Six Sigma, Monte Carlo Simulation, and Kaizen for Outsourcing
Appendix 2: RiskSixSigma Property Function
In an @RISK simulation, the RiskOutput function identifies a cell in a spreadsheet as a simulation output. A distribution of possible outcomes is generated for every output cell selected. These probability distributions are created by collecting the values calculated for a cell for each iteration of a simulation.
When Six Sigma statistics are to be calculated for an output, the RiskSixSigma property function is entered as an argument to the RiskOutput function. This property function specifies the lower specification limit, upper specification limit, target value, long term shift, and the number of standard deviations for the six sigma calculations for an output.
specifies an LSL of .88, a USL of .95, target value of .915, long term shift of 1.5, and a number of standard deviations of 6 for the output Part Diameter. You also can use cell referencing in the RiskSixSigma property function.
When @RISK detects a RiskSixSigma property function in an output, it automatically displays the available Six Sigma statistics on the simulation results for the output in the Results Summary window and adds markers for the entered LSL, USL, and Target values to graphs of simulation results for the output (refer to Figures 2 and 4).
Figure 9: RiskOutput description, examples and guidelines
Appendix 3: The @RISK Developer's Kit
The @RISK Developer's Kit (RDK) is a risk analysis programming toolkit. The RDK allows you to build Monte Carlo simulation models using Windows and .NET programming languages, such as C, C#, C++, Visual Basic, or Visual Basic .NET. Unfortunately, as of the writing of this article, the RDK doesn't support Six Sigma. However, I've been assured that an upcoming release of this development tool will.
Unlike the Excel version of @RISK, the RDK does not require a spreadsheet to run simulations. This means user models can be larger and execute faster. All simulation results can be accessed programmatically, directly in the developer's application. Two powerful charting engines—that of @RISK and Microsoft Excel with its extensive customization capabilities—can be used to generate graphs of simulation results.
RDK applications can be run in a desktop, network server, or web environment. The RDK fully supports multithreading to allow the development of scalable web applications. Models built using the RDK can run simulations and generate results entirely in the user's program or the @RISK Interface can be used to display reports and graphs on simulation results.
Why Use the RDK?
For many users, the spreadsheet is the preferred risk modeling environment. However, many times an application written in a standard programming language needs Monte Carlo simulation or distribution fitting capabilities. The application will have its own user interface, variables, and model calculations. In addition, a developer may want to allow users to access the application over a corporate network or over the Internet.
The RDK allows custom applications such as these to run simulations and generate result graphs and statistics. Applications written with the RDK often will run faster and can contain larger numbers of distributions and outputs when compared with models written in spreadsheet versions of @RISK. This is because RDK applications are compiled and do not contain additional routines executed during spreadsheet recalculation.
On-line Real-time Notification
With the RDK, you also could perform trend analysis and have one or more actions, such as a point outside sigma limits, trigger an emailm or other alert to help isolate the causes of poor performance. Or, you could continuously calculate Cpm or other index on historical data to initiate an alert.
Distribute Custom Solutions Over the Web
The RDK allows you to streamline the distribution of your risk analysis solutions through enterprise-wide web deployment. Server-based risk analysis models—such as corporate financial models, engineering applications, and financial planning tools—can be accessed over the Internet from any browser, allowing users to enter model parameters and inputs, run simulations, and view results and graphs. Model structure, logic, and RDK simulation libraries are stored on the server, ensuring consistency for all end-users and removing local installation and support issues.
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