Problem Solving & Quality · Quality Tools

Pareto Analysis

Rank defect categories by frequency or cost and plot the cumulative curve: attack the vital few that make most of the loss.

Time30 min
FormatSolo
StageQuality Tools

Pareto Analysis: what it is and why it works

Pareto analysis ranks problem categories by their contribution (count, downtime hours or cost) and plots them as descending bars with a cumulative percentage line. In most processes a few categories account for most of the total loss, and the chart makes them obvious. The team chooses the measure that matters, groups data over a representative period, keeps the 'other' bar small, and identifies the few categories that make up the bulk of the loss, often somewhere around 70 to 80%, though the split varies. Problem solving then starts on the top one or two, and the chart is redrawn after the fix.

Improvement resources are limited, and spreading them across every issue produces little. A Pareto chart turns a long list of complaints into a priority order that is easy to defend. Its main trap is the choice of measure: by count, minor cosmetic defects may lead, while by cost a rare but expensive failure may dominate, so plotting both often changes the decision. It is one of the seven basic quality tools. Check sheets supply its data, stratification shows where the top bar comes from, and an Ishikawa diagram starts the cause analysis on it. A flat Pareto, with bars of similar height, usually signals poor categories rather than a lack of priorities.

What you need

Data on problem occurrences over a representative period
Clear, mutually exclusive categories
A weighting measure: count, hours, cost or customer impact
A spreadsheet or charting tool

What you get

A Pareto chart with bars in descending order and a cumulative line
The vital few categories identified
A decision on which problem to attack first
A baseline chart for comparison after improvement

When to use it

When the improvement effort is spread thinly over every problem at once.

How to do it, step by step

Choose the measure that matters: count of defects, downtime hours or cost — cost often changes the ranking.
Group the data into categories over a representative period; keep “other” small.
Sort categories in descending order and draw bars, then the cumulative percentage line.
Identify the few categories that make up roughly 70–80 % of the total.
Launch problem solving on the top one or two, and redraw the Pareto after the fix to confirm the bar has shrunk.

Worked example: Stoppages on a tissue converting line

Illustrative scenario — figures are realistic but not from a real company.

A tissue converting plant logs 14 categories of unplanned stoppage on its main rewinder line. Over 12 weeks, the maintenance manager counts 1,140 stops and plans to focus on web breaks, the most frequent cause.

By count, web breaks led with 410 stops, ahead of core-loading jams with 260 and glue-system faults with 150.
The team redrew the Pareto by lost production hours, using stop durations from the downtime log. Web breaks averaged about 3 minutes each, while glue faults and log-saw blade problems took far longer.
By hours, the ranking changed: glue-system faults 88 h, log-saw blade problems 61 h, core-loading jams 26 h, web breaks 21 h. The top two made up 64% of the 233 lost hours, and the top three 75%.
A vague 'electrical' category was split into three specific ones so that 'other' stayed under 5% of the total.
Problem solving was launched on glue-system faults, the largest bar by hours.

Result. Glue faults were traced to nozzles clogging after weekend shutdowns. A flush procedure and heated nozzle holders cut glue-related downtime to 22 hours over the next 12 weeks. The redrawn Pareto confirmed that the bar had shrunk, and log-saw blades became the next target. Ranking by count alone would have sent the team after the shortest interruptions.

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Common pitfalls and how to avoid them

Using count when cost is what matters.Chart by the measure that reflects the real loss (cost, hours or customer impact) and compare it with the count view.
A large 'other' bar.Split 'other' until it is small; if it ranks among the top bars, the categories are not useful.
Too short a data period.Use a period that covers normal variation in products, shifts and seasons.
Never redrawing the chart.Redraw it after the fix, with the same categories and a comparable period, to confirm the bar has shrunk.

Frequently asked questions

What is the 80/20 rule in quality?

It is a rule of thumb that a large share of problems, roughly 80% in the classic phrasing, comes from a small share of causes, around 20%. J. M. Juran called this 'the vital few and the trivial many'. The exact numbers vary from case to case; the practical point is that effort should go first to the few categories that make up most of the loss.

Should a Pareto chart use frequency or cost?

Use the measure that best reflects the loss you want to reduce. Frequency is easy to collect but can overweight minor defects, while cost, downtime hours or customer impact often change the ranking. When in doubt, draw both: if the top categories match, the decision is easy; if they differ, the discussion is worth having.

What does a flat Pareto chart mean?

If all the bars are roughly the same height, the categories probably do not separate the problem well. Try grouping by a different factor, such as cause instead of symptom or location instead of defect type, or stratify the data by line, product or shift. A flat chart can also point to a systemic problem affecting every category equally.

Origin

Pareto principle — J. M. Juran applied Vilfredo Pareto’s income observations (1896) to quality as “the vital few and the trivial many” (Quality Control Handbook, 1951).

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