Utility Maintenance Scheduling

Challenge

‍A utility company with a reactive maintenance approach was experiencing frequent service disruptions due to equipment failures. Their maintenance was primarily conducted after problems occurred, resulting in higher repair costs, longer outages, and customer dissatisfaction. Preventable issues were causing approximately 65% of all service disruptions.

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Solution

An AI predictive maintenance system for infrastructure using Obviously AI and Make.com that:

• Monitored equipment status indicators and environmental conditions
• Analyzed patterns of failure from historical maintenance records
• Predicted maintenance needs before failures occurred
• Optimized maintenance scheduling to minimize service impacts
• Coordinated field technician dispatching based on skills, location, and priority
• Tracked maintenance effectiveness and equipment lifecycle metrics

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Implementation

‍The system was implemented over 10 weeks:

1. Sensor and monitoring system integration
2. Historical failure analysis and pattern recognition model development
3. Predictive algorithm creation for different equipment types
4. Maintenance workflow automation and scheduling optimization
5. Field technician mobile app development for real-time information

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Results

• Preventable outages reduced by 76%, significantly improving service reliability
• €320,000 annual maintenance savings through shift from reactive to preventive work
• 2.0 FTE saved in emergency response and field operations
• Equipment lifespan extended by 25% through better preventive maintenance

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Customer Impact

Beyond the operational savings, the company saw a 31% improvement in customer satisfaction scores related to service reliability.

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