Manufacturing | Demand & Production Planning
Predictive Maintenance Service for the Manufacturing Industry
Objective
Unexpected equipment failures cause significant downtime and maintenance costs for manufacturers. A predictive maintenance solution is needed to anticipate equipment failures and optimize maintenance schedules.
Challenges
- High variability in equipment performance and failure modes.
- Integrating diverse data sources from sensors, logs, and historical maintenance records.
- Ensuring real-time data processing and actionable insights.
- Balancing maintenance schedules to minimize downtime without over-maintaining equipment.
Solution Proposed
- Utilization of solutions such as Random Forest & XGBoost to analyze sensor data and predict equipment failures.
- Implementing Business Intelligence (BI) tools to visualize maintenance trends and performance metrics.
- Leveraging engineering expertise to validate predictive models and ensure they align with physical equipment behavior.
- Innovative use of ensemble learning techniques to improve prediction accuracy and robustness.
Outcome
- Cut back on maintenance costs by 20%.
- Reduction in unplanned downtime.
- Improved equipment lifespan.
- Enhanced decision-making with real-time insights and predictive analytics.
Design & Thinking Wins
- Successfully integrated predictive maintenance solution in a leading automotive manufacturing plant.
- Achieved a 25% increase in Overall Equipment Effectiveness (OEE).
- Recognized by industry leaders for innovative use of ML in maintenance.
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.
