Manufacturing & Industrial Intelligence
Predicting breakdowns before they stop the line
A manufacturer
Dozens
production facilities across the enterprise
Year one
significant drop in unplanned downtime
01 / Challenge
The problem.
Downtime was costly and unpredictable, and quality defects surfaced too late in production. Supply chain decisions ran on lagging indicators instead of real-time data.
02 / Approach
How we built it.
We built an industrial analytics platform that connects production data with business systems. Predictive maintenance, quality monitoring, and scheduling models support decisions on the factory floor and in operational planning.
03 / What we built
The system.
01
Industrial IoT analytics platform
02
Unified data layer across production and business systems
03
Predictive maintenance models
04
Real-time quality monitoring with anomaly detection
05
ML-driven production scheduling
06
Edge computing nodes on the factory floor
04 / Outcomes
What changed.
Unplanned downtime dropped significantly within the first year
Defects caught earlier, cutting waste and rework
Better inventory turns and stronger terms for procurement