Stamped Energy
Steel rolling mill with glowing hot metal billets on the production line

Plant efficiency

Prescriptive equipment intelligence

The stack that finds energy waste also flags equipment issues early, with owners and evidence. Real-time decision making before trips and waste compound.

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Indicative outcomes

What this pillar is built to move

Indicative ranges when teams act on early energy-linked drift. Aligned with industrial reliability bands (for example 15-25% emergency / PM spend reduction in published prescriptive programs). Your pilot replaces these with verified figures. Not a CMMS or full vibration PdM claim.

10-20%

Unplanned downtime prevented

When early drift prescriptions are closed before a trip

15-25%

Emergency maintenance cost reduction

Fewer rush repairs when issues surface on the energy graph first

Shared

Context for utilities and maintenance

One prescription trail with evidence, not two dashboards

Energy drift often arrives first

Odd load shapes, rising specific energy, and idle patterns appear before a trip. Without a shared prescription, maintenance and utilities talk past each other.

Plant-tuned models

Pre-trained models, fine-tuned on your plant

We start from domain pre-trained models, then train and fine-tune on your plant's actual equipment data. Every plant and every asset has a different baseline. Models keep learning from your operating history so early signs of equipment drift or breakage show up before a trip, with an assigned next action and evidence trail.

Kiln main drive · early vs late

Stamped signalGeneric alert
T-early

40-50% earlier than generic alert systems

Failure

What we do

Where we actually help on equipment intelligence

Real-time intelligence from the same energy graph. Early warnings with owners, not another vibration screen to ignore.

  • SEC and load-shape drift

    Detect rising specific energy and abnormal profiles before a hard failure.

  • Utility asset early flags

    Chillers, compressors, furnaces: approach, unload, and holding signals with context.

  • Cross-department Rx

    Route actions when schedule tradeoffs involve production, utilities, and maintenance.

  • Evidence, not alert noise

    Every finding stays tied to tags, baselines, and cleared outcomes.

Example prescriptions

Illustrative prescription

Chiller approach drift

Inspect chiller approach temperature drift on Bank B before the next peak shift; hold setpoint changes until cleared.

Who
Utilities · maintenance
Impact
Indicative: avoid compounding HVAC kWh and process risk. Plant-specific; not a guaranteed outcome.
Evidence
Tag trend vs baseline; closed when inspection is logged.

Illustrative prescription

Compressor anomaly with energy context

Investigate Bank A specific power rise during unload; check filters and intake before weekend holding.

Who
Maintenance · utilities
Impact
Indicative: catch inefficiency before a trip or MD surprise. Plant-specific.
Evidence
kW per CFM trend vs baseline; closed when work order logged.

Illustrative prescription

Furnace hold without batches

Review soak hold on furnaces 3 and 4 with zero Saturday batches; confirm setback or shutdown with production.

Who
Heat treatment · production
Impact
Indicative: cut idle holding risk and energy waste. Plant-specific.
Evidence
Holding kWh vs schedule; closed when decision logged.

Proof

Verified with evidence

Findings stay tied to the energy graph and cleared outcomes. Not a claim to replace full CMMS or vibration PdM programs.

Ops-cleared ledgerPlant-specific baselinesBill confirmation optional

Same Connect to Improve loop

Equipment findings ride the same Platform loop as energy prescriptions. Improve based on decisions taken and verified outcomes.

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