Foundry: Energy and cycle-time optimization with IoT
A foundry operation improved process consistency by correlating machine behavior, energy signals, and production outcomes.
Case study map
From scattered execution to one AI Native OS.
This case study is formatted around the business problem, the connected Optiwise operating layer, and the measurable owner outcome.
2 hrs
daily time saved per role
20%
more team capacity unlocked
1 truth
for job, stock, quality, and dispatch
Before Optiwise
Machine signals were not becoming fast action.
A foundry operation improved process consistency by correlating machine behavior, energy signals, and production outcomes.
Connected action
Optiwise converts live machine and worker signals into alerts.
The case study shows how the same job, material, task, document, and status can move through one live operating layer.
Owner outcome
Owners stop being the middleware for every exception.
Teams get clearer ownership, faster escalation, and more reliable decisions without waiting for one person to connect the dots.
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.