RightChain Paretos

Pareto charts are one of the many types of visualizations, available on the RightChain AI platform. Pareto charts display the cumulative contribution of increasing numbers of observations (e.g. products, customers, supplies, etc.), with observations ranked from highest to lowest contribution (i.e. in descending order of contribution). Each new visual, created by a user, appears as a named tab above the visual. The tab name, is the title of the visual provided by the user. A description of the visual, may also be provided by the user. The description appears in the tab's tool tip. To create the Pareto chart, users select an X axis variable, and a Y axis variable. The visual is generated automatically. The user may also choose the statistics that appear below the visual. Lastly, Pareto Settings allow users to customize the display, including the observation sort direction (ascending or descending), cutoffs for ABCD stratifications, and the inclusion or exclusion of label field names.

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Fundamentals of Pareto Charts

A Pareto chart is a type of bar graph that is used in statistical analysis to represent the frequency of various causes and to highlight the most significant factors in a dataset. It is a visual tool widely used in quality control and decision-making processes. The Pareto chart is based on the Pareto principle, also known as the 80/20 rule, which was proposed by Vilfredo Pareto, an Italian economist. The principle suggests that for many events, roughly 80% of the effects come from 20% of the causes. Here’s how a Pareto chart is typically structured and used:

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1. Bar Graph: The chart includes bars, each representing a specific cause or type of problem. The bars are ordered from the highest frequency to the lowest.

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2. Cumulative Line Graph: Overlaying the bars is a line graph that represents cumulative totals of the frequencies. This line helps to visualize the point where most of the effects come from a minority of the causes.

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3. Categories: The bars are categorical variables (e.g., types of defects or reasons for a failure) that are responsible for quality problems.

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4. Frequency: The height of each bar indicates the frequency of the issues, typically measured either by the number of occurrences or by the cost or time impact.

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5. Analysis: The chart helps in identifying the "vital few" causes that need the most attention. These are the categories that appear to the left and are typically the tallest bars, showing that they have the most significant impact.

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6. Decision Making: By focusing on the problems that have the greatest impact, organizations can efficiently allocate resources to address the most critical issues first, thus improving overall quality and efficiency.