Manufacturing · Live POC

Energy Intelligence Engine

Compares actual plant-wide power consumption against an AI-computed baseline in a 24-hour curve chart — exposing exactly when and where excess energy is consumed. Ranks the top machines by energy waste (kWh/day), quantifies the ₹ annual cost of each anomaly, and generates a prioritized playbook (VFD tuning, compressor staging, load shifting to off-peak). Shows a Plant Energy Index (kWh per produced unit) across 10 plants to benchmark operational efficiency.

Energy Intelligence Engine — Pragyan Ai

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Equipment Failure Prediction Engine

Scores every machine in the plant by failure probability using vibration RMS, bearing temperature, and oil quality signals. Shows a ranked maintenance queue with days-to-failure estimates and a risk timeline chart. Drills into individual machine health cards with sensor trend plots, anomaly markers, and AI-generated root-cause explanations. Tracks MTBF, maintenance cost avoided, and unplanned downtime prevented — with a full energy anomaly view per asset.

OEE & Production Loss Engine

Breaks down Overall Equipment Effectiveness into Availability, Performance, and Quality components for every production line. Waterfall chart maps exactly where production loss is occurring — planned downtime, unplanned stoppages, speed losses, or quality rejects. Ranks lines by OEE score, forecasts OEE for the next 7 days, and identifies the top loss event per shift. Quality tab shows defect rate, scrap, and rework trends with AI-generated corrective actions.

Product Quality Intelligence

Classifies every defect type by count, trend, severity, and Cost of Poor Quality (COPQ in ₹). A plant × week heatmap instantly shows which facilities are degrading. The Predictive Engine forecasts quality scores 7 days forward using ensemble ML models (94% confidence), flags the highest-risk upcoming batches with per-batch failure probability, and outputs an AI prevention plan per defect — from torch voltage tuning to pre-bake material schedules.

Supply Risk Intelligence

Maps every tier-1 and tier-2 supplier on a risk matrix across financial health, geopolitical exposure, logistics dependency, and lead-time volatility. Scores suppliers 0–100 and flags those in critical zones with AI-generated risk narratives. Shows concentration risk by geography, alternative sourcing recommendations, and a disruption impact simulation — estimating ₹ production loss if a flagged supplier fails within 30 days.

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