Maker of the world's leading risk and decision analysis software, @RISK and the DecisionTools Suite
@RISK for Six Sigma

A key application of @RISK is Six Sigma and quality analysis. Industries ranging from engine manufacturing to precious metals to airlines and consumer goods are using @RISK every day to improve their processes, enhance the quality of their products and services, and save money.

Whether it’s in DMAIC, Design for Six Sigma (DFSS), Lean projects, Design of Experiments (DOE), or other areas, uncertainty and variability lie at the core of any Six Sigma analysis. @RISK uses Monte Carlo simulation to identify, measure, and root out the causes of variability in your production and service processes and designs.

Many companies and consulting firms use Monte Carlo simulation in their Six Sigma training and analyses. Palisade is proud to partner with these experts to promote the use of Monte Carlo simulation in Six Sigma.

We’ve trained over 400 people on @RISK in our Six Sigma training program. We aren’t necessarily interested in turning our engineers into statisticians, but we are interested in enabling engineers to use statistics—and @RISK is a great tool for this.
Ernest Lifferth
Director, Design for Six Sigma
Cummins, Inc.

Six Sigma Capability
Metrics in @RISK

@RISK analyzes thousands of different possible outcomes, showing you the likelihood of each occurring. Uncertain factors are defined using over 40 probability distribution functions, which accurate describe the possible range of values your inputs could take. @RISK allows you to define Upper and Lower Specification Limits and Target values for each output, and comes complete with a wide range of Six Sigma statistics and capability metrics on those outputs, like Cpk, Cp, Cpm, and much more. These metrics can be placed directly in your spreadsheet model, or appear in the @RISK Results Summary window. Output graphs show markers for LSL, USL, and Target values. The Industrial edition of @RISK adds RISKOptimizer to your Six Sigma analyses for optimization of project selection, resource allocation, and more.

@RISK Six Sigma metrics

• RiskCp - Calculates Process Potential
• RiskCpm - Can be used when a target value other than the center of the specification spread has been designated as desirable.
• RiskCpk - Calculates Process Capability
• RiskCpkLower - Calculates Process Capability - Upper Spec Limit
• RiskCpkUpper - Calculates Process Capability - Lower Spec Limit
• RiskDPM - Defective Parts per Million
• RiskK - Measure of Process Center
• RiskLowerXBound – Lower X-value for a specified number of standard deviations from the mean
• RiskPNC - Total Percent Nonconforming
• RiskPNCLower - Percent Nonconforming below Lower Spec Limit
• RiskPNCUpper - Percent Nonconforming above Upper Spec Limit
• RiskPPMLower - Defective Parts per Million below Lower Spec Limit
• RiskPPMUpper - Defective Parts per Million above Upper Spec Limit
• RiskSigmaLevel - Process Sigma Level
• RiskUpperXBound - Upper X-value for a specified number of standard deviations from the mean
• RiskYV - Yield Value
• RiskZlower - Z-Score for Lower Spec Limit
• RiskZMin- Process capability when special factors are removed and the process is properly centered
• RiskZupper - Z-Score for Upper Spec Limit

Six Sigma Quick Start in @RISK
To do a quick Six Sigma analysis in @RISK:

1. Add your output cell, and click the properties button in the @RISK Add Output dialog box. Choose the Six Sigma tab, and enter LSL, USL, and Target values (or plug in cell references).

2. Enter in your spreadsheet where you'd like to see Six Sigma metrics. For instance, =RiskCpkUpper(A10) will return the upper limit of Cpk for the output in cell A10.

3. Simulate. Then see Six Sigma metrics directly in your spreadsheet, in the @RISK Results summary window, and on graph markers.

The figure below illustrates how @RISK helps to identify, quantify, and hone in on variation in your processes.

@RISK Industrial edition also includes RISKOptimizer, which combine the power of Monte Carlo simulation with genetic algorithm-based optimization. This gives you the ability to tackle optimization problems like that have inherent uncertainty, such as:

• resource allocation to minimize cost
• project selection to maximize profit
• optimize process settings to maximize yield or minimize cost
• optimize tolerance allocation to maximize quality
• optimize staffing schedules to maximize service

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