Predictive maintenance to reduce downtime by 60-80%

Business Challenge

Factories faced frequent equipment failures leading to production halts, high maintenance costs, and safety concerns. Key issues included:

• Reactive maintenance resulting in unexpected downtime
• Lack of real-time visibility into equipment health
• High spare parts and labor costs from emergency repairs
• Lost revenue from idle production lines

Our Approach

We developed an AI-powered predictive maintenance system that monitors equipment health in real time and predicts failures before they occur.

Capabilities:

• Predictive failure detection using sensor and operational data
• Real-time alerts to maintenance teams for proactive intervention
• Optimization of spare parts inventory based on predicted needs
• Historical data analysis for reliability improvements

Implementation Process

• Phase 1: Data collection from IoT sensors and maintenance logs
• Phase 2: AI model training for failure prediction and anomaly detection
• Phase 3: Pilot deployment on critical production assets
• Phase 4: Full-scale rollout with integration into CMMS (maintenance systems)

Results

• 60–80% reduction in unplanned downtime
• 30% lower maintenance costs from proactive interventions
• Increased equipment lifespan and safety compliance

Business Impact

• $2.5M annual savings in maintenance and lost production costs
• Improved worker safety and reduced liability risks
• Stronger competitiveness with higher production reliability

Technical Implementation

• Machine learning models for predictive analytics
• IoT sensor integration with cloud-based monitoring
• Real-time dashboards for asset health tracking

Key Features

• AI-driven predictive alerts
• Proactive maintenance scheduling
• Spare parts optimization