Stamped Energy
Molten metal pour in an energy-intensive manufacturing plant

Energy savings

Load management and energy-efficiency prescriptions

Incomer, sub-meters, SCADA, and bills become ranked actions: what to change, who owns it, monthly rupee impact. Real-time decision making on the loads that move your bill.

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Indicative outcomes

What this pillar is built to move

Benchmark ranges from comparable plants. Your pilot replaces these with verified figures.

15-20%

Typical electricity cost recovery

Process-intensive mid-market plants

15-25%

MD / demand charge reduction

Often from incomer meter and bill data alone

10-20%

Non-production energy flagged

Idle loads, holding, HVAC staging, batch gaps

You already have the meters

Demand spikes and high SEC show up after the fact. The missing piece is an assigned next action before the billing window closes, not another trend chart.

Agentic intelligence

From plant signals to the next best action

An agentic system watches what is happening across the plant: meters, SCADA, bills, and process context. ML models surface data anomalies and load-shape drift. Stamped then ranks what to do next against industry practice for MD, tariff windows, idle waste, and utilities staging, and assigns an owner with monthly rupee impact.

Agentic loop · tap a step

Synced with points

Signals

Incomer, sub-meters, SCADA tags, and bills stream into one energy graph. No hardware retrofit required to start.

What we do

Where we actually help on load and energy

Real-time intelligence on your energy graph. Prescriptions your electrical and ops teams can execute without a hardware retrofit.

  • Maximum demand and co-starts

    Catch overlapping startups and soft-land before the MD window locks in.

  • Tariff and TOD windows

    Shift flexible loads and holding into cheaper slabs when production allows.

  • Idle and holding waste

    Flag compressors, furnaces, and auxiliaries running without production.

  • HVAC and utilities staging

    Stage chillers, AHUs, and shared utilities against process demand.

Example prescriptions

Illustrative prescription

MD co-start stagger

Stagger compressor and furnace restarts after lunch by at least 12 minutes.

Who
Shift supervisor · utilities
Impact
Indicative: lower MD charge exposure in the peak window. Plant-specific; not a guaranteed outcome.
Evidence
Baseline demand curve vs post-action window; ledger entry when cleared.

Illustrative prescription

Idle compressor unload

Cut unload hours on Bank A between press strokes; hold pressure setpoint until production resumes.

Who
Utilities / maintenance
Impact
Indicative: reduce non-production kWh on compressed air. Plant-specific.
Evidence
Unload hours vs production tags; closed when action logged.

Illustrative prescription

TOD holding shift

Move furnace holding into the off-peak slab when Saturday batches are empty.

Who
Heat treatment supervisor
Impact
Indicative: lower tariff-weighted holding cost. Plant-specific.
Evidence
Tariff window vs holding kWh; ledger when cleared.

Proof

Verified with evidence

Potential vs realised impact in an ops-cleared ledger. DISCOM bill confirmation can follow when the period closes. It is optional proof, not the only story.

Ops-cleared ledgerPlant-specific baselinesBill confirmation optional

Same Connect to Improve loop

This pillar runs on the Platform operating loop: Connect, Observe, Decide, Execute, Verify, Improve. Improve based on decisions taken. No separate product to deploy.

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