The Pareto chart is based on the research of Villefredo Pareto. He found that approximately 80 percent of all wealth of Italian cities he researched was held by only 20 percent of the families. The Pareto principle has been found to apply in other areas, from economics to quality control. Pareto charts have several disadvantages, however.
Based on the Pareto principle, any process improvement should focus on the 20 percent of issues that cause the majority of problems in order to have the greatest impact. However, one of the disadvantages of Pareto charts is that they provide no insight on the root causes. For example, a Pareto chart will demonstrate that half of all problems occur in shipping and receiving. Failure Modes Effect Analysis, Statistical Process Control charts, run charts and cause-and-effect charts are needed to determine the most basic reasons that the major issues identified by the Pareto chart are occurring.
Pareto charts can show where the major problems are occurring. However, one chart may not be enough. To trace the cause for the errors to its source, lower levels of Pareto charts may be needed. If mistakes are occurring in shipping and receiving, further analysis and more charts are needed to show that the biggest contributor is in order-taking or label-printing. Another disadvantage of Pareto charts is that as more are created with finer detail, it is also possible to lose sight of these causes in comparison to each other. The top 20 percent of root causes in a Pareto analysis two to three layers down from the original Pareto chart must also be compared to each other so that the targeted fix will have the greatest impact.
Pareto charts can only show qualitative data that can be observed. It merely shows the frequency of an attribute or measurement. One disadvantage of generating Pareto charts is that they cannot be used to calculate the average of the data, its variability or changes in the measured attribute over time. It cannot be used to calculate the mean, the standard deviation or other statistics needed to translate data collected from a sample and estimate the state of the real-world population. Without quantitative data and the statistics calculated from that data, it isn’t possible to mathematically test the values. Qualitative statistics are needed to whether or not a process can stay within a specification limit. While a Pareto chart may show which problem is the greatest, it cannot be used to calculate how bad the problem is or how far changes would bring a process back into specification.