Ian Rooney
PQ Systems Pty Ltd

Pareto diagrams are a deceptively simple management tool that can be invaluable for analysis and decision making. Although simple to construct and to interpret, Paretos are able to analyse complex issues and clarify the way forward.

A Pareto diagram is a simple bar chart that ranks items in decreasing order of occurrence. The purpose of a Pareto diagram is to separate the significant aspects of a problem or issue from those that are trivial. The Pareto diagram is a graphical representation of data and is similar to a histogramexcept that it monitors items rather than numbers. The diagram is based on the principle that there is an unequal distribution of items in the universe. It is the law of “the significant few versus the trivial many.” For example, there are often many causes of a problem or issue; however, only some of them are significant. This is known as the 80:20 rule: the significant few items make up 80 percent of the problem or issue, while the trivial many make up about 20 percent. A Pareto diagram is used to identify the significant few items. Vilfredo Pareto, an Italian economist and sociologist, who conducted a study in Europe in the early 1900s on wealth and poverty, first developed this principle. Pareto found that wealth was concentrated in the hands of the few and poverty in the hands of the many. Pareto’s principle was named and popularised by Joseph M. Juran in the late 1940s. It was Juran who made the principle a universal concept.

A Pareto diagram, one of the most useful analytic tools, can easily be applied in any industry or setting. It can be used to analyse the causes of a problem or issue, to study the results of a change in a process or system, and to plan for continuous improvement. A Pareto diagram can be used to stratify or divide the data to identify the most significant aspects of a problem or issue. Theories for improvement can then be generated to reduce the significant aspects. After trying the improvement theories, new Pareto diagrams can be used to see if the theories have worked. At this point, the larger bars in the first Pareto diagram should be smaller. For continuous improvement, use the new diagram to make plans to reduce the “new” largest bars.

In some circumstances the number of categories examined is small; that is, fewer than four. In these cases, a Pareto diagram has a strange appearance and may not seem to be useful. An alternative in this situation is to use a pie chart. A pie chart is a way of displaying data pictorially in a circle. The various portions of the data being examined are represented by segments of the circle (like pieces of a pie).

Caution is required when using Pareto diagrams if the system being analysed has not been examined for statistical stability using a control chart. If the system being examined is statistically unstable, output may not be consistent. A wildly fluctuating system will produce inconsistent Pareto diagrams that can lead to misjudgments. Suppose a retail manager failed to note that customer returns varied greatly from month to month. By choosing a month in which returns were unusually high for the analysis, the ranking of categories could be entirely different from those for a month in which returns were unusually low or at normal levels. Repeating the Pareto diagrams can help to confirm the order of the categories. However, the most effective protection against being misled is to first use a control chart to tell if the system is stable and predictable.

To interpret Pareto diagrams start by looking at the chart to identify the categories that are significant compared to those that are trivial. Expect the significant few categories to represent approximately 80 percent of the data in the Pareto diagram. This can be tested by looking at the cumulative percentage line for the first few categories.

The example Pareto shows the number of complaints received by a hotel categorised by type of complaint. In the example, (reminiscent of an establishment run by Mr Faulty?) “Dining room service” and “Dining room food” are the two largest bars. Reading from the cumulative line to the right axis, they represent approximately 80 percent of the data. There is a substantial difference between the first two categories and the remaining bars. So, tackling the first two bars offers substantial advantage.

No matter how data is categorised, it can be ranked and made into a Pareto diagram. Be careful to limit the number of categories to 10 or less. Using too many categories may have the effect of flattening the Pareto so that no single bar is dramatically different from the others. In this situation, no significant advantage is gained by working on the tallest bar. If possible, try to re-categorise the data to see if a clear difference in the bars can be found.

As always, I look forward to your comments and questions. I’m at support@pqsystems.com

Like to know more about pareto diagrams? Please view our seminar schedule for a location near you.