Apparent Randomness, Real Pattern Hidden
Micro-stops appeared random: sometimes sensor issues, sometimes pressure problems, sometimes material jams, sometimes operator actions. With 6-8 micro-stops daily from diverse causes, prioritization was impossible.
What Became Visible
Systematic root-cause categorization of 500+ micro-stops revealed: Cause A (pressure issues): 38% of micro-stops. Cause B (sensor drift): 25% of micro-stops. Cause C (material staging): 17% of micro-stops. Other causes: 20%. The first three causes accounted for 80% of micro-stops.
Pareto-Focused Prevention
Resources concentrated on the three largest root causes rather than spreading effort across all micro-stops. This targeted 80% of the problem with focused intervention.
How it worked: Root-cause clustering (Pareto analysis) identified that eliminating three specific causes would eliminate 80% of micro-stops. Prevention effort concentrated on high-impact causes.
Results
vs scattered effort
from Pareto elimination
Micro-stops that appear random often cluster around 3-5 root causes. Pareto analysis reveals which causes to target for maximum impact.
Operational Reality
The 80/20 rule applied perfectly: 80% of micro-stops from 20% of causes. Focused prevention attacked the vital few.