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Sort the vital few from the trivial many. Enter your reason codes and values, the chart sorts by impact, draws the cumulative line, and marks the 80 percent threshold. Built for downtime, scrap, complaints, and any categorical loss.
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FAQ
Because the most frequent issue is rarely the most expensive one. A defect that happens 200 times a month and loses one minute each time is 200 minutes. A defect that happens twice a month but loses three hours each time is 360 minutes. Sort by total impact in the unit that matters.
Two possibilities: either the data collection is too coarse (you grouped causes that should be separate) or the problem genuinely has many independent contributors. Re-bucket the data first. If it is still flat, run a fishbone to organize candidates, then a 5 Why on the highest-confidence ones.
Five to ten is the practical range. More than ten and the chart gets unreadable; bundle the long tail into “Other” once it falls below 5 percent. Watch out for “Other” growing larger than named categories: your reason codes are not specific enough.
Monthly on the things that matter: downtime by reason code, scrap by defect type, customer complaints by category. After a corrective action takes hold, the dominant cause drops and a new one rises. The Pareto re-orders, and you attack the next one.
A histogram shows the distribution of a continuous variable. A Pareto shows discrete categories sorted by impact. Pareto answers “which causes are biggest.” Histogram answers “what is the variation.” Manufacturing teams use Pareto more often because most factory data is categorical.
BUILT BY OPERATORS, NOT CONSULTANTS