Preventing Costly Downtime: How Titan Industrial Recovered 12% in Production Time

The Business Problem

Titan Industrial, a mid-sized manufacturing firm, was struggling with rising maintenance costs and unpredictable equipment failures. Their primary assembly line would frequently suffer from "micro-stoppages"—short, unexplained pauses that collectively drained 12% of their weekly production capacity. Because they relied on manual oversight, their team was always one step behind the next disaster.

The QueryLess Solution

We built a high-frequency data pipeline that streamed sensor data—vibration, temperature, and voltage—directly from their equipment every 500 milliseconds. We replaced their static alert system with a custom Automated Anomaly Detection engine using an Isolation Forest machine learning model.

We provided an Executive Python Dashboard that allowed plant managers to visualize machine health in real-time, identifying subtle deviations long before they resulted in physical hardware failure.

The Result

The system proved its value within the first month by flagging a 2-degree spike in motor temperature that perfectly predicted a bearing failure 48 hours in advance. By switching to predictive maintenance, Titan saw a 22% reduction in unplanned pauses and saved $18,000 monthly by eliminating emergency shipping fees and overtime repair labor.