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.

What are we doing?

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.

Why are we doing it?

This use case seeks to explore solutions to a number of challenges:

  1. The ability to monitor the environment around manufacturing facilities that could influence operations, quality, productivity, and efficiencies. This includes internal and external variables; 
  2. To learn ‘what’ influences affect processes; 
  3. To understand and compensate for the above influences; 
  4. To create a flexible manufacturing capability with minimal fixed infrastructure where the effects of the environment in different locations can be captured, understood, and controlled to allow flexibility and agility. 
  5. To monitor the health of machines in real-time. 
  6. To prove that machines can ‘return to home’ positions based on events such as fire alarms.
  7. To prove visual recognition performance on a 5G network to identify parts and orientations using the automatic kitting cell.

What are the expected benefits?

  1. Reduction in energy costs; 
  2. Reduction in infrastructure such as tooling, jigs, and fixtures; 
  3. Improving the control of the manufacturing environment 
  4. Ability to use reconfigurable tooling for alignment activities 
  5. For the kitting process this includes: 
  • Increased efficiency
  • Reduce costs
  • Reduction in errors
  • Remove the need for an alignment system
  • Improve safety
  • Increase flexibility.

Industrial Applications

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.

Real-time Monitoring and Adaptive Closed-Loop Control

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.

Chain of Custody System (CCS)

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.

Distributed and Shared Hybrid Reality Spaces (HRS)

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.

Digital Twin Track and Trace

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