What Is A Pareto Chart? Definition & Examples

Pareto charts are a common tool used by manufacturers to analyze quality and defect data, providing a simple visual representation as to the frequency of certain issues and the cumulative percentage of their occurrence.

In this post, we'll discuss the value of pareto charts in a manufacturing setting and how they're being used to more easily evaluate quality issues on the shop floor.

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A Pareto Chart is a graph that indicates the frequency of defects, as well as their cumulative impact. Pareto Charts are useful to find the defects to prioritize in order to observe the greatest overall improvement.

To expand on this definition, let’s break a Pareto Chart into its components.

1) A Pareto Chart is a combination of a bar graph and a line graph. Notice the presence of both bars and a line on the Pareto Chart below.

2) Each bar usually represents a type of defect or problem. The height of the bar represents any important unit of measure — often the frequency of occurrence or cost.

3) The bars are presented in descending order (from tallest to shortest). Therefore, you can see which defects are more frequent at a glance.

4) The line represents the cumulative percentage of defects.

Let’s look at the table of data for the Pareto Chart above to understand what cumulative percentage is.

For Collar Defects, the % of Total is simply (10/59)*100.

The Cumulative % corresponds to the sum of all percentages previous to and including Collar Defects. In this case, this would be the sum of the percentages of Button Defects, Pocket Defects, and Collar Defects (39% + 27.1% + 16.9%).

The last cumulative percentage will always be 100%.

Cumulative percentages indicate what percentage of all defects can be removed if the most important types of defects are solved.

In the example above, solving just the two most important types of defects — Button Defects and Pocket Defects – will remove 66% of all defects.

In any Pareto Chart, for as long as the cumulative percentage line is steep, the types of defects have a significant cumulative effect. Therefore, it is worth finding the cause of these types of defects and solving them. When the cumulative percentage line starts to flatten, the types of defects do not deserve as much attention since solving them will not influence the outcome as much.

5) In manufacturing, Pareto Charts are used as a quality management tool: they help analyze and prioritize issue resolution.

The idea behind a Pareto Chart is that the few most significant defects make up most of the overall problem. We have already covered two ways the Pareto Charts help find the defects that have the most cumulative effect.

First, the first bars are always the tallest, indicating the most common sources of defects. Second, the cumulative percentage line indicates which defects to prioritize to get the most overall improvement.

6) The Pareto Principle can analyze Pareto Charts, also known as the 80/20 rule.

The Pareto Principle states that 80% of the results are determined by 20% of the causes.

Therefore, you should try to find the 20% of defect types that cause 80% of all defects.

While the 80/20 rule does not apply perfectly to the example above, focusing on just 2 types of defects (Button and Pocket) has the potential to remove the majority of all defects (66%).

When it comes time to build Pareto Charts to analyze defects in your production lines, you should not have to open Excel.

With the right frontline operations software, your operations can achieve Quality 4.0 enabling real-time visualizations and reporting to be generated automatically.

Creating a Pareto chart isn’t complicated, but doing it well means following a structured process. Whether you're using Excel, Python, or a modern analytics tool, these five steps will help you go from raw data to a clear visual that highlights your highest-impact issues.

Step 1: Collect and Categorize Your Data
Start with a clean dataset that represents the problems or outcomes you want to analyze. In manufacturing, that might include defect types, downtime causes, or machine failures. Group each observation into consistent categories (e.g., “scratched part,” “missing label,” “loose fitting”).

Avoid over-categorizing. Too many labels can dilute your insights and flatten the curve.

Step 2: Sort Categories by Frequency or Cost
Once your categories are defined, count how often each one occurs—or, if you’re tracking cost, sum the total impact for each. Then sort the list in descending order, putting the most significant issues at the top.

Example:

Step 3: Calculate Percentage and Cumulative Total
Now turn raw counts into percentages to better understand their relative impact. For each category:

• % of Total = (Category Count / Grand Total) × 100

• Cumulative % = Sum of % of Total for all previous categories
  
  

Add these two columns to your table:

Step 4: Plot Bars and the Cumulative Line
With your data prepared, it’s time to build the chart:

• X-axis: Categories (ordered left to right by size)

• Primary Y-axis: Bar height for frequency or cost

• Secondary Y-axis: Line graph for cumulative percentage
  
  

The result is a bar chart topped with a rising line, the visual signature of a Pareto chart. Step 5: Identify the “Vital Few”

Now interpret the chart. The point of the cumulative line is to help you identify the 80/20 cutoff, the smallest number of causes that account for the majority of problems. Often, it’s the first two or three bars that cross the 80% mark. That’s your priority zone.

These are your “vital few”, the issues most worth addressing first.

Example: Pareto Chart for Manufacturing Defects

This example comes from a shirt assembly line. Inspectors tracked defect data for one week to see which problems appeared most often. The focus was on visible quality issues during finishing and alignment.

After the counts were sorted and percentages added, the first two defects, loose buttons and misaligned pockets, made up more than half of the total. Most of the repair work traced back to those areas.

Chart Overview

The bars in the chart show how often each defect appeared. The line shows the cumulative share of total defects.

Description: Pareto chart for shirt assembly quality data. The first two bars, loose buttons and misaligned pockets, are the tallest. The cumulative line rises quickly and then levels off, showing that a few causes create most of the rework.

Other Use Cases Beyond Manufacturing

Pareto charts started in quality engineering, but they fit anywhere you need to see which problems carry the most weight. Any process that produces data like technical, service, or commercial, can benefit from this kind of simple ranking.

Software Bugs
Development teams often use Pareto charts during testing. When hundreds of bugs are logged, the chart helps sort them by category. It’s common to find that a few recurring issues, like authentication failures or layout errors make up most of the test failures. Tackling those first removes the biggest obstacles before launch.

Customer Complaints
Support teams can apply the same idea to complaint data. Calls might cover delays, wrong shipments, damaged goods, and returns. When plotted, the chart often shows that two problem types drive most of the complaints. That makes it easier to focus resources where customers feel the most pain.

Sales Analysis
In sales reviews, Pareto analysis highlights concentration. Often a small group of products generates the bulk of revenue. Seeing that pattern allows teams to double down on high-performing lines and question the value of low-volume SKUs that tie up inventory and attention.

Field Service and Support
Maintenance and service groups use Pareto charts to understand repeat trouble spots. If most callouts come from a few equipment models or component failures, those items become the first targets for preventive work or redesign.

Across all of these examples, the purpose stays the same: make the data show where effort pays off fastest. A Pareto chart doesn’t replace deeper analysis, it helps you decide where that analysis should begin.

How to Interpret a Pareto Chart

A Pareto chart isn’t just for display, it helps you decide where action makes the biggest difference. The key is knowing how to read what the shape of the chart is showing.

Start with the Cumulative Line
The curved line tracks how each category adds to the total impact i.e. defects, downtime, cost, or whatever you’re measuring, as you move from left to right.

A steep climb means those first categories carry most of the weight. That’s where losses or failures are concentrated.
A slower, flatter climb means later categories contribute much less. They’re worth noting, but not where you’ll get major improvement.

Watch for the point where the line starts to level. That’s the transition from the few big hitters to the long tail of smaller issues.

Finding the 80/20 Point
You’ll often see that a small share of causes drives most of the result, usually close to 80%. You don’t need to draw a hard line, just notice where the curve crosses that range. The categories on the left side of that mark are your best starting points for corrective action.

What the Flattening Means
Once the curve starts to level off, each added category represents less of the problem. If you’re looking at items that occur once or twice a month, you’re probably in the zone of diminishing returns. Fixing those won’t move your overall metrics much compared to the main drivers.

Pareto vs. Bar vs. Histogram: What’s the Difference?

While Pareto charts may resemble bar charts or histograms at first glance, they’re used for different types of analysis. This comparison helps readers recognize those differences so they can pick the chart that fits their question, not just the one that looks familiar.

Each gives a different view of performance. Picking the right one depends on what you’re trying to understand, not how the chart looks.

Each gives a different view of performance. Picking the right one depends on what you’re trying to understand, not how the chart looks.

Advantages and Limitations

Pareto charts are widely used because they help teams focus attention where it counts. But like any tool, they come with trade-offs. Knowing both sides makes them far more useful in practice.

Advantages

1. Helps Set Priorities
A Pareto chart quickly shows where the biggest problems are. Sorting categories by size and adding a cumulative line makes it easy to see which few causes are responsible for most of the losses or defects.

2. Clear Communication
The combined bar-and-line layout turns data into something that’s easy to read. Patterns that might be buried in a spreadsheet are obvious in a single view, which helps when explaining findings to people outside the technical group.

3. Drives Focused Improvement
In Lean, Six Sigma, or general performance reviews, Pareto charts give structure to how teams use their time and resources. They help keep improvement work anchored in data instead of assumptions.

Limitations

1. Can Hide Rare but Serious Issues
A chart built only on counts or frequency can overlook problems that happen rarely but have high impact, like a failure that shuts down a line once a year. Those need separate attention.

2. Depends on Clean Categorization
If categories overlap or aren’t well defined, the chart loses meaning. Consistent labeling and clear definitions are essential before you plot the data.

3. Less Reliable with Small Samples
With very limited data, a Pareto chart can exaggerate random variation. In that case, it’s better to collect more data or look at trends across several runs before drawing conclusions.

Tulip Analytics integrates all your operations’ data in one place. All your reports and graphs — including Pareto Charts — will be displayed on dashboards in real-time. That way, you will conduct root cause analysis for the defects that have the most influence on your output.