The seven basic tools of quality

by Matthew Moore on 30th November 2007

Introduction

In 1950, the Japanese Union of Scientists and Engineers (JUSE) invited legendary quality guru W. Edwards Deming to go to Japan and train hundreds of Japanese engineers, managers and scholars in statistical process control. Deming also delivered a series of lectures to Japanese business managers on the subject, and during his lectures, he would emphasise the importance of what he called the "basic tool" that were available to use in quality control.

One of the members of the JUSE was Kaoru Ishikawa, at the time an associate professor at the University of Tokyo. Ishikawa had a desire to 'democratise quality': that is to say, he wanted to make quality control comprehensible to all workers, and inspired by Deming’s lectures, he formalised the Seven Basic Tools of Quality Control.

Ishikawa believed that 90% of a company’s problems could be improved using these seven tools, and that –- with the exception of Control Charts -- they could easily be taught to any member of the organisation. This ease-of-use combined with their graphical nature makes statistical analysis easier for all.

The seven tools are:

Cause and Effect Diagrams
Pareto Charts
Flow Charts
Check sheet
Scatter Plots
Control (Run) Charts
Histograms

What follows is a brief overview of each tool. If you would like to know more or be trained in their use, please get in touch using the form at the top of the page.

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Cause and Effect Diagrams

Also known as Ishikawa and Fishbone Diagrams

First used by Ishikawa in the 194os, they are employed to identify the underlying symptoms of a problem or "effect" as a means of finding the root cause. The structured nature of the method forces the user to consider all the likely causes of a problem, not just the obvious ones, by combining brainstorming techniques with graphical analysis. It is also useful in unraveling the convoluted relationships that may, in combination, drive the problem.

The basic Cause and Effect Diagram places the effect at one end. The causes feeding into it are then identified, via brainstorming, by working backwards along the "spines" (sometimes referred to as "vertebrae"), as in the diagram below:

Basic Cause and Effect Diagram

For more complex process problems, the spines can be allocated a category and then the causes/inputs of each identified. There are several standard sets of categorisations that can be used, but the most common is Material, Machine/Plant, Measurement/Policies, Methods/Procedures, Men/People and Environment –- easily remembered as the "5M’s and an E" –- as shown in the below:

Process Cause and Effect Diagram

Each spine can then be further sub-divided, as necessary, until all the inputs are identified. The diagram is then used to highlight the causes that are most likely a contributory factor to the problem/effect, and these can be investigated for inefficiencies/optimization.

Control (Run) Charts

Dating back to the work of Shewhart and Deming, there are several types of Control Chart. They are reasonably complex statistical tools that measure how a process changes over time. By plotting this data against pre-defined upper and lower control limits, it can be determined whether the process is consistent and under control, or if it is unpredictable and therefore out of control.

The type of chart to use depends upon the type of data to be measured; i.e. whether it is attributable or variable data. The most frequently used Control Chart is a Run Chart, which is suitable for both types of data. They are useful in identifying trends in data over long periods of time, thus identifying variation.

Data is collected and plotted over time with the upper and lower limits set (from past performance or statistical analysis), and the average identified, as in the diagram below.

Example of a Run Chart