Pareto Chart Example: How to Build One!

The first time I used a Pareto chart example in a quality review was at a contract electronics manufacturer in the West Midlands — power supply assemblies, warranty returns, probably 2018 or early 2019. The quality team had been chasing eight different defect types for three months with roughly equal attention, partly because the production manager felt each one deserved investigation. The chart took about 20 minutes to build from the check sheet data. Two defect types — seal failure and mislabelled batch code — accounted for 71% of all returns. The other six had been consuming most of the investigation time. Nobody had done the calculation before. Pareto analysis in project management and quality settings does exactly that: finds out whether a concentration exists in your data, and if so, where the boundary falls.

What Is a Pareto Chart?

A Pareto chart puts bars in descending order of frequency or impact — tallest on the left, shortest on the right — and overlays a cumulative percentage line that climbs from the first bar to 100% at the right edge. The name comes from Vilfredo Pareto, an Italian economist who noted in the 1890s that around 80% of Italy’s land was owned by 20% of the population. Joseph Juran later applied that observation to quality defects, called it the “vital few and trivial many,” and the chart became one of the standard tools in quality management and Pareto chart in quality management training worldwide.

The left axis carries the count or measure — complaints, defects, minutes of delay, whatever you’re tracking. The right axis carries cumulative percentage, scaled so 100% on the right aligns with the total count on the left. The bars run tallest to shortest. The cumulative line climbs from the top of the first bar to 100% at the right edge. Where it crosses 80% marks the boundary: categories to the left are the vital few.

The chart forces a prioritisation decision that meetings rarely produce on their own. Someone has to commit to working on category A before category B. A Pareto chart makes that argument visible instead of leaving it to whoever talks loudest in the room.

The 80/20 Principle: What It Actually Claims and What It Doesn’t

The 80/20 split is not a mathematical constant. Pareto observed it in Italian land distribution in the 1890s. Juran noticed something similar in defect distributions. In practice the ratio varies considerably — sometimes 70/30, sometimes 90/5, sometimes so flat that eight categories each contribute 10–12% and there’s no meaningful concentration at all. The chart shows whether any Pareto-like pattern exists in your specific data. Sometimes there isn’t one. That’s the answer. It’s still useful.

The flat-chart result is as informative as the steep one. Eight categories each contributing 10–12% means there’s no dominant cause, Pareto prioritisation isn’t the right instrument, and the investigation needs to go somewhere else — possibly looking at which category is most expensive or most fixable rather than most frequent.

A SIMPLE Pareto Chart Example

Let’ s assume that you are a quality control manager of a concrete plant and you tested the strength of all the concrete samples. Concrete strength must be 30 Mpa and you categorized the results as per below table.

In order to draw the cumulative percentage line, you calculated the cumulative values as shown in the table below.

By using the categories (strength) and the number of samples tested you can draw the Pareto chart as shown in the figure below.

Worked Pareto Chart Example

The following pareto chart example uses complaint data from a food packaging facility tracking customer returns over a three-month period. The quality team has recorded 247 complaints across six complaint categories. They want to know where to focus improvement effort first.

Step 1: Raw data

Complaint category	Count	% of total
Damaged packaging	89	36.0%
Wrong product in pack	61	24.7%
Short weight / underfill	43	17.4%
Labelling error	28	11.3%
Foreign material	17	6.9%
Other	9	3.6%
Total	247	100%

Step 2: Add cumulative percentage

Complaint category	Count	Cumulative count	Cumulative %
Damaged packaging	89	89	36.0%
Wrong product in pack	61	150	60.7%
Short weight / underfill	43	193	78.1%
Labelling error	28	221	89.5%
Foreign material	17	238	96.4%
Other	9	247	100%

The 80% line crosses between “Short weight / underfill” (78.1%) and “Labelling error” (89.5%). The first three categories — damaged packaging, wrong product in pack, and short weight — account for 78% of all complaints. Those are the vital few. The quality team should focus improvement effort on those three before addressing labelling errors or foreign material, which together account for only 18.2%.

Reading the chart: the bars show each category’s individual count. The orange line shows cumulative percentage. Where the cumulative line hits 80% — which in this case happens between bar 2 and bar 3 — marks the boundary between the vital few and the trivial many. Three categories to the left of that boundary account for 78% of complaints. Those are where improvement effort should start.

How to Build a Pareto Chart: Step by Step

Start with clean categories

Start with a defined period and a defined measurement. What are you counting — defects, complaints, delays, errors? What time period? The data needs to be complete for the period chosen. Pareto charts built on partial data, or on data collected inconsistently, produce misleading priorities.

The category definitions matter more than most practitioners realise. If “damaged packaging” is a category that catches everything from minor dents to complete package failures, it’ll dominate the chart regardless of whether those cases share a root cause. Before building the chart, make sure each category represents a meaningfully distinct cause or problem type — not a catch-all that accumulates disparate issues under a single label. A “miscellaneous” or “other” category that’s large relative to the rest is a sign the categorisation needs work.

Sort descending — always

Arrange categories from highest count to lowest. Tallest bar on the left. A bar chart that isn’t sorted this way is just a bar chart — the cumulative line loses its meaning if the bars aren’t descending. Excel produces bars in the order you give it the data. Check before you build.

Add the cumulative column

For each category, calculate the running total of counts and express it as a percentage of the overall total. The cumulative percentage for the first bar is just that bar’s percentage. For the second bar, it’s the first plus the second. By the final bar, the cumulative percentage reaches 100%.

Build the chart

Scale the left axis from 0 to the total count. Scale the right axis from 0% to 100%, aligned so that 100% on the right corresponds to the total on the left. Draw bars descending left to right. Plot the cumulative line from the top of the first bar to the top-right corner. Add a horizontal reference at 80% on the right axis — that’s the line the cumulative curve needs to cross before you can identify the vital few.

The 80% line is the point

Where the cumulative line crosses the 80% reference, drop straight down to the x-axis. Everything to the left of that vertical is your vital few. In the worked example, the line crosses between short weight (78.1%) and labelling error (89.5%) — so the first three categories are the vital few. The 80% threshold is a convention. Some teams use 70%, some 85%, depending on how many categories they can realistically act on. The choice should reflect improvement capacity, not aesthetic preference for round numbers. I’ve seen teams pick 90% and end up with five priority categories. Five isn’t a priority. It’s a to-do list.

How to Read the Cumulative Line

A pareto chart example without the cumulative line is just a sorted bar chart. Useful but incomplete. The line is what turns it into a Pareto chart — it converts the bars into a running total that shows what percentage of the problem you’ve accounted for as you move left to right.

Each point on the cumulative line shows the running total up to and including that category. First point: just the first bar’s percentage. Second point: first plus second. Last point: 100%, always. The slope between points is the diagnostic — steep between the first two bars means those categories are doing most of the work; flat means each additional category is contributing less and less.

A steep initial climb — line jumps high for the first two bars, then flattens — means the Pareto effect is strong. A small number of categories dominate. A gradual, nearly straight diagonal from bottom-left to top-right means the opposite: every category is contributing roughly equally, there’s no vital few, and the chart isn’t telling you much beyond what a simple table already shows. Neither shape is a failure. The steep one gives you a clear priority. The gradual one tells you frequency isn’t the right sorting criterion for this problem.

Reading the crossing point

Where the cumulative line crosses the 80% reference line, drop straight down to the x-axis. The categories to the left of that vertical are your vital few — the ones to address first. The categories to the right are the trivial many — not unimportant, but lower priority relative to the resources required to address them.

People often treat the categories to the right of the 80% line as irrelevant. They’re not. If a foreign material complaint occurs once in three months, it accounts for a tiny percentage of total complaints — but if it’s the complaint most likely to result in a product recall, cost and frequency are two different dimensions and the Pareto chart only captures one of them. Risk-weighted Pareto analysis adjusts each bar by severity or cost rather than frequency, which sometimes produces a completely different prioritisation.

When a Pareto Chart Example in Quality Management Misleads

The categorisation problem

The most common analytical error in Pareto chart work is using categories that are too broad. A “damaged packaging” category that includes transit damage, seal failure, incorrect torque on closures, and label adhesion failures will dominate the chart — but it doesn’t tell you what to fix. Each of those subcauses might have a completely different root cause, different department ownership, and different fix cost. A Pareto chart built on coarse categories points you toward the right area. That’s all it does — it identifies that “damaged packaging” is the dominant complaint category, not that transit vibration or seal failure or incorrect cap torque or label adhesion is the specific problem requiring attention. A second-level Pareto chart, built on the subcauses of the dominant category, is where the actual analysis happens. The first chart is positioning; the second is diagnosis.

Frequency isn’t always the right measure

A complaint that occurs 89 times per quarter but costs £2 per incident may be less important than one that occurs 9 times but costs £800 per incident and carries recall risk. Frequency-based Pareto charts underweight rare but high-severity events. Systematically. In food, pharmaceutical, and medical device contexts, a frequency chart should always be supplemented by a cost- or severity-weighted analysis before any resource decision. The frequency chart shows you what happens most often. The cost chart shows you what hurts most.

What inconsistent data collection does to the chart

The chart reflects the data collection process as much as the underlying problem. Different shifts recording complaints using different criteria, categories that are easier to log than others, a time period that captured one unusually bad batch: these all produce charts that look clean but reflect collection artefacts rather than real process performance. Before using pareto analysis in project management or quality review as the basis for a resource decision, spend ten minutes with whoever collected the data and ask what might have been missed or double-counted. It’s a better use of time than building the chart and then defending its assumptions in the review meeting.

When no Pareto pattern exists

A cumulative line that climbs at roughly equal steepness across all categories means no Pareto pattern exists in this data. No dominant cause. No vital few. Nothing to prioritise using frequency. When the chart gives you that result, different prioritisation criteria have to take over — severity, cost to fix, strategic importance, whatever fits the decision being made. The Pareto chart has told you something useful: frequency alone won’t determine your priority order.

Pareto Chart as One of the Seven Basic Quality Tools

The Pareto chart in quality management is one of the seven basic quality tools, a set of graphical techniques compiled by Kaoru Ishikawa in the 1960s. The seven tools are: control charts, cause-and-effect (fishbone) diagrams, check sheets, histograms, scatter diagrams, flow charts (or stratification charts), and Pareto charts. Pareto charts answer one question: where do we focus first?

In practice, the Pareto chart and the cause-and-effect diagram work closely together. The Pareto chart identifies which problem category to focus on — damaged packaging, in the worked example above. The cause-and-effect diagram then structures the root cause analysis for that specific category, exploring the machinery, methods, materials, measurement, environment, and people factors that might explain why packaging is being damaged. The Pareto chart narrows the field to one category. The fishbone diagram opens it back up within that narrower space. For more on cause-and-effect analysis, see the scatter diagram article for how correlation tools complement Pareto prioritisation.

For a broader view of how Pareto analysis fits within quality management improvement cycles, the ASQ Pareto chart resource provides additional worked examples and guidance on chart interpretation across different industries.

Two Very Different Pareto Chart Patterns

The shape of the cumulative line is the key diagnostic. A steep rise followed by a plateau signals a strong Pareto effect — a small number of categories dominate. A near-straight diagonal signals a flat distribution where no category dominates. These two shapes call for completely different responses. The first calls for targeted intervention on the vital few. The second calls for a different analytical tool altogether — the Pareto chart isn’t the right instrument when everything contributes roughly equally.

Frequency-based vs severity-weighted: a different pareto chart example

This is where pareto chart analysis in quality management gets more nuanced. The same data produces different charts depending on whether you weight by frequency or by severity (cost, risk, customer impact). In the food packaging example, foreign material ranks fifth by frequency — but if each foreign material complaint carries an average investigation cost of £4,200 versus £140 for damaged packaging, the cost-weighted Pareto looks completely different.