What is a Pareto Analysis?
Pareto analysis is a ranked comparison of factors related to a quality problem. It helps identify and focus on the vital few factors.
Historical Evolution of the Pareto Analysis
Pareto analysis gets its name from the Italian-born economist Vilfredo Pareto (1848-1923), who observed that a relative few people held the majority of the wealth. Pareto developed logarithmic mathematical models to describe this non-uniform distribution of wealth, and the mathematician M.O. Lorenz developed graphs to illustrate it.
The Pareto Principle
Dr. Joseph Juran was the first to point out that what Pareto and others had observed was a “universal” principle—one that applied in an astounding variety of situations and appeared to hold without exception in problems of quality.
In the early 1950s, Juran noted the “universal” phenomenon that he has called the Pareto principle: That in any group of factors contributing to a common effect, a relative few account for the bulk of the effect. Juran has also coined the terms “vital few” and “useful many” to refer to those few contributions which account for the bulk of the effect and to those many others which account for a smaller proportion of the effect.
As experienced managers and professionals, we intuitively recognize the Pareto principle and the concepts of the vital few and useful many, for we see them in operation in everyday business situations. For example, we might observe that:
- The top 15 percent of our customers account for 68 percent of our total revenues
- Our top five products or services account for 75 percent of our total sales
- A few employees account for the majority of absences
- In a typical meeting, a few people tend to make the majority of comments, while most people are relatively quiet
The principles of the vital few and useful many also apply to RCCA opportunities. Each quality effect that we can observe (for example: quality costs, defects, rework, customer dissatisfaction, returns, complaints, etc.) results from numerous contributors to that effect. When many individual contributors are looked at, it is apparent that only a few account for the majority of the total effect on quality.
For example, when we gather the facts, we might find that:
- In a 25-step manufacturing process, five of the operations account for 65 percent of the total scrap generated
- Of the 12 unique services that our company offers, three of the services account for 82 percent of the customer complaints
- Of the 18 items of information that must be filled in on an order form, four of the items generate 86 percent of the errors found on these forms
In these typical cases, the few (steps, services, items) account for the majority of the negative impact on quality. If attention is focused on these vital few, the greatest potential gain from our RCCA efforts can be had.
The Pareto principle is so obvious and so simple that you might wonder what all the fuss is about. After all, everybody knows that, don’t they? Then why do employees so often hear managers complaining that they are faced with dozens of problems in their organization? And why do employees so often see company task forces listing dozens of problems and setting out to solve all of them simultaneously and with equal vigor?
If you really understood the simple but profound Pareto principle, the first step when faced with a host of problems would be to gather data and facts to identify the vital few. Focus could then be put on attention and improvement efforts on those few things that would give the greatest improvement in quality.
Pareto Diagrams and Tables
Pareto diagrams and tables are presentation techniques used to show the facts and separate the vital few from the useful many. They are widely used to help project teams and steering committees make key decisions at various points in the RCCA sequence.
Regardless of the form chosen, well-constructed Pareto diagrams and tables include three basic elements:
- The contributors to the total effect, ranked by the magnitude of their contribution
- The magnitude of the contribution of each expressed numerically
- The cumulative-percent-of-total effect of the ranked contributors
If you have already studied Stratification, you will notice that a Pareto diagram presents the results of stratifying a problem by one particular variable. The contributors to the effect are the categories for that stratification variable.
A look at the following example of how to construct and use Pareto diagrams and tables will illustrate and further explain these three basic elements.
Example: The “Out of Order” Orders
A project team was chartered to improve the quality of order forms coming in with errors from field sales offices to the home office. There were 18 items on the order form, which we will designate here as items A to R. The team developed a checksheet which it used to collect the frequency of errors on the forms for a week. The results of the team’s study, in the form of a Pareto table, are shown in Figure 14.
Pareto Table of Errors on Order Forms
Note that the Pareto table contains the three basic elements described above. The first column lists the contributors, the 18 items, not in order of their appearance on the form, but rather, in order of the number of errors detected on each item during the study. The second and third columns show the magnitude of contribution—the number of errors detected on each item and the corresponding percentage of total errors on the form. The fourth column gives the cumulative-percent of total. This column is the key to Pareto analysis.
“Cumulative-percent of total” is the sum of percents of total down through each position on the ranked lists. At order-form item J, the cumulative-percent of total is 29% + 25%, or 54%. At Q it is 29% + 25% + 21% + 11%, or 86%.
In other words, the first four items, G, J, M, and Q, account for 86% of the total errors detected in the study. These are the “vital few.”
A Pareto diagram of the same data is shown in Figure 15. Again, note the three basic elements that make up the diagram.
Pareto Diagram of Errors on Order Forms
On the Pareto diagram, the 18 items on the order form are listed on the horizontal axis in the order of their contribution to the total. The height of each bar relates to the left vertical axis, and shows the number of errors detected on that item. The line graph corresponds to the right vertical axis, and shows the cumulative-percent of total.
Note how the slope of the line graph begins to flatten out after the first four contributors (the vital few) account for 86 percent of the total.
The remaining contributors (useful many) will not significantly improve the quality on an individual basis and should be eliminated from the team’s agenda for the time being unless a simple solution is available that will address these categories as a group.
Both the Pareto table and the Pareto diagram are widely used, but the diagram form generally tends to convey much more information at a glance than the table of numbers.
The implications of the Pareto analysis for the project team described above are profound. If the team can find remedies that will prevent errors on the four vital few information items, they can significantly improve the quality of order forms coming in from the sales offices. This is an important point: Without the facts and without a Pareto analysis, the team would be faced with the much larger and more costly task of trying to find ways to prevent errors from occurring on all 18 items. The Pareto table or diagram clearly shows that a significant improvement can be achieved with a much smaller, but more precisely focused, effort.
Pareto analysis leads a project team to focus on the vital few problems or causes of problems that have the greatest impact on the quality effect that the team is trying to improve. In Pareto analysis, facts are gathered and attempt to find the highest concentration of RCCA potential in the fewest projects or remedies. These offer the greatest potential gain for the least amount of managerial and investigative effort.
Example: Cost of Poor Quality in an Automobile Transmission Plant
In our next example, managers at an automobile transmission manufacturing plant used a Pareto diagram to analyze data from the plant’s Cost of Poor Quality accounting system. See Figure 16. The goal of the analysis was to identify the vital few cost categories and to form quality improvement teams to pursue cost reductions. The Pareto diagram clearly shows that a few categories account for the bulk of overall cost of poor quality in the plant.
Annual Cost of Poor Quality
While the diagram in Figure 16 does serve the purpose of prioritizing the cost categories, it is not clear from the diagram how many categories should be included in the “vital few.” Should the managers concentrate on two? On four? On five? If the team had included a cumulative-percent-of-total graph, or a cumulative-percent-of-total column in the superimposed Pareto table, the vital few would have been easier to identify.
Example: Medical Center Customer Survey
Like other improvement tools, Pareto analysis is equally useful and effective outside of manufacturing applications. For example, an improvement team at a large medical center was formed to look into causes of patient dissatisfaction. A preliminary list of 23 likely causes of dissatisfaction was put into questionnaire form, and patients were surveyed.
This next illustration is a Pareto diagram of the analyzed data. Of the 23 surveyed potential causes of patient dissatisfaction, six were found not to be contributors; thus, Figure 17 shows only 17. Of the contributors, the one that the team expected to show up as the leading cause of dissatisfaction (waiting room time) generated fewer responses than three other contributors. Most importantly, the ranking of “telephone access” (i.e., difficulty getting through to physician or having to wait long) as the leading cause of dissatisfaction was unexpected, and its dominance among the vital few led the team to undertake a Pareto analysis of causes contributing to the access problem itself.
Example: Process Steps in Manufacturing an Integrated Circuit
In the example in Figure 18, a project team at a semiconductor manufacturing plant used Pareto analysis as part of their diagnostic journey. An earlier Pareto analysis had revealed that 59 percent of certain operators’ time was spent straightening bent leads on integrated circuit packages prior to shipment. The team conducted a study in which all integrated circuits were inspected for bent leads, before and after each manufacturing process step. The aim of the data gathering and analysis was to determine which of the seven process steps were contributing to the bulk of total bent leads. Figure 18 shows the results of the study.
Pareto Chart Bent Leads Problem
The team found that while bent leads could occur at any of the seven process steps, three of the steps (electrical testing, lead clipping, and hermetic testing) accounted for 75 percent of all the bent leads observed. A simple change in the design of test equipment dramatically reduced the number of bent leads and yielded a 40 percent improvement in productivity.
Pareto analysis can be applied to customer problems as well as to cost-related problems.
Example: Reasons for Delayed Shipments
In Figure 19, a project team in a major chemical company set out to improve customer service. The team determined that a major source of customer dissatisfaction was delays in shipping, and they constructed a cause-effect diagram listing some 13 theories about potential causes for the delays. Next, the team conducted “autopsies” (that is, detailed analyses of actual cases of failure) on a set of delayed orders, and they classified the reasons for the delays into 13 categories. The number of delayed orders in each category was then counted to produce the Pareto diagram shown in Figure 19. Note that in this case only the four leading causes, the vital few, are named in the figure; the other nine are represented as a group in the bar labeled “useful many.”
Improvement in Customer Service
While all 13 theories about the causes for delays were correct (i.e., there was at least one example for each theorized cause), the top four categories, Order-Promising Errors, Off-grade Production, Laboratory Approval Delays, and Pooling, accounted for 80 percent of the delays. Cause-effect diagrams, “autopsies,” and Pareto diagrams are often used together, as illustrated in this example, to separate the vital few root causes of a problem from the others.
Since the nine other causes (“useful many”) were lumped together and plotted as a single bar accounting for 20 percent of the delayed orders, the reader can conclude that the four vital few together account for 80 percent of the delays. But while the cumulative-percent of total can be deduced from this type of chart, it is not as clear as on charts with superimposed line graphs or other notations. Furthermore, although using a “miscellaneous” or “all other” category is sometimes helpful for presentation purposes, it may obscure the basic point that while there are many other causes, their individual contributions are so small that it is not profitable to deal with them.
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