MetalWorks Industries avoided an estimated $2.3M in unplanned downtime by deploying our predictive maintenance platform that monitors equipment health in real-time using IoT sensor data and machine learning.
Our Role: Platform architecture, ML model development, dashboard UI, and ongoing model retraining
Estimated cost of unplanned downtime avoided in first year
Downtime Avoided
Reduction in routine maintenance costs
Maintenance Cost Reduction
Average advance warning before predicted equipment failure
Lead Time
MetalWorks Industries operates 200+ manufacturing machines across three facilities. Unplanned equipment failures were costing them an estimated $400,000 per hour in downtime, plus secondary costs from missed delivery deadlines and emergency maintenance labor. Their existing maintenance schedule was based on fixed time intervals, leading to both unnecessary maintenance on well-functioning equipment and unexpected breakdowns between scheduled service windows.
We designed and deployed a predictive maintenance platform that connects to existing IoT sensors on manufacturing equipment to continuously monitor vibration, temperature, pressure, and performance metrics. Our machine learning models learn each machine's normal operating profile and detect subtle anomalies that precede failures, typically 7–14 days before a breakdown would occur. The system sends prioritized alerts to maintenance teams with specific diagnostic recommendations.
Unplanned downtime was MetalWorks' most costly operational problem. Fixed maintenance schedules couldn't predict when equipment would actually fail.
We built a platform that turns existing IoT sensor data into actionable maintenance predictions using machine learning.
The platform monitors 200+ machines in real-time, detecting early warning signs and alerting maintenance teams with specific repair recommendations.
MetalWorks avoided an estimated $2.3M in unplanned downtime in the first year after deployment, while also reducing routine maintenance costs by 22%.
The platform paid for itself within four months. Knowing exactly when each machine needs attention has transformed our operations.
James Okonkwo
Director of Operations, MetalWorks Industries