Manufacturing | Product Design & Operational Excellence
Connected Factory Solutions for Manufacturing Majors
Objective
Manufacturing industries compromise on efficiency due to unplanned downtimes, suboptimal production processes, and a lack of real-time insights into factory operations. These issues result in increased operational costs, reduced productivity, and lower overall equipment effectiveness (OEE).
Challenges
- Data silos across different factory systems.
- Inconsistent data quality and lack of real-time data integration.
- Difficulty in predicting equipment failures and maintenance needs.
- Limited visibility into production line performance.
Solution Proposed
AI/ML Algorithms:
- Predictive Maintenance: Using Random Forest and Gradient Boosting algorithms to predict equipment failures.
- Quality Control: Implementing Convolutional Neural Networks (CNNs) for real-time defect detection in production lines.
- IoT Sensors: Deploying IoT sensors for real-time data collection on machine performance and environmental conditions.
Outcome
- Increase in overall equipment effectiveness (OEE) by 20%.
- Reduction in unplanned downtime.
- Improvement in product quality with reduction in defects.
- Enhanced real-time decision-making capabilities.
Design & Thinking Wins
- Successful deployment in a leading automotive manufacturing plant, resulting in a 25% increase in production efficiency.
- Adoption by a major electronics manufacturer, leading to a 20% reduction in operational costs.
- Implementation in a pharmaceutical company, achieving a 15% improvement in compliance and quality standards.
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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.
