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AI in Manufacturing10 June 2026 · 1 min read · Vinayak Raizada

From metering to prescriptions: the intelligence gap

Most plants have meters. Few have intelligence that tells a shift supervisor what to do tomorrow morning.

prescriptive AImeteringSME manufacturing
From metering to prescriptions: the intelligence gap

Data abundance, action scarcity

Walk into any auto component plant and you'll find meters, SCADA screens, and monthly Excel exports. Ask the plant head what to change tomorrow to save ₹50,000 this month, silence.

The gap isn't data collection. It's translation to action.

Three layers that matter

  1. Connect, incomer, compressors, furnaces, bills in one model

  2. Observe, SEC, MD, and non-production load baselined by shift

  3. Prescribe, ranked actions in rupees with payback days

Analytics that stop at layer 2 are expensive wallpaper. Layer 3 is where payback lives.

Why SMEs can move faster than conglomerates

Enterprise EMS deployments take 18 months and six consultants. An SME with one incomer and three problem assets can pilot in 2–3 weeks:

  • Week 1: connect data

  • Week 2: baseline

  • Week 3: first prescriptions verified against meter trend

Speed is the advantage. Use it.

Start here

Pick one asset class, compressors, furnaces, or shift-start MD. Baseline it for 30 days. Issue three prescriptions. Verify on the bill.

That's the intelligence gap closing in practice, not theory.

See it on your plant

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