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The Pareto chart is based on the research of Villefredo Pareto. He found that roughly 80 percent of all the wealth in the Italian cities he investigated was owned by only 20 percent of families. The Pareto principle has been found to apply in other areas, from economics to quality control. However, Pareto charts have several disadvantages.
Easy to do but difficult to fix
Based on the Pareto principle, any process improvement should focus on the 20 percent of the issues that cause the most problems in order to have the greatest impact. However, one of the disadvantages of Pareto charts is that they do not give an idea about the root causes. For example, a Pareto chart will show that half of all problems occur in shipping and receiving. Failure Effect Mode Analysis, Process Control Statistical Charts, Execution Charts, and Cause and Effect Charts are needed to determine the most basic reasons why the main identified problems are occurring.
Multiple Pareto charts may be required
Pareto charts can show where the biggest problems are occurring. However, a diagram may not be enough. To trace the cause of errors back to their source, lowering the Pareto chart levels may be needed. If errors occur in shipping and receiving, a more in-depth analysis and more diagrams are needed to show that the biggest contributor is in order taking and label printing. Another disadvantage of Pareto charts is that as they are created in more detail, it is also possible to lose sight of these causes compared to the others. The 20 percent of the root causes in a Pareto analysis two or three layers below the original Pareto chart must also be compared with each other so that the solution has the greatest impact.
Qualitative data versus quantitative data
Pareto charts can only show qualitative data that can be observed. It simply shows the frequency of an attribute or measurement. A disadvantage of generating Pareto charts is that they cannot be used to calculate the mean of the data, its variability, or changes in the attribute measured over time. They cannot be used to calculate the mean, 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 is not possible to prove the values mathematically. Qualitative statistics are necessary regardless of whether or not a process can stay within a specification limit.