The Pareto Chart is a graphical tool to map and grade business process problems from the most recurrent to the least frequent. In other words, this helps to identify the most frequently occurring problems or separate the vital few from the trivial many.

Vilfredo Pareto introduced the Pareto Chart, an economist in Italy who discovered that 80% of Italy’s wealth was owned by 20% of the population. Hence this Analysis also know as 80/20 rule.

The Pareto Chart is a combination of both the Bar chart and line chart. The bar chart indicates the frequency of occurrence or defects, whereas the line graph indicates the cumulative data percentage in descending order.

A Pareto Chart (also known as a Pareto Diagram) is a bar chart that re-orders the categories so they are rank-ordered from the largest total occurrences to the smallest.

In process analysis, this helps identify the most frequently occurring problems or defect – or separate the vital few from the useful many.

The correct ranking for the Pareto chart categories from left to right would be from highest to lowest.

## When You Should Use a Pareto Chart (Pareto Diagram)

• See Pareto Analysis for a fuller discussion.
• When analyzing the frequency of root cause data and you want to focus on the most significant contributors (the vital few).
• When you want to understand broad cause by looking at the component pieces.
• Very effective way to share a lot of information quickly.

## How to Make a Pareto Chart (Pareto Diagram)

### How to construct a Pareto Chart

The Pareto chart’s primary purpose is to identify the most frequent defects, errors that can be count and categorize. Below are the steps to create a Pareto chart using the Pareto Analysis.

• Determine the categories that wish to monitor or impacts the business
• Establish a measurement system, and also important to use the same measure for all contributors.
• Determine the timeline that data covers for the Pareto for all the categories.
• Collect data for all the categories in two columns, categories (example: type of defects) in the first column and corresponding frequency (total count of each defect category) in the second column.
• Draw a bar graph with categories (example: type of defects) in the x-axis and frequency on the y-axis.
• Re-order the categories in descending order (from largest to smallest categories).
• Then, calculate the cumulative percentage of each category in descending order. i.e Individual defect count/ total defects count *100.
• Draw another vertical axis on the right side of the graph with values from 0 to 100%.
• Plot the cumulative percentage of each category on the x-axis.
• Now join all the points, it will form a curve.
• Analyze the graph. Identify the top 80% on the Y-axis. This point on the x-axis divides the vital few defects (left side) and trivial many defects on the right side.

## Example of a Pareto Chart in the DMAIC Process

Example: A XYZ group runs franchise hotels across USA. Hotel management decided to collect customer stay experience from top eight revenue generated locations via online survey. Team analysed the results using Pareto analysis.

Following are the results from eight locations

#### How to Make a Pareto Chart in Excel

Step 1: Write “Complaint Type” in cell B3 and “Count” in next cell i.e C3. Below them, enter all the customer complaint categories and enter their respective total count in the column C.

Step 2: Select the whole range (including titles). Go to Data ==> select sort make sure that “My list has header row” is checked. In “Sort By” select “Count” and in “Order” drop-down select “Largest to Smallest”, then click on “Ok”.Now it will sort the count in descending order.

Step 3: Add the total categories count in cell C12. Then add the “Cumulative percentage” column next to the Count column and calculate the cumulative percentage as shown as per the formula in D4 cell, and drag D4 below rows to apply the same formula for all the categories.

Step 4: Again, select complaint type, count, and cumulative percentage columns including titles.

Step 5: Go to Insert ==> Chart. Make sure that “Column” is selected as chart type, and and then select “Clustered column” chart from 2-D Column chart.

Step6: Then, from the two bar graphs, click on the cumulative percentage bar graph and right-click on it ==>select “change series chart type”.

Step 7: On the “Change Chart Type” ==> Choose “Line with a marker” chart under Line chart and then clike on “ok”.

Step8: Select the line graph and right-click on it ==> select “Format data series”

Step 9: Under “Series Option” ==>select “Secondary Axis” radio button . Also right-click on the secondary axis ==>select “Format Axis”. Under “Axis Options” change the “Maximum” option to “Fixed” and then enter the value as 100.

Step10: Add titles on the chart and also include axis labels. Analyze the data: Identify top customer complaints. In other words, use the Pareto Analysis i.e 80% of customer complaints.

#### Interpret the Pareto graph:

From the above diagram, it is evident that the top three categories (Bad smell, no hot water, and also breakfast food quality) contribute 80% of customer complaints.

## Pareto Chart (Pareto Diagram) Example

It can be easily created by tallying a check sheet, making a bar chart out of it, and re-ordering it.

## Pareto Analysis

Pareto Analysis is a way of looking for the most common contributing causes to a situation. Using a Pareto chart to perform graphical analysis on your data can help you identify the biggest drivers to your process and appropriately prioritize your actions.

### Analysis with the Pareto Principle

Often called the 80-20 rule,the Pareto Principle is a common ‘rule of thumb’ that 80% of the effects of something can be attributed to 20% of the drivers.

### Pareto Analysis Principle Example:

• Profits – Many businesses discover that 80% of their profits are driven by 20% of their products. Thus it makes sense for them to focus on that 20% of those customers because that gives them the best chance to drive profits.
• Errors -Sometimes you can see that one aspect of your process is responsible for delivering 80% of your errors. If you fix that one process, you can achieve outsized results.

## What if your data doesn’t show an 80-20 split?

Don’t worry. This happens all of the time in the real world.

### Analyzing Multiple Groups in a Pareto Chart

Sometimes it makes sense to sum a few main items make 80%. This is a good strategy if you have one or 2 main drivers like so:

If you get 2 or 3 main drivers, then you can decide if you want to work on all of them or just one. You might decide that for your current project it makes sense to work just one and lock in the savings with that improvement. Then you can attack the others after you have that win. Reducing your project’s scope is often a good strategy for when you have a short timeline.

Other times it might be very expensive to fix the root cause of an issue. In that case, I’d recommend creating a cost benefit analysis to see if your project can generate the savings necessary to make doing the fix worth it. If it is, great! If not, you might see if you can either change your process to avoid the situation or focus on the other items.

### What if the Pareto Chart Is Flat? (Or, what if nothing pops on the chart?)

Sometimes the chart doesn’t show one item or even a few items as outsized drivers in a process. Sometimes there is very little to distinguish one group from another.

This usually indicates that the variable you are investigating isn’t the right driver for the relationship. Try different categorizations or a different data attribute in your analysis.

## What to do when the ‘Other’ or ‘NA’ category is the biggest

This is a huge pet-peeve of mine. Sometimes people will not label their data correctly or at all. Then, when you make a Pareto chart, the other category or NA is the biggest one on the chart.

This reflects a lack of data. The only course of action is to go back and correctly label your data.

#### Pareto Analysis Example: 80% of defects as other.

Let’s say that a software development agency was mapping out the root causes of bugs in their software. Some bugs were found to be errors in the data, others in the user interface code, and still others were missed requirements. But in their error tracking software people did not select the right root cause and the system defaulted to ‘other.’

When the team tries to do process improvement, they are faced with a graph that looks like this:

Sure, the 3 categories are represented, but that ‘other’ category dwarfs them.

It makes no sense to ignore the ‘other’ category. It’s just too big.

The only appropriate thing to do is to go through each of the tracked bugs and update the status to the appropriate category.

Have you performed Pareto analysis? What patterns have you seen?

## Six Sigma Green Belt Pareto Chart (Pareto Diagram) Questions

Question: When in the process of trying to identify the Critical X’s for a LSS project a Belt creates a(n) _____________ because frequently it is 20% of the inputs that have an 80% impact on the output.

(A) Pareto Chart
(B) FMEA
(C) Np Chart
(D) X-Y Diagram

Question: Which of the following tools can be used to identify and quantify the source of a problem?

(A) Affinity diagram
(B) Control chart
(C) Pareto chart
(D) Quality function deployment

## Authors

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Kelvin Murray says:

What does a Pareto chart tells us?

bconard1@verizon.net says:

A pareto chart tells us those occurances that make up 80% of the cumulative total which are the “significant many” to focus on identifying root cause.

Kelvin Murray says:

A bar graph representation of a frequency distribution is commonly referred to as what?

bconard1@verizon.net says:

A bar graph is commonly referred to as a histogram

Kelvin Murray says:

What should a well constructed histogram reveal to you?

bconard1@verizon.net says:

A well constructed histogram should tell you categories that make up the total of all occurances and the rank order of those categories.

villargi says:

Hello Ted,

Thanks for the post. Just wondering myself, why 80-20 and not 70-30 for instance? I see it is useful to prioritize and also to detect we’re not addressing the right attributes, but why these numbers, may it come from observation in nature, experimental?

All the best,
Ignacio

Good question, Ignacio.

Here’s a quote that I found on the history:

“80-20 Rule Background
The 80-20 rule—also known as the Pareto principle and applied in Pareto analysis—was first used in macroeconomics to describe the distribution of wealth in Italy in the early 20th century. It was introduced in 1906 by Italian economist Vilfredo Pareto, best known for the concepts of Pareto efficiency.

Pareto noticed that 20% of the pea pods in his garden were responsible for 80% of the peas. Pareto expanded this principle to macroeconomics by showing that 80% of the wealth in Italy was owned by 20% of the population.

In the 1940s, Dr. Joseph Juran, prominent in the field of operations management, applied the 80-20 rule to quality control for business production. He demonstrated that 80% of product defects were caused by 20% of the problems in production methods. By focusing on and reducing the 20% of production problems, a business could increase its overall quality. Juran coined this phenomenon “the vital few and the trivial many.””