Project Summary

The Challenge: An agro-processing facility in West Africa experienced frequent machine breakdowns, leading to costly production delays and inefficient resource use. The lack of a predictive maintenance system made it difficult to identify issues before they escalated.

The Solution: Ismatic Engineering deployed a customised IoT-based predictive maintenance system. IoT sensors were installed to monitor machine performance metrics such as vibration, temperature, and pressure. The data was analysed in real-time to detect anomalies and predict potential failures.

The Outcome

The predictive maintenance system reduced machine downtime by 40%, significantly improving production efficiency. Maintenance costs decreased by 25% as issues were resolved before major failures occurred. This allowed the facility to optimise resource use, increase productivity, and improve overall profitability.