### Pareto Analysis

In 1950, Joseph M. Juran rephrased the theories of Italian economist Vilfredo Pareto (1848–1923) as the Pareto principle, often referred to as the 80-20 rule. The rule postulates that in any series of variables (problems or errors), a small number will account for most of the effect (for example, 80% of customer complaints come from 20% of customers, or 80% of a company’s profit comes from 20% of products made). Juran referred to the “vital few” versus the “useful many.”

A Pareto chart graphically displays the relative importance of differences among groups of data within a set—a prioritized bar chart. Depicting values from the highest to the lowest in the form of bars (left to right), the Pareto chart has many potential uses for decision making, for example:

• Relative frequency of categories of occurrences.
• Which 20% of sources caused 80% of the errors.
• Relative costs incurred in producing different types of defectives.
• Determining which category or categories should be the focus of improvement efforts.

### For example

Crackers Are Us (CAU) is a fictitious bakery that produces crackers for the consumer market. Crackers are sold to distributors, which sell to retail stores. The product package and the company’s website provide contact information for submitting consumer complaints. CAU’s complaint unit logs every complaint. Overall, complaints (the numbers reflect units) for the past month were the highest on record. The month’s summary is shown in Table 1.A Pareto chart graphically displays the data (see Figure 3). It appears that 78% of the complaints came from 20% of the complaint categories. Further analysis may indicate a need for nested Pareto charts, which are more discrete breakdowns of the top 80% or weighting of the categories by dollar cost.

Ensure the categories chosen are clearly differentiated to avoid overlap. The intention of the graphic is to clarify the data represented. Remember the data source: Garbage in is garbage out.

The Pareto chart is a valuable means for visualizing the relative importance of data.

—Russ Westcott

Bibliography

1. Hartman, Melissa G., “Separate the Vital Few From the Trivial Many,”Quality Progress, September 2001, p. 120.
2. Juran, Joseph M., and A. Blanton Godfrey, eds., Juran’s Quality Handbook, fifth edition, McGraw-Hill, 1999, section 5.20-5.24.
3. Stevenson, William J., “Supercharging Your Pareto Analysis, Frequency Approach Isn’t Always Appropriate,” Quality Progress, October 2000, pp. 51–55.
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