Pareto Chart

We will use one dataset throughout this page: 400 customer complaints logged over a year, sorted into eight categories. The same chart works for defect types, downtime reasons, missed appointments, or rejected claims.

1. The bars, sorted from tallest to shortest. Sorting is what makes a Pareto chart different from a standard bar chart. It puts the largest category first so you can immediately see where most of the volume sits.
2. The left vertical axis. This shows the raw count, running from 0 up to your grand total. In our example, that total is 400—not 152 (the height of the first bar) and not a rounded number like 500.
3. The right vertical axis. This shows percentages, running strictly from 0% to 100%. No other range is valid.
4. The cumulative percentage line. This line shows a running total. Each point adds the current bar's percentage to the sum of the bars before it. The first point sits at 38% (the first bar's share), and the final point reaches exactly 100%.
5. The 80% reference line. This horizontal line runs across the chart at 80%. Look at where the cumulative line crosses it. The bars to the left of that crossing point are the vital few, the ones worth your time. The bars to the right are the trivial many.

The two axes have to agree

The left and right axes measure the exact same data using two different units: raw counts on the left and percentages on the right. When set up properly, the chart is easy to verify. The left axis should end at your grand total (400), and the right axis should run from 0% to 100%. When both scales match, the first point on the cumulative line will sit at the exact same height as the top of the first bar.

You can check any Pareto chart in about ten seconds. Look straight across from the top of the first bar. If the first point of the line sits above or below the top of that bar, the two vertical scales do not match. If the scales do not match, every crossing you read off that chart sits in the wrong place.

The second common mistake is where the cumulative line starts. The first point on the line must equal the first bar's individual share, which is 38% in our data. If the line starts at 0% and only reaches 38% above the second bar, every point on the line has been shifted one category to the right.

When that happens, the line will cross 80% over the fifth bar instead of the fourth. That makes you assign work to five categories when four are enough. One cell fixes it: set the first cumulative value to the first category's percentage rather than to zero.

Where the Other bar goes

If your data includes a catch-all category like Other, Miscellaneous, or Unknown, always place it at the far right. Keep it at the end no matter how tall it is. An "Other" bar is a combination of unrelated issues, not a cause you can investigate and solve, so it cannot be a priority. If you sort it by size and place it near the front, you put an unfocused group at the top of your project list.

In our dataset, Other accounts for 8 out of 400 complaints, which is small enough to set aside. A tall Other bar is a different problem, and it has its own fix further down this page.

How do you calculate the cumulative percentage line on a Pareto chart?

Divide each category's count by the grand total to get its share. Then add that share to the running total of the shares that came before it. Here 152 ÷ 400 = 38%, then 38 + 24 = 62%, then 62 + 13 = 75%, on to exactly 100%.

What is the difference between a Pareto chart and a bar chart or a histogram?

A bar chart shows categories in any order you like. A Pareto chart sorts them tallest to shortest and adds a running-total line, so it answers which few matter instead of how big each one is. A histogram is different again. Its bars are numeric ranges in numeric order, and you must never sort them, because the shape of the spread is the point.

Where does the Other bar go on a Pareto chart?

Last, always, even when it is taller than the bars to its left. Ranking it by height puts a bucket nobody can act on at the front of the queue.

The Pareto chart is one of the seven quality tools. It shows you where to use the other six.

Here is the same dataset of 400 complaints across the same eight categories, with no record re-counted. This time, each category is multiplied by the average cost to resolve it, including refunds, replacement shipments, and administrative time.

Billing errors account for 38% of all complaints but only 11.4% of the total expense. Damaged in transit makes up only 10% of complaints, but it represents 29.0% of the total cost. Shipping damage affects less than one-third as many customers as billing errors, but each case costs nearly ten times as much because you must replace the product, pay for new shipping, and often issue a refund.

Notice what stays the same between both passes. Four categories are still needed to clear 80% in both tables (85.0% by count, and 90.4% by cost). The top three categories by cost reach 79.0%, just under the reference line. The number of priority categories is the same, but the specific categories on that list are completely different. If you only look at the count chart, your team might spend the quarter fixing billing errors while leaving $5,800 in physical damage untouched.

Also notice where Other sits. Its $200 cost is higher than Website error at $120, and it still goes last. That rule holds in both passes.

Cost is not the only alternative metric. When reviewing equipment downtime, sort by total minutes lost rather than by the number of stops, and short, frequent interruptions stop dominating the chart. When analyzing quality defects, rank them by where you caught them: the same defect costs ten times more at the customer than at the machine, as covered in our guide on defect classification.

In healthcare, sorting clinic delays by total patient-minutes highlights different problems than counting individual delayed visits. If your organization measures first-pass yield, you are already weighing outcomes by their operational effect rather than by simple counts.

Suppose your team takes the cost ranking at its word and fixes packaging, which was the largest cost category and only the fourth-most-frequent one. As a result, damage complaints drop from 40 cases down to 8, while all other categories remain unchanged. Here is the updated table sorted by complaint count.

Total complaints decrease from 400 to 368. Total resolution costs drop from $20,000 down to $15,360. That represents an annual savings of $4,640, or a 23.2% reduction in resolution expenses, achieved by addressing a category that ranked fourth on the initial count chart.

Two patterns stand out in the updated table. First, the cumulative curve rises more steeply. Three categories now account for 81.5% of complaints, whereas those same three previously covered 75%. When you resolve one major issue, the remaining categories take up larger shares of the new total. So your vital few gets shorter as you work: three categories now, instead of four.

Second, Damaged in transit and Other both have 8 cases, and Other still goes last. The rule matters here. Both bars are the same height, so without it a problem you have already fixed could outrank a bucket nobody has sorted.

The cost ranking shifts as well. Wrong item is now the top cost category at $5,200, which represents 33.9% of the remaining $15,360 in annual expenses. That becomes the next priority for your team, and you know it only because you drew the chart a second time.

This is why a Pareto chart belongs in a structured improvement cycle. In PDCA (Plan-Do-Check-Act), you use the chart during Plan to pick your target, and again during Check to see whether the bar moved. The DMAIC process uses the tool the same way in Measure and later in Control.

In an A3 problem-solving report, place the original chart on the left to document the initial condition, and the updated chart on the right to show the result. The first chart explains why you chose the project, and the second provides clear evidence that the change worked.