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.
Lost and late supply-chain production items can cause significant loss in productivity, hitting suppliers hard and costing millions per year. Smarter monitoring of critical assets in transit will enable a digital Certificate of Conformity to be created, ensuring the product is within acceptable standards when it arrives.
This industrial application will use 5G to track and observe asset conditions in transit. Assets - in this instance, a tool or part - will have sensors attached or be carried in smart containers or boxes that have sensors and connections to 5G. The sensors will provide feedback on asset conditions which could be environmental such as temperature; events such as shock or exposure to extremes; or location using GPS.
The data will be utilised to better track the location of assets and respond to events that take them outside tolerances.
This use case seeks to explore solutions to a number of challenges:
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.
Factory Ecosystem Monitoring (FEM)
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.
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.
