Critical Steps

Premise of Article:  Our team is working on a simulation project, and has good project management skills and a standard problem solving approach.  Now we want to understand what additional steps to execute to ensure success with our simulation project.  Below are five unique & critical steps for simulation projects.

#1 Confirm Model Design:  It is not usual for people to want to include as much detail as possible in their model.  The reality is that you want to include only the detail necessary to achieve the model’s objective.

To resolve this conflict in thinking, we generate a Model Scope document.  This document should include model scope, process map, relevant input data, assumptions, experiments to perform with model, etc.  We need to have this document’s content agreed to by critical team members.

#2 Fit Distributions to Data Collected:  For model input (arrival rate, cycle times, etc.), we will pull data and/or collect data.  Then, we fit statistical distributions this data.  This ensures that we are correctly reflecting randomness in our model.

#3 Select Runtime Parameters:  To ensure that we are collecting output data from our model that accurately reflects steady state behavior of our system, we identify the model’s runtime parameters.  These include length of warmup period, run length, and number of replications.

#4 Verify/Validate Model:  To ensure that we can trust the model to predict accurately, we will verify/validate that the model is behaving correctly.  This includes performing sensitivity analysis on the critical input data.

#5 Utilize Confidence Intervals:  To ensure that our interpretation of the model results is correct, we utilize confidence intervals on critical output metrics.

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