Use Case

Quality Correlation:
找到根本原因

When yield drops, the root cause is often buried in hundreds of 工艺变量. 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%
解决方案

How Jemba solves it

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

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

Predictive Maintenance →Energy Monitoring →

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