Case Study: Large Equipment Management Company improves predictive maintenance with Prolifics

Large Equipment Management Company - Customer Case Study

Large Equipment Management Company needed a faster, more accurate way to monitor complex equipment across global sites and respond to failures in real time. Prolifics helped address the challenge of manually analyzing failures, handling both structured sensor data and unstructured repair tickets, and identifying which equipment parts were causing problems. The company also needed to incorporate weather conditions into maintenance decisions, and Prolifics used IBM weather data, IBM DB2, IBM SPSS, and IBM Cognos Analytics as part of the solution.

Prolifics implemented an analytics-driven predictive maintenance platform that automated data loading, transformed repair ticket text into structured insights with fuzzy logic and natural language processing, and created dashboards for real-time monitoring and threshold-based alerts. The result was a centralized data hub and a more accurate cognitive model for predicting equipment issues, enabling proactive maintenance and faster reaction times. Prolifics helped the company eliminate hours of manual labor and data mining, and reduced weeks of equipment failure forecasting work to just minutes.