Challenge: Reduce infrastructure and through-life operational costs via real-time, agile monitoring of critical production environments.
Result: Estimated 5-10% improved machine utilisation, reduction in energy use (10-15%) and maintenance time (20%), arising from performance optimisation and enhanced resource utilisation.
This industrial application use case will use sensor data and 5G to capture, monitor and gain insight of the changing variable conditions within a factory environment. Ultimately, this data will benefit operations by adjusting activities and settings to compensate for any changes in the environment. It will also provide insight into how the environment can affect operations and cause fluctuations in tolerances.
The sensors will provide feedback on internal factory and external local environmental conditions such as temperature, air pressure and humidity as well as external influences such as tides and lunar cycles, location data, building management systems supplying events data on electricity distribution, alarms and other events. They will also provide feedback on other events and influences such as machine movement, radio frequency identification scans, connections to human-machine interface on machines, shocks or exposure to extremes.
To understand the impact of 5G, all data will flow through the 5G network to realise the effects, impact, benefits, and constraints of 5G on the manufacturing processes.
This use case seeks to explore solutions to a number of challenges:
- Increased efficiency
- Reduce costs
- Reduction in errors
- Remove the need for an alignment system
- Improve safety
- Increase flexibility.
5G Factory of the Future has developed five use cases for 5G in manufacturing, delivering innovation with measurable outcomes.
Introducing Hybrid Reality Spaces
Challenge: Enabling real-time rich information and AI assistance to be exploited by people at the point-of-use; reducing cost of downtime, interpretation and uncertainty.
Result: Estimated reduction in travel costs (65%) and maintenance time (15%) arising from real-time, worldwide collaboration and increased ease of training and maintenance support.
Real-time Monitoring and Adaptive Closed-Loop Control
Challenge: Reduce cost and time associated with defects and quality issues. Create a no-fault forward manufacturing system.
Result: Estimated 15-25% reduction in the number of defects, amount of waste generated and machine downtime arising from improved process precision and predictive maintenance strategies and fewer errors.
Challenge: Increasing visibility across the supply chain network through all tiers for assets and products, guaranteeing operational efficiency and delivery to customers.
Result: Estimated 30% reduction in lost and damaged assets, improved schedule accuracy, and providing supply-chain transparency and real-time condition monitoring for assets tracked by the system.
Challenge: Enable a business-winning paradigm via data-driven digital twins through the product lifecycle.
Result: Estimated 15-20% machine utilisation improvement (reduction in idle time, improved scheduling) and factory efficiency, arising from data-driven decision-making, real-time asset location and inventory accuracy, efficient scheduling, asset performance optimisation, and improved predictive maintenance.
