Predictive Maintenance Proof of Concept: Manufacturing Startup

A 4-month proof of concept implementing Gloomlab's predictive analytics platform to optimize equipment maintenance schedules and reduce unexpected downtime in a small-scale manufacturing operation.

The Challenge

A growing manufacturing startup was experiencing 15% unplanned downtime due to equipment failures. With limited maintenance staff and budget constraints, they needed a cost-effective solution to predict equipment issues before they caused production delays. Traditional maintenance schedules were either too conservative (wasting resources) or too aggressive (missing critical issues).

Our Solution

Gloomlab implemented a pilot predictive maintenance system using IoT sensors and our machine learning platform. We deployed monitoring on 8 critical machines, collecting vibration, temperature, and operational data. Our AI models were trained to identify patterns indicating potential failures, with alerts sent to maintenance teams 48-72 hours before predicted issues.

Results

The 4-month pilot demonstrated: 40% reduction in unplanned downtime, $50,000 in prevented equipment damage, and 25% optimization in maintenance scheduling. The system successfully predicted 12 out of 14 potential failures with 85% accuracy. The client has committed to expanding the pilot to their full production line and is considering Gloomlab for their upcoming Series A manufacturing facilities.