Rank
Categories are ordered from highest to lowest effect so the dominant problems become immediately visible.
Pareto Analysis – Interactive Quality Improvement Tool
Find the vital few issues that create the greatest effect. Build a Pareto from editable quality data, switch between frequency, cost, downtime and weighted risk, identify the priority categories, drill down into a dominant issue, compare before-and-after improvement and turn the result into a structured quality improvement project.
Data → Pareto → Priority → Root Cause → Action → Verify Frequency · Cost · Downtime · Risk Focus effort where it matters
100
Total effect
Scratches
Largest category
35.0%
Largest share
3
Categories to reach threshold
78.0%
Cumulative share
What a Pareto chart tells you
Prioritisation, not root causeShare
Each category is shown as a proportion of the total effect, helping compare relative importance.
Cumulative impact
The cumulative line shows how quickly the ranked categories account for the total problem.
Focus
The chart helps teams choose where investigation and improvement effort is likely to deliver the greatest benefit.
The key principle
A small number of categories often account for a large proportion of the total effect.
This is often described as the “80/20 rule”, but 80% is not a mandatory cut-off and real datasets rarely divide exactly 80/20. The purpose is to expose concentration and support rational prioritisation.
Do not confuse priority with cause. A Pareto may tell you that “dimensional defects” dominate, but it does not tell you why those defects occur. Root-cause methods come next.
Problem-solving sequence
Collect consistent data
Build Pareto
Select priority
Stratify / drill down
Find root cause
Implement action
Repeat Pareto
A strong system closes the loop. The Pareto is repeated after corrective action to demonstrate whether the distribution actually changed.
One dataset can tell four different stories
Frequency ParetoRanks how often something happens. Ideal for recurring defects, QNs, complaints or audit findings.
Cost ParetoRanks financial impact such as scrap, rework, warranty or cost of poor quality. A rare failure can dominate if it is expensive.
Downtime ParetoRanks lost production time. Useful when a small number of fault modes drive availability loss.
Weighted-risk ParetoRanks a defined risk score such as quantity × severity or another organisation-approved weighting. Use with care and document the weighting logic.
Good question:
“Which few categories account for most of the effect we care about?”
“Which few categories account for most of the effect we care about?”
Poor question:
“Which bars fall to the left of 80%, therefore everything else can be ignored?”
“Which bars fall to the left of 80%, therefore everything else can be ignored?”
Project Definition
Define the measure before analysingHow to choose the measure
The same categories may rank differently depending on the measure. Always state what the bars represent and why that measure is relevant to the project objective.
Category Data
Editable source dataFrequency is the number of occurrences. Cost can be total COPQ for the category. Downtime can be minutes or hours, but keep units consistent. Weighted risk is an optional organisation-defined score.
#CategoryFrequencyCostDowntimeRisk
100
Total selected effect
Scratches
Largest category
3
Categories to threshold
78.0%
Cumulative effect at threshold
Interactive Pareto Chart
FrequencyPriority Contribution
These bars show each category's share of the selected measure. Categories are automatically sorted from greatest to least.
Ranked Data Table
Cumulative percentage shown explicitly| Rank | Category | Selected value | % of total | Cumulative % | Priority band |
|---|
Second-Level Pareto / Stratification
Go deeper before jumping to actionIf a high-level category is too broad to act on, break it down into more specific subcategories. Repeat until the data points toward a useful investigation boundary.
Why stratification matters
Example: “Dimensional defects” may be the largest category. A second Pareto may reveal that 60% are bore-diameter failures, and a third stratification may show one machine or tool family dominates those failures.
- Avoid categories so broad they cannot guide investigation.
- Use the same definitions across the dataset.
- Consider time, product, supplier, shift and process-route stratification.
- Do not create so many tiny categories that the signal disappears.
#SubcategoryValue
Drill-Down Pareto
Before vs After Verification
Close the loopEnter the after-improvement value for each category. This helps demonstrate whether the dominant issue was actually reduced and whether another category has now become the priority.
Improvement Assessment
100
Before total
—
After total
—
Total reduction
—
New largest category
Before vs After Pareto
Root-Cause & Action Notes
Effectiveness questions
- Was the same data definition used before and after?
- Was the comparison period long enough to be representative?
- Did the targeted category reduce in absolute terms, not just percentage share?
- Did the total problem rate reduce?
- Has the issue shifted to a different product, process, supplier or failure mode?
- Does the change remain effective over time?
Quality-management context
- ISO 9001 / AS9100: Pareto analysis is a useful method for supporting data-driven evaluation, nonconformity analysis, corrective action and continual improvement. These standards do not prescribe one mandatory Pareto format.
- Seven Basic Quality Tools: Pareto analysis is commonly taught alongside check sheets, histograms, cause-and-effect diagrams, scatter diagrams, control charts and other basic quality tools.
- Structured problem solving: Pareto is especially valuable before deeper root-cause analysis because it narrows the problem boundary and directs effort.
- Customer / organisational requirements: Always apply contractual, sector, customer and internal requirements for data definitions, risk prioritisation and corrective action.
Important: never use a Pareto threshold to override safety, regulatory, airworthiness, product-critical or customer-mandated priorities. A rare catastrophic failure can require immediate action even if it sits at the far right of a frequency Pareto.
Best practice for a defensible Pareto
- Define the question first. State exactly what effect is being ranked.
- Use mutually understandable categories. Avoid overlapping or ambiguous definitions.
- Keep the data period consistent. Mixing unlike periods or processes can create misleading conclusions.
- Use the right denominator. Counts alone may mislead if production volumes differ greatly.
- Choose the measure that matches the objective. Frequency, cost, downtime and risk can produce different priorities.
- Watch the “Other” category. If it is large, break it down.
- Stratify high-level categories. Drill into machine, supplier, product, shift or failure mode.
- Investigate root cause separately. Pareto is not causal evidence.
- Repeat after improvement. Use equivalent data to verify effectiveness.
- Use engineering judgement. Severity and product risk can outweigh frequency.
Frequency can mislead
100 cosmetic scratches may be less important than three failures that scrap expensive hardware or create a safety concern.
Percentages can hide volume
A category can fall from 30% to 20% simply because other problems increased. Track absolute values as well.
Data quality matters
Inconsistent defect coding, duplicate records or a catch-all “Other” bucket can distort the priority picture.
Management Summary
Project Notes
Pareto Analysis Report
Special Processes Institute · Quality Improvement Analysis
1. Executive Assessment
2. Project Definition
3. Key Results
Total effect
Largest category
Categories to threshold
Cumulative share
4. Ranked Pareto Data
| Rank | Category | Value | % | Cumulative % |
|---|
5. Findings & Actions
Observations
Recommendations
Risks / Assumptions
6. Quality Context
Pareto analysis is a prioritisation tool. It helps identify the categories contributing most to the selected effect, but does not itself establish root cause. Apply relevant ISO 9001, AS9100, customer, contractual and organisational requirements together with competent quality and engineering judgement.