The Problem
The core problem was a lack of real-time visibility into the actual condition of their high-value assets. Because they were relying on reactive and time-based maintenance, critical challenges were faced.
The solution
Biotz designed and deployed a comprehensive Industry 4.0 Predictive Maintenance System, integrating a full-stack architecture to turn raw machine data into actionable intelligence.
- IoT Integration: Sensors were installed on critical machinery to stream high-frequency data (such as, vibration, temperature, and power consumption) to the Biotz IoT Platform.
- Digital HMI (Human Machine Interface): A centralized web dashboard developed for the maintenance team, displaying a visual “health score” for each machine. Whenever the AI model detects a potential issue, the system automatically generates and sends predictive work orders.
The impact
Operational StabilitySubstantial reduction in unplanned downtime: Manufacturing capacity became more reliable and predictable, fulfilling clients needs with better confidence.
Maintenance CostOptimization of maintenance scheduling and spend: Costly, reactive emergencies became planned, condition-based interventions, lowering labor and spare parts inventory costs.
ProductivityIncreased Overall Equipment Effectiveness (OEE): Maximized the productive use of high-value machinery, directly contributing to greater output and profitability.
Future ReadinessEstablished a scalable digital foundation: Guibe is now positioned to integrate additional AI and automation tools across its manufacturing processes, supporting continued future growth.
