Monte Carlo Simulation

Presenting Simulation Results

Presenting the results of a Monte Carlo simulation can be challenging when your audience isn’t inclined to receive probabilistic information.  Decision makers often want a single number for net present value, project completion date, or profit. How do we as the modeler/analyst present this information so it is understood and appreciated for the extra information …

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Optimizing a Design with Monte Carlo Simulation

Performance and cost trade-offs are a constant in engineering design.  Optimizing a design to balance these competing forces can be challenging to quantify and we often have to resort to experience or intuition.  One of the traditional cost drivers is specifying tolerances.  Part dimensions are naturally random variables since each part manufactured will not be …

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Project in action

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 …

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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 …

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