Manufacturing · Live POC

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.

Supply Risk Intelligence — Pragyan Ai

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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.

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.

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