Manufacturing
Smart Factory Orchestration
Predictive maintenance platform connecting 48 plant floors with real-time ML-driven operations intelligence.
35%
Downtime Reduction
48
Plant Floors
3×
Prediction Accuracy
12M
Events / Day
35%
Downtime Reduction
48
Plant Floors
3×
Prediction Accuracy
12M
Events / Day
Measurable Results
Business Impact
Context
The Challenge
A manufacturing conglomerate faced unplanned downtime costing millions across distributed production facilities with fragmented sensor data and reactive maintenance.
“Every hour of unplanned downtime costs us six figures. We needed to see problems before they became failures.”
Aging Equipment
Legacy machinery across 48 facilities with limited sensor coverage and no centralized monitoring.
Reactive Maintenance
Maintenance schedules based on calendar intervals rather than actual equipment health signals.
Isolated Plant Data
Each facility operated independent SCADA systems with no cross-site analytics capability.
Our Approach
The Solution
Integrated IoT sensor networks with ML pipelines for predictive maintenance, production optimization, and digital twin simulation under a unified operations intelligence layer.
Predictive Maintenance AI
ML models analyzing 12M daily sensor events to predict equipment failures before they occur.
Edge Analytics
Real-time anomaly detection at the plant floor with sub-second alerting to operations teams.
Operations Dashboard
Unified view across 48 facilities with digital twin simulation and throughput optimization.
IoT Data Platform
Centralized ingestion from SCADA, PLCs, and edge sensors with TimescaleDB time-series storage.
Cloud Infrastructure
AWS-native platform with Kubernetes orchestration and automated scaling for peak production loads.
System Design
Solution Architecture
Operations Teams
Operations Portal
Edge Analytics
Predictive Models
IoT Data Platform
Digital Twin Dashboard
Delivery
Implementation Timeline
Discovery
Stakeholder alignment, data audit, and success criteria definition.
Architecture
Reference architecture, security model, and integration blueprint.
Model Training
Feature engineering, model development, and validation framework.
Deployment
Production rollout, observability, and enterprise integration.
Go Live
Full production launch, monitoring, and optimization handoff.
Engineering
Technology Stack
Python
PyTorch
AWS
IoT Edge
Docker
Kubernetes
React
TimescaleDB
Transformation
Business Outcomes
Before
- Calendar-based maintenance
- Fragmented sensor data
- Reactive downtime response
- Manual production scheduling
After
- Predictive maintenance AI
- Unified IoT platform
- Proactive failure prevention
- Optimized throughput planning
“We went from firefighting downtime to predicting it. The platform paid for itself within the first quarter of deployment.”
