Category 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 […]

Failure Modes and Effects Analysis (FMEA)

Failure Modes and Effects Analysis (FMEA) is a form of risk analysis that should be applied when designing or changing products and processes.  FMEA is a methodical seven step approach to analyzing a product or process for ways that it can fail, and then taking appropriate actions based on this information.   We will explain the […]

Overall Equipment Effectiveness (OEE)

A metric that is commonly used in Lean Six Sigma is Overall Equipment Effectiveness, or OEE. OEE can be used to help target key areas for improvement. It is a way to measure equipment performance in three major areas where losses or waste may occur. Although OEE is most commonly applied in manufacturing, it can […]

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. […]

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 […]

Taguchi Designed Experiments

Classical Designed Experiments (DOE) were discussed in a previous article in this series.  In the last article we discussed descriptive statistics that describe the center of an output or response (mean, median and mode) and the amount of variability in the output or response (range, standard deviation and variance). Taguchi Designed Experiments are an alternative […]

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.  […]