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Design Analysis Spreadsheet |
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What does it do?  | The design analysis spreadsheet is an MS-Excel™ workbook that has been designed to perform partial derivative analysis and root sum of squares analysis. The design analysis spreadsheet provides a quick way to predict the mean and standard deviation of an output measure (Y), given the means and standard deviations of the inputs (Xs). This will help you develop a statistical model of your product or process, which in turn will help you improve that product or process. The partial derivative of Y with respect to X is called the sensitivity of Y with respect to X or the sensitivity coefficient of X. For this reason, partial derivative analysis is sometimes called sensitivity analysis |
Why Use? 
| The design analysis spreadsheet can help you improve, revise, and optimize your design. It can also:Improve a product or process by identifying the Xs which have the most impact on the response.Identify the factors whose variability has the highest influence on the response and target their improvement by adjusting tolerances.Identify the factors that have low influence and can be allowed to vary over a wider range.Be used with the Solver** optimization routine for complex functions (Y equations) with many constraints. ** Note that you must unprotect the worksheet before using Solver.Be used with process simulation to visualize the response given a set of constrained |
When Use? 
| Partial derivative analysis is widely used in product design, manufacturing, process improvement, and commercial services during the concept design, capability assessment, and creation of the detailed design.When the Xs are known to be highly non-normal (and especially if the Xs have skewed distributions), Monte Carlo analysis may be a better choice than partial derivative analysis.Unlike root sum of squares (RSS) analysis, partial derivative analysis can be used with nonlinear transfer functions.Use partial derivative analysis when you want to predict the mean and standard deviation of a system response (Y), given the means and standard deviations of the inputs (Xs), when the transfer function Y=f(X1, X2, ., Xn) is known. However, the inputs (Xs) must be independent of one another (i.e., not correlated) |
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Six Sigma 
