Foundry: Energy and cycle-time optimization with IoT

Foundry | Energy spikes and unstable cycle time hurt profitability

A foundry operation improved process consistency by correlating machine behavior, energy signals, and production outcomes.

The Reality

• Energy consumption was reviewed only at aggregate levels.
• Cycle-time anomalies were not linked with specific process conditions.
• Corrective action lacked real-time trigger points.

The Cost

• Higher unit energy cost without clear root-cause.
• Process instability and output variability.
• Late response to machine drift conditions.

The Fix

Digitize

• Captured asset-level energy and cycle-time signals.
• Mapped process events with output and rejection outcomes.
• Created shared operations-energy monitoring views.

Optimize

• Detected drift patterns and high-consumption windows.
• Prioritized process settings with strongest efficiency gains.
• Guided shift-level corrective actions with data evidence.

Scale

• Set auto-alerts for unusual energy/cycle correlations.
• Built AI recommendations for process stabilization.
• Replicated optimized settings across lines.

The Result

Before: Energy and process losses were visible too late.

After: Better process control and measurable efficiency improvements.

Digitize what you have. Optimize what you can see. Scale what you have earned.