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Eston Martz

Six Sigma

Five Critical Six Sigma Tools: A Quick Guide

What they do and why they’re important

Published: Tuesday, September 5, 2017 - 11:02

Six Sigma is a quality improvement method that businesses have used for decades—because it gets results. A Six Sigma project follows a clearly defined series of steps, and companies in every industry in every country around the world have used this method to resolve problems. Along the way, they’ve saved billions of dollars.

But Six Sigma relies heavily on statistics and data analysis, and many people new to quality improvement feel intimidated by the statistical aspects.

You needn’t be intimidated. Although it’s true that data analysis is critical in improving quality, the majority of analyses in Six Sigma are not hard to understand, even if you’re not very knowledgeable about statistics.

Familiarizing yourself with these tools is a great place to start. This column briefly explains five statistical tools used in Six Sigma, what they do, and why they’re important.

1. Pareto chart

The Pareto chart stems from an idea called the Pareto Principle, which asserts that about 80 percent of outcomes result from 20 percent of the causes. It’s easy to think of examples even in our personal lives. For instance, you may wear 20 percent of your clothes 80 percent of the time, or listen to 20 percent of the music in your library 80 percent of the time.

The Pareto chart helps you visualize how this principle applies to data you’ve collected. It is a specialized type of bar chart designed to distinguish the “critical few” causes from the “trivial many,” enabling you to focus on the most important issues. For example, if you collect data about defect types each time one occurs, a Pareto chart reveals which types are most frequent, so you can focus energy on solving the most pressing problems. 

2. Histogram

A histogram is a graphical snapshot of numeric, continuous data. Histograms enable you to quickly identify the center and spread of your data. It shows you where most of the data fall, as well as the minimum and maximum values. A histogram also reveals if your data are bell-shaped or not, and can help you find unusual data points and outliers that may need further investigation. 

3. Gage R&R

Accurate measurements are critical. Would you want to weigh yourself with a scale you know is unreliable? Would you keep using a thermometer that never shows the right temperature? If you can’t measure a process accurately, you can’t improve it, which is where Gage R&R comes in. This tool helps you determine if your continuous numeric measurements—such as weight, diameter, and pressure—are both repeatable and reproducible (the “R&R” of Gage R&R), both when the same person repeatedly measures the same part, and when different operators measure the same part.

4. Attribute agreement analysis

Another tool for making sure you can trust your data is attribute agreement analysis. Where Gage R&R assesses the reliability and reproducibility of numeric measurements, attribute agreement analysis assess categorical assessments, such as pass or fail. This tool shows whether people rating these categories agree with a known standard, with other appraisers, and with themselves. 

5. Process capability

Nearly every process has an acceptable lower and/or upper bound. For example, a supplier’s parts can’t be too large or too small, wait times can’t extend beyond an acceptable threshold, fill weights need to exceed a specified minimum. Capability analysis shows you how well your process meets specifications and provides insight into how you can improve a poor process. Frequently cited capability metrics include Cpk, Ppk, defects per million opportunities (DPMO), and sigma level. 

Conclusion

Six Sigma can bring significant benefits to any business, but reaping those benefits requires the collection and analysis of data so you can understand opportunities for improvement and make significant and sustainable changes.

The success of Six Sigma projects often depends on practitioners who are highly skilled experts in many fields, but not statistics. However, with a basic understanding of the most commonly used Six Sigma statistics and easy-to-use statistical software, you can handle the statistical tasks associated with improving quality, and analyze your data with confidence. 

Discuss

About The Author

Eston Martz’s picture

Eston Martz

For Eston Martz, analyzing data is an extremely powerful tool that helps us understand the world—which is why statistics is central to quality improvement methods such as lean and Six Sigma. While working as a writer, Martz began to appreciate the beauty in a robust, thorough analysis and wanted to learn more. To the astonishment of his friends, he started a master’s degree in applied statistics. Since joining Minitab, Martz has learned that a lot of people feel the same way about statistics as he used to. That’s why he writes for Minitab’s blog: “I’ve overcome the fear of statistics and acquired a real passion for it,” says Martz. “And if I can learn to understand and apply statistics, so can you.”

Comments

Good wrap-up on these tools

This was a good summary of these tools, but in my humble opinion, you left out a very important one: The process control chart, AKA process behavior chart. Without that, you cannot know whether your Pareto chart is any good (what if the largest bar is that large because of a special cause--especially if it's one that's already been addressed). The histogram has little meaning if the data were not homogeneous; if it's a histogram of a time-ordered process, you need to find out whether that process was stable. If it's trending over time or if the process has shifted, the histogram will not be representative. Control charts should be used to baseline the process data, to check the results of pilots, to detect differences after improvements are made; they can also be used with process data in place of one-way ANOVA or t-tests, to find differences between production lines/processes or before-after testing. Unfortunately, a lot of the Six Sigma literature now relegates control charts to the CONTROL phase, and they are treated as an afterthought, when they should be the basis of any good project. I've seen Six Sigma textbooks that would have us running capability studies without running process behavior charts first.