DiscoverSim™ Case Studies
Case Study 1 – Basic Profit Simulation: Introduction
- This is an example of DiscoverSim Monte Carlo simulation to determine probability of daily profit using a basic profit model for a small retail business. We will apply distribution fitting to historical data and specify input correlations to define the model in a way that closely matches our real world business. The profit (Pr) requirement is Pr > 0 dollars (i.e., the lower specification limit LSL = 0) The profit equation, or “Y = f(X) transfer function”, is calculated as follows: Total Revenue, TR = Quantity Sold * Price Total Cost, TC = Quantity Sold * Variable Cost + Fixed Cost Profit, Pr = TR – TC
- In this study we will use DiscoverSim to help us answer the following questions:
- What is the predicted probability of daily profit?
- What are the key X variables that influence profit Y? Can we reduce the variation in profit by reducing the variation of the important input variables?
- Distribution Fitting – Discrete Batch Fit
- Distribution Fitting – Continuous Batch Fit
- Distribution Fitting – Specified Distribution Fit
- Create Input Distributions with Stored Distribution Fit
- Specify Input Correlations
- Run Simulation and display
- Histograms, Descriptive Statistics, Process Capability Report
- Percentile Report
- Scatter Plot/Correlation Matrix
- Sensitivity Chart of Correlation Coefficients
- Sensitivity Chart of Regression Coefficients
- Distribution Fitting – Nonnormal Process Capability
|Summary of DiscoverSim Features Demonstrated in Case Study 1:
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