Manufacturing | Asset Management & Digital Twin
Predictive Maintenance Solution for the Manufacturing Industry
Objective
Manufacturing companies aim to develop and build predictive maintenance services to anticipate equipment failures and optimize maintenance schedules. The objective is to reduce downtime, enhance operational efficiency, and minimize maintenance costs through advanced analytics.
Challenges
- High costs associated with unplanned equipment failures and downtime.
- Difficulty in analyzing vast amounts of sensor and operational data.
- Integration of disparate data sources and legacy systems.
- Need for real-time monitoring and predictive insights.
- Balancing maintenance schedules with production demands.
Solution Proposed
- Predictive Maintenance solution utilizes machine learning algorithms such as Support Vector Machines (SVM) and Random Forest to predict failures, alongside time series forecasting techniques like ARIMA for trend analysis.
- Deployment of a cloud-based analytics platform that integrates IoT data streams, enabling real-time monitoring and analysis.
- BI dashboard provides actionable insights and alerts for maintenance teams.
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 cloud-based architecture for data processing and storage.
- Real-time data ingestion from IoT sensors and devices.
- Advanced machine learning models for predictive analytics.
- Interactive BI dashboard for visualization and reporting.
- Seamless integration with Existing Enterprise Systems (ERP, CMMS).
- Customizable alerting and notification system for maintenance teams.
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.
