**Now Includes SigmaXL Version 10**

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Getting Started Tutorial

DiscoverSim Version 2.2 Feature List Summary

Key Features (bold denotes new in Version 2.0, 2.1 and 2.2):

• Excel add-in for Monte Carlo Simulation and Optimization


• Uses Six Sigma Language: Specify Inputs (Xs) and Outputs (Ys)


• Monte Carlo simulation with 53 Continuous and 10 Discrete Distributions


• Batch Distribution Fitting for all Continuous and Discrete Distributions (excluding custom)


• Nonnormal Process Capability for all Continuous and Discrete Distributions (excluding custom)


• Percentiles to Parameter Calculator


• Specify Input Correlations


• Excel Formula Interpreter for Accelerated Calculations


• Sensitivity Analysis based on correlation or stepwise regression


• Optimization using Input Controls:
  
  
  • Mixed Integer Distributed Ant Colony Global Optimization (MIDACO)
  
  
  • Genetic Algorithm (GA) Global Optimization
  
  
  • Sequential Quadratic Programming (SQP) for Fast Local Optimization
  
  
  • Nelder-Mead (NM) simplex optimization for fast non-smooth problems
  
  
  • Exhaustive Discrete optimization - all combinations for small discreet problems
  
  
  • Powerful hybrid of the above 3 methods:
  
  

  1. All discrete control: MIDACO or Exhaustive Discrete if applicable

  
  

  2. All continuous controls: MIDACO, GA, followed by SQP or NM

  
  

  3. Mixed continous/discrete controls: MIDACO 1 (Course), MIDACO 2 (Fine), followed by SQP or NM

  
  
  • Specify linear and nonlinear constraints
  
  
  • Stochastic Optimization minimize dpm or maximize Ppk
  
  
  • Insert DSIM function such as Percentiles or Capability Metrics.  These can then be used in constraints
  
  
  • Multiple Response Optimization using Desirability, Weighted Sum or Deviation from Target

  
  

  Common Continuous Distributions:

  
  
  

  • Normal
  • Triangular
  • Uniform
  • Pearson Family (specify Mean, StdDev, Skewness, Kurtosis)
  • Log Normal**
  • Exponential**
  • Weibull**
  • PERT
  • Custom Continuous (Weighted and Unweighted)

  
  

  Advanced Continuous Distributions:

  
  

  Beta	Johnson SB
  Beta (4 Parameter)	Johnson SL
  Box-Cox**	Johnson SU
  Burr**	Laplace
  Cauchy	Largest Extreme Value
  Chi-Squared**	Levy
  Chi-Squared with Scale (3 Parameter)**	Logistic
  Error Function (ERF)	Log Gamma**
  F**	Log Logistic**
  F with Scale (4 Parameter)**	Maxwell
  Fatigue Life**	Non-Central Chi-Squared**
  Fisk**	Non-Central F**
  Folded Normal	Non-Central T
  Frechet**	Pareto
  Gamma**	Power
  Generalized Error	Rayleigh
  Generalized Gamma**	Reciprocal
  Generalized Logistic	Skew Normal
  Generalized Pareto	Smallest Extreme Value
  Half Normal**	Students T
  Inverse Gamma**	Students T with Location and Scale (3 Parameter)
  Inverse Gaussian	Von Mises

  
  
  

  ** denotes with/without Threshold

  
  

  Common Continuous Distributions:

  
  

  • Bernoulli (Yes/No)
  • Binomial
  • Geometric
  • Hypergeometric
  • Logarithmic
  • Negative Binomial
  • Poisson
  • Step
  • Uniform (Discrete)
  • Custom Discrete (Weighted and Unweighted)

  
  

  Stochastic Information Packet (SIP):

  
  

  • Import Stochastic Information Packet (SIP).
  • A SIP is a standard library of data (see ProbabilityManagement.org for more details on this standard).

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