Powersim Studio AcademicRisk Analysis

Once you have created a simulation project, you are ready to run Risk Analysis to investigate the uncertainties within your model. 

Simply identify the model variables that represent uncertainty. These variables are referred to as Assumptions. Then identify the variables for which you want to study the influence of the uncertainties. These variables are referred to as Effects. Choose between two different Risk algorithms; Monte-Carlo or Latin Hypercube.

Risk analysis is a built-in feature of Powersim Studio and is available by adding a Risk Analysis task:

  • Select the set of assumptions and effects before the analysis starts.
  • Choose how many times the simulation is to be run during the analysis, for example 40 times. As much as 100,000 times is possible.
  • You may clone a Risk Analysis task to make studies with different horizons: for example a 1-year, short-term plan in addition to a 10-year, long-term business analysis.
  • After the analysis, study the resulting percentiles and average values on the Effects variables.

Risk Analysis will give answers to how volatile your business strategies are when considering real-life uncertainties.

Telecom case

In the below example, a simulation model was used by a telecom company to plan when and how to increase their network capacity. The model considers different types of networks, staffing capacity, time to launch, and cost & revenue factors.

A Telecom simulation analyzing risk before expanding networks.

The end users ran multiple risk analyses to investigate how sensitive their business was to uncertainties in customer potential, complexity of network implementation, and its deterioration. The graphs show percentiles and standard deviation for one such Risk Analysis.

Benefits from using Risk Analysis in simulations

  1. Use Risk Analysis to get a feel for how much uncertainties in your market place affect your business.
  2. Improve your strategies and decisions after evaluating the results from a Risk Analysis.
  3. Validate your model settings by testing known variations in assumptions towards the expected model behaviour. 
  4. Allow critics of your model to change assumptions and stress test with a risk analysis to build confidence in your model.
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