Crestview Industrial Group
Predictive Maintenance Across 6 Production Facilities
A mid-sized industrial manufacturer deployed AI-powered predictive maintenance across its facilities and cut unplanned downtime by 35 percent in the first six months.
Reduction in unplanned downtime within six months
Estimated annual savings from prevented failures
Machines monitored in real time
Average advance warning before predicted failures
What they were dealing with.
Crestview was losing an average of $180,000 per hour whenever a production line went down unexpectedly. Their maintenance approach was reactive. Equipment failed, teams scrambled to fix it, production halted. They knew predictive maintenance was the answer but had no way to get there with ageing equipment.
What we built.
We fitted sensor arrays to 340 pieces of critical equipment across all six facilities and built a data pipeline to ingest readings in real time. A machine learning model trained on 18 months of historical failure data now monitors every machine continuously and raises alerts when patterns suggest a component is approaching failure.
Discovery
Weeks 1 to 4
Deep dive into existing systems, data landscape and requirements. Full architecture blueprint produced.
Build
Weeks 5 onwards
Phased development with regular reviews. Continuous integration and stakeholder sign-off at each milestone.
Launch
Final phase
Production deployment, training and an active optimisation period to tune performance against agreed KPIs.
The results.
Reduction in unplanned downtime within six months
Estimated annual savings from prevented failures
Machines monitored in real time
Average advance warning before predicted failures