A manufacturing company wanted to reduce unplanned downtime and maintenance costs by predicting when machines would need repairs or servicing.

Challenges:

• The company faced frequent unplanned maintenance, leading to expensive repairs and production delays.
• There was no predictive system in place to monitor equipment health and prevent breakdowns before they happened.

Solution:

Quanois implemented a predictive maintenance solution using IoT sensors, real-time data analysis, and machine learning to monitor the health of critical equipment.

• IoT-Powered Data Science:

  We installed IoT sensors on key machinery to collect data on operational parameters like vibration, temperature, and pressure.

• Machine Learning:

  We applied machine learning models to predict when equipment would fail, allowing the company to perform maintenance proactively.

• Results:

  • Reduced downtime by 30%.
  • Lowered maintenance costs by 20%.
  • Increased production capacity by 15% as machines operated more efficiently.