CPG | Manufacturing
Predictive Maintenance Services
Objective
CPG companies often face downtime and maintenance costs due to unexpected equipment failures. There is a need for a predictive maintenance solution to anticipate equipment failures and optimize maintenance schedules.
Challenges
- High variability in equipment performance and failure modes.
- Integration of 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
- Utilize approaches such as Random Forest & XG Boost to analyze sensor data and predict equipment failures.
- Implement Business Intelligence (BI) tools to visualize maintenance trends and performance metrics.
- Leverage 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
- Maintenance costs were reduced 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.
