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.

What are we doing?

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.

Why are we doing it?

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

  1. Optimising packing based on layouts and 3D nesting. A particular challenge is that this is heavily dependent upon the material and fragility/sensitivity.
  2. Understanding of an asset’s state in a journey; the condition and location at the start, during and end of a journey.
  3. Develop a solution that is agnostic to sensors and hardware
  4. Creating a centralised tracking of a product through the supply chain - achieved using sensor
  5. data derived from smart containers and assets themselves.

What are the expected benefits?

  1. Reduce costs, breakages and identify anomalies or events through journey interactions. Locations of assets can also be a challenge. 
  2. Investigate solutions to the following problems: lost or misplaced tooling and products; damage to parts and tooling; and optimisation of packing.

Industrial Applications

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.

Introducing Hybrid Reality Spaces

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

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.

Factory Ecosystem Monitoring (FEM)

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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