Predictive Maintenance AI for a Manufacturing Plant

A building materials manufacturer running a 24/7 production line in Kigali was losing significant revenue to unplanned equipment breakdowns. Maintenance was reactive — machines ran until they failed, causing production stoppages that averaged 4 hours per incident. With aging equipment and no systematic maintenance tracking, breakdowns were increasing in frequency and the plant was missing production targets.

We installed IoT sensors on critical equipment to capture vibration, temperature, and power consumption data, then built machine learning models that identify patterns preceding failures. Quick Tech partnered with a local industrial automation firm who handled sensor installation and calibration, while our team built the predictive analytics platform.

• Partnered with local industrial automation firm for sensor deployment
• Installed vibration, temperature, and power sensors on 15 critical machines
• Built data pipeline processing 50,000+ sensor readings daily
• Trained ML models on 6 months of historical breakdown data
• Designed alert system with escalation to maintenance teams
• Built maintenance scheduling dashboard with parts inventory tracking

The platform continuously analyzes sensor data and compares patterns against known failure signatures. When the model detects early warning signs — unusual vibration frequencies, temperature trends, or power draw anomalies — it generates alerts with predicted time-to-failure and recommended maintenance actions. The maintenance team can schedule interventions during planned downtime windows rather than dealing with emergency breakdowns.