# Tag Archives: Six Sigma

## Evaluating Gage Bias and Linearity

In an earlier article in this series, I introduced some basic issues in measurement systems analysis such as granularity, accuracy, reproducibility, repeatability, stability and linearity.  In this article we will explore in more detail how to evaluate the performance of a measurement system with respect to bias and linearity. Gage bias examines the difference between […]

## Issues in Process Control

Earlier in this series I introduced the topics of Process Capability and Process Control. You may wish to review that article before reading this one. I recently received the following query from one of my Green Belt students: “In my company our manufacturing process has a lot of special variations which makes the process very unpredictable. […]

## The Kano Model

In a previous article in this series we discussed a number of tools that can be used to do a better job of capturing the Voice of the Customer regarding requirements. Today we will look in more detail at one of those tools, the Kano Model. This model was developed by Dr. Noriaki Kano, a […]

## The Theory of Constraints and Six Sigma

When practicing Six Sigma or any other form of process improvement, it is important to focus improvement efforts where they will do the most good. Of all of the things that we could work on, what should we work on in order to return the most benefit to the organization and the customers of the […]

## Shainin Methods

Previously we discussed Taguchi Methods as an alternative to classical Designed Experiments. Another approach to experimentation is Shainin Methods, as developed by Mr. Dorian Shainin and strongly advocated by Mr. Keki Bhote. The late Dorian Shainin began introducing a series of experimentation techniques in the 1930’s and continued his work for nearly 50 years. Keki […]

## Descriptive Statistics

In the Analyze phase of Six Sigma, we take the data that we collected in the Measure phase and try to make some sense of it.  We use statistical thinking and analysis to prove or disprove our hunches about what is going on in a process, and to identify the true root cause of a […]

## Estimating Population Parameters from Samples – Point Estimates and Confidence Intervals

In statistics, a data sample is collected or selected from a larger population.  The population includes all of the data that are of interest.   Populations are often so large that it is impossible or impractical to collect data about the entire population.  We therefore rely on samples to make inferences or estimates about the population.  […]

## Designed Experiments – A High Level Introduction

In an earlier article, I discussed the transformation function Y = f(X), where Y represents the output of a process.  The output is a function of the inputs (the X’s) that affect the output. Designed Experiments are used to help us quantify the effect that the inputs have on the output of a process.  In […]

## The Transformation Function: Y = f(X)

Six Sigma is focused on improving the performance of processes.  In simple terms, processes have inputs, activities, and outputs.  The process activities transform the inputs that come from suppliers into a product or service that is delivered to a customer. Consider baking a cake.  The inputs are ingredients such as eggs, flour, milk, butter and […]

## Statistical Software – A Trap for the Unwary

Statistical software is widely used in practice to conduct various statistical tests and to generate various graphs and charts.  In our Six Sigma courses we use Minitab, which has been under continuous development since 1972 and is one of the most widely used statistics packages.  There are many other fine products on the market as […]