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Liem Ferryanto

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Designing and Analyzing Experiments with Mixtures

In the development phase of Design for Six Sigma (DFSS), practitioners who work in domains such as medical devices, pharmaceuticals, foods, material composition and semiconductors frequently deal with experiments to determine optimal ingredient mixtures for desired products. The factors in a mixture experiment are the ingredients or components of a mixture, and the response is…

Analyzing Experiments with Ordered Categorical Data

Six Sigma projects in various industries often deal with experiments whose outcomes are not continuous variable data, but ordered categorical data. Analysis of variables (ANOVA) is a technique used to analyze continuous experimental data, but is not adequate for analyzing categorical experimental outcomes. Fortunately, many other methods have been developed to deal with categorical experiments,…


Importance of Tolerance Design in Six Sigma Projects

One of the most popular metrics used in assessing Six Sigma projects, both DMAIC (Define, Measure, Analyze, Improve, Control) and Design for Six Sigma (DFSS), is defects per million opportunities (DPMO). This measurement is the average number of defects per unit observed during an average production run divided by the number of opportunities for making…


Taking Advantage of Computer-Based Analysis for DFSS

The goal in product design or business process engineering is to create products or processes that are insensitive to the sources of variation that inhibit their intended function. The design phase in the product development process is a crucial activity since this is when most downstream production and quality problems are locked-in. As a consequence,…