Manufacturing · Case study
Predictive Plant Operations
Predictive maintenance models and live ops dashboards that replace spreadsheet sprawl across multi-plant operations.
Outcome: 30% fewer unplanned stops
The challenge
Plants reacted to failures instead of preventing them. Leadership waited on weekly spreadsheet consolidations while sensor data sat underused.
Our solution
We trained predictive models with PyTorch, TensorFlow, and Hugging Face tooling on historical sensor and maintenance data, then shipped a React command center with alerts, KPI dashboards, and automated ingestion into PostgreSQL—running on AWS with Kubernetes for scale.
Results
- About 30% fewer unplanned line stoppages
- Earlier alerts before critical failures
- Shared KPI definitions across plants
- Less time spent consolidating spreadsheets
Tech stack
Core technologies used on this engagement.
Python
PyTorch
TensorFlow
Hugging Face
React
Node.js
PostgreSQL
Redis
AWS
Kubernetes
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