The Challenge:
How do you predict equipment failures before they happen while optimizing maintenance costs across multiple manufacturing facilities?
Our client, an automotive parts manufacturer, operated a complex ecosystem of CNC machines, injection molders, and assembly lines that required constant vigilance. Their tech teams were caught in an endless cycle of emergency repairs, with critical equipment failures often occurring during peak production hours. The company was spending 40% more on maintenance than industry benchmarks while still experiencing 16% unplanned downtime monthly.
Traditional scheduled check-up was proving insufficient for their diverse equipment portfolio. Some machines were being serviced too frequently, wasting resources and resulting in unnecessary interruptions. Others were running until failure, causing catastrophic breakdowns that halted entire production lines. The lack of real-time visibility into equipment health meant their teams were always one step behind, reacting to problems rather than preventing them.
The company’s leadership knew they needed a smarter approach. They required a solution that could analyze vast amounts of sensor data, identify patterns that human operators might miss, and provide actionable insights for accurate scheduling. The goal was clear: shift from reactive maintenance to predictive intelligence that would keep production flowing smoothly while minimizing costs.
