Manufacturing | Quality Assurance
Connected Field Service for Enhanced Maintenance Support
Objective
Manufacturing industries seek to develop a Connected Field Service capability to enhance maintenance support for manufacturing industrial processes. The aim is to predict equipment failures, optimize maintenance schedules, and improve overall operational efficiency for manufacturing clients.
Challenges
- Complex industrial equipment with multiple failure modes.
- Diverse data sources and formats (sensors, historical maintenance records, operational logs, etc.).
- Real-time data processing and analysis requirements.
- Integration with existing client systems and processes.
- Balancing predictive maintenance with operational constraints.
Solution Proposed
- Utilized machine learning algorithms such as Random Forest and Gradient Boosting for failure prediction, alongside time series analysis (ARIMA) for forecasting.
- These algorithms were chosen for their ability to handle complex, multi-dimensional data and provide interpretable results.
- Custom-built IoT gateway to ensure seamless data collection and transmission.
- The BI-powered dashboard enables intuitive visualization and decision-making.
Outcome
- 20% reduction in maintenance costs.
- Reduction in unplanned downtime.
- Increase in equipment lifespan.
- Improvement in Overall Equipment Effectiveness (OEE).
- Accuracy in failure prediction within a 24-hour window.
- Decrease in the Mean Time to Repair (MTTR).
Design & Thinking Wins
- Scalable IoT architecture supporting thousands of connected devices.
- Edge computing modules for real-time analysis and reduced latency.
- Digital twin framework for maintenance scenario simulation.
- AI-driven anomaly detection and predictive maintenance algorithms.
- Interactive BI dashboard for maintenance insights and scheduling.
- API-based integration with client ERP and CMMS systems.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
