5G Factory of the Future has developed five use cases for 5G in manufacturing, delivering innovation with measurable outcomes

Industrial Application 01

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.

Industrial Application 02

Chain of Custody System (CCS)

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.

Industrial Application 03

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.

Industrial Application 04

Distributed and Shared Hybrid Reality Spaces (HRS)

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.

Industrial Application 05

Digital Twin Track and Trace

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.

Supported By

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