strength, material dimensions, and loading is often accounted …

Simulation vs. Machine Learning

Simulation and machine learning are related in that they both revolve around models, but they are very different.  In fact, simulation and machine learning are almost opposites.  We’ll dive into the differences between the two and how they are used. Simulation Characteristics With simulation the model is often known.  In other words, we know how …

Monte Carlo Simulation Overview

Monte Carlo simulation has many applications such as risk analysis and engineering design.  It’s often the best method to deal with uncertainty in complex models where an analytical solution would be nearly impossible.  The graphic below provides a Monte Carlo simulation overview. The first step is to identify the uncertain inputs, or random variables.  This …

Using Monte Carlo Simulation for Purchasing Decisions

Using Monte Carlo simulation for purchasing decisions can allow for overall cost reduction by selecting optimum order quantities.  In this article we will explore how to use simulation to minimize the total cost of a purchased component. When using purchased components, the purchase price is only part of the total cost.  Carrying cost also should …

Basics of Monte Carlo Simulation

When understood properly, Monte Carlo simulation is an invaluable tool for understanding uncertainty, and ultimately the risks that your project faces.  In this article we will discuss the basics of Monte Carlo simulation. Risk is sometimes a catch-all term that isn’t well-defined.  Monte Carlo simulation can find risks such as the following: What is the …