IoT Analytics for Smart Manufacturing

01 / The Challenge

Bluepeak's production lines generated terabytes of sensor data that went nowhere. Maintenance was calendar-based, so teams serviced healthy machines while failing ones halted production each unplanned stoppage costing roughly $40,000 an hour.

02 / Our Approach

The thinking behind the build

Industry 4.0 projects usually drown in platform-building before delivering value, so we inverted the order: pick the three failure modes that cost the most (bearing wear, belt tension, thermal drift), instrument for those, and prove the predictions pay before generalizing the platform. Plant managers saw live dashboards within ten weeks.

We also refused to let the models be a black box every anomaly alert links to the sensor traces behind it, because maintenance crews don't act on predictions they can't interrogate. Adoption, not accuracy, is what cuts downtime.

03 / The Solution

What we built

We built an ingestion pipeline streaming 12,000 sensors into a cloud time-series platform, dashboards giving plant managers live line visibility, and anomaly-detection models that flag developing failures days in advance turning maintenance from calendar-driven to condition-driven.