Pareto chart: prioritizing the 20% of causes that explain 80% of the problem

In one line

A Pareto chart ranks the causes (or defects) of a problem from largest to smallest weight, to show with data — not intuition — which handful of causes concentrates most of the impact.

What it is

The name comes from Vilfredo Pareto, an Italian economist who observed in the late 19th century that a minority of the population held most of the land in his country (something like 20% of the people owning 80% of the land). It had nothing to do with factories or quality: it was an observation about the distribution of wealth.

Decades later, the quality consultant Joseph Juran took that observation and named it the "Pareto principle" when applying it to a pattern he saw repeating in industry: in almost any process, a small number of causes explains most of the defects, while the rest — the majority of the causes — barely adds up. Juran summed it up as the difference between the "vital few" and the "trivial many".

In practice, a Pareto chart is a histogram of causes or defects sorted from highest to lowest frequency (or cost), with a line showing the cumulative percentage as the bars are added up. That line is what shows, at a glance, where the "elbow" is, the point beyond which adding more causes contributes little.

What it is for

When several candidate causes show up for the same problem and the resources to tackle them are limited — they always are — Pareto helps you decide where to start. Without that data, the most common move is to invest first in the most visible cause or the one that is most convenient to fix, which is not necessarily the one that weighs the most. The chart forces you to quantify before acting, so you do not spend improvement effort on a cause that explains 5% of the problem while the one that explains 80% is left untouched.

How it is applied

To build a Pareto chart you first need a data record: how many times each cause or defect occurred (frequency), or how much each one cost in money or lost time. Without that prior record — typically a simple check sheet filled in over a defined period — there is no Pareto chart possible, because the tool does not generate data, it only sorts and prioritizes it.

Once you have the data in hand, the procedure has five steps:

1. List the causes (or defect categories) to be compared.
2. Count the frequency or the cost of each one, over the same period of time for all of them.
3. Sort the causes from highest to lowest, according to that number.
4. Calculate the percentage each cause represents of the total, and the cumulative percentage as the causes are added up in that order.
5. Draw the cutoff line at 80% cumulative: the causes that fall above that line are the "vital few" — the candidates to tackle first.

Real example

A case documented in the journal Contaduría y Administración (UNAM) describes a quality circle at the Cerraduras y Candados Phillips plant, in Naucalpan de Juárez, Mexico, which in 1997 was facing a defect of badly made markings on plates: 1.15% of daily production came out with that defect, which required extra polishing on 6 out of every 2,000 pieces produced per day.

The team brainstormed the possible causes and, instead of relying on intuition, measured how much each one weighed on the defect. The result, sorted from highest to lowest — exactly the logic of a Pareto chart — was this:

A single cause — the method used to bend the plates — accounted for 80% of the impact. With that data, the team did not invest in training or in changing the feeder (which together added up to only 15%): it redesigned the bending die, with an investment of $6,578. The extra polishing dropped from 6 pieces per day to zero, beating the goal they had set, with a reported annual saving of $143,284.

The lesson is not just the result: without quantifying, the most likely move was to invest first in training — the "most visible" cause to anyone looking at the plant floor — and leave the real 80% of the problem untouched. (This same case is told in more detail in this site's article on the 7 basic quality tools.)

Template to use

To practice the mechanics of the calculation with concrete numbers, here is an illustrative exercise taken from the handbook of the source cited at the beginning (it is not a real case reported by a company, but a teaching example from the handbook itself): a medical practice that records why insurers reject its billing claims.

The 80% cutoff falls on the third row: 3 of the 7 reasons (43% of the causes, not exactly the "20%" of the rule of thumb) are enough to explain more than 82% of the rejected claims. With that data, the billing team can decide with good judgment where to focus first — for example, tackling the claims filed late before the duplicates, because those rejections are final (the revenue is lost) while a duplicate can still be resubmitted.

So you do not have to build it by hand, there is a Pareto spreadsheet in Excel with the formulas already in place: it sorts the causes on its own, calculates the percentage and the cumulative, marks the vital few and draws the chart. The values it comes with are examples and need to be deleted.

Benefits

• It turns a discussion of opinions ("I think the problem is such and such") into a decision based on data.
• It avoids putting effort into the most visible cause or the most convenient one to tackle when it actually explains little of the problem — the Phillips case is the textbook example of this.
• It is quick to build: a simple spreadsheet is enough, with no specialized software or advanced statistics.
• It is the natural prioritization step after gathering candidates with another tool, such as the Ishikawa diagram: first you open up the range of possible causes, then Pareto tells you which one to investigate first.

Limitations to keep in mind

• It depends entirely on having frequency or cost data already recorded — without a prior check sheet, there is no Pareto chart possible, only a list of causes with no weight.
• The "80/20" rule is an empirical tendency, not a fixed law: there are problems where the impact is spread across several causes and no cutoff this clear appears (as in the medical claims example, where it took 3 causes and not just one to reach 80%).
• It prioritizes, but it does not explain: it tells you which cause to tackle first, not why that cause happens — for that you then need an Ishikawa or a few 5 Whys on the cause that has already been prioritized.
• How the categories are grouped changes the result: categories that are too broad can hide a serious specific cause, and categories that are too fine can dilute a real cause into several small ones that do not look important on their own.

That is why, in practice, Pareto is almost never used alone: it is the prioritization step inside a broader process such as Root Cause Analysis (RCA), once another technique has already generated the list of candidate causes.

Try it with your own data

Enter your own causes and their frequency (or cost). The table sorts itself and highlights the "vital few" as soon as the cumulative crosses 80% — try editing the numbers and watch how the cutoff moves.

In summary

A Pareto chart is nothing more than sorting causes from highest to lowest and adding up a cumulative percentage — but that simple ordering is what keeps you from spreading improvement effort equally across causes that weigh very differently. The Cerraduras y Candados Phillips case shows the clearest extreme of this: a single cause, out of four candidates, concentrated 80% of the problem. Before deciding where to invest time or money in an improvement, it is worth asking whether that decision is based on a real Pareto chart or on the cause that jumps out first.