Use Case

Quality Correlation:
Trouvez la cause racine

When yield drops, the root cause is often buried in hundreds of variables process. Manual investigation is slow and subjective. Jemba’s correlation engine finds the hidden patterns that explain quality losses.

Root-Cause Analysis EngineTemperature vs Scrap Rater = 0.87 · p < 0.001Temperature (°C)Top Contributing VariablesExtrusion Temp92%Humidity Zone 378%Pressure Valve A65%Coolant Flow Rate54%Motor RPM42%Ambient Temp31%Raw Material Lot24%Operator Shift18%
La solution

How Jemba solves it

Jemba analyses hundreds of variables process simultaneously, identifying which parameters most strongly correlate with quality outcomes — even when the relationships are non-linear or interact with each other.

  • Analyses 700+ variables process simultaneously
  • Identifies non-linear correlations humans miss
  • Ranks parameters by impact on quality outcomes
  • Actionable insights, not just data visualisation
−22%
rebuts and reprises (aggregate across deployments)
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Related use cases

Predictive Maintenance →Energy Monitoring →

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