Pareto Chart Analysis

Pareto Chart Analysis

A Pareto chart analysis, named after the Italian economist Vilfredo Pareto, is a graphical tool used to prioritize and analyze data based on the principle of the "80/20 rule." The 80/20 rule suggests that roughly 80% of the effects come from 20% of the causes.

A Pareto chart consists of both bar and line graphs, with the bars representing individual categories or factors, and the line representing the cumulative percentage. The categories or factors are arranged in descending order of their frequency or impact, with the most significant ones appearing on the left.

The primary purpose of a Pareto chart analysis is to identify the vital few categories or factors that contribute the most to a particular problem or outcome. By focusing on these critical few, you can allocate resources and efforts more effectively, targeting the areas that will yield the greatest impact.


The steps to conduct a Pareto chart analysis typically involve:

  1. Define the problem or outcome you want to analyze.
  2. Collect relevant data and categorize it into different groups or factors.
  3. Determine the frequency or impact of each category or factor.
  4. Calculate the cumulative percentage for each category or factor.
  5. Create a bar graph with the categories or factors arranged in descending order.
  6. Plot a line graph representing the cumulative percentage on the secondary axis.
  7. Analyze the chart to identify the significant categories or factors that contribute the most to the problem or outcome.
  8. Focus on addressing or improving the vital few categories or factors to achieve the desired results.

Pareto chart analysis is commonly used in various fields, such as quality control, project management, process improvement, and decision-making, to prioritize and address issues effectively. It provides a visual representation of data that enables stakeholders to quickly understand the most influential factors and make informed decisions based on the analysis.

#ParetoAnalysis #DataVisualization #EffectiveDecisionMaking #DataAnalysis

To view or add a comment, sign in

Insights from the community

Others also viewed

Explore topics