Predictive Maintenance

The technical materials for this project are confidential. Below is a brief summary of the work.

Background: Unexpected equipment failures in dairy processing plants can lead to costly downtime, production delays, and product loss. Fonterra, a global dairy company, needed a proactive solution to better manage equipment health across its factories.

Aim: The objective was to develop a highly accurate predictive model that could identify potential equipment failures a month before they occur, minimizing unplanned downtime and maintenance costs.

Action: Using Python, I built a machine learning model that analyzes operational and sensor data from factory equipment to predict failures with 99% accuracy. The model was trained and validated on historical failure patterns to ensure robustness and scalability across multiple facilities.

Result: The predictive model enables Fonterra to take preemptive maintenance actions, significantly reducing unplanned outages and improving operational efficiency. Its 99% accuracy offers high confidence in failure detection, helping ensure consistent production and cost savings.