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.

What are we doing?

A digital twin is a dynamic virtual representation of a physical object or system, usually across multiple stages of its lifecycle. It can be used to answer ‘what-if’ questions and present insights in an intuitive way by using real-world data, simulation and / or machine learning models, combined with data analysis, to enable understanding, learning and reasoning.

Unlike the industrial application use cases, which utilise specific machines, technology and processes, this will support other use cases to enhance their capabilities and apply digital twin principles. It will investigate solutions to business challenges including: floor space costs and optimisation; remote maintenance and operations; training and learning; and using virtual technology to test ideas that if done physically can be time consuming, costly and disruptive.

To understand the impact of 5G, all data will flow through the 5G network to analyse the effects, impact, benefits, and constraints of 5G on the manufacturing processes.

Digi track blueprint

Why are we doing it?

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

  1. Explore how the digital twin can facilitate and enable a flexible manufacturing capability; 
  2. Improve situational awareness with a better response to changing situations by analysing and understanding influences, impacts and how to plan for these; 
  3. Create and prove a reference architecture for the digital twin and 5G; 
  4. ‘What-if analysis’ and scenario mapping: being able to simulate real case situations and explore the effects of events and the options to address these;
    Use historical and real-time data to predict behaviours and most appropriate models; 
  5. Capitalise and add value to other industrial applications through the use of data captured, supplementing and enhancing their capabilities and results;
  6. Expand the use of AI and advanced analytics by including the digital twin for other use cases;
  7. Test future scenarios by modeling on changing needs, scaling up or down or planning for crisis;
  8. Remote troubleshooting with ability to analyse problems and causes remotely;
  9. Factory optimisation by using digital twins to accelerate and reduce the costs of factory changes such as flexibility, flow, space and layout.

What are the expected benefits?

  1. Improve efficiencies: simulations could reduce setup and response times;
  2. Accelerated learning and reduced costs by using digital twin as a training asset
  3. Improve the ability to predict problems, plan for events and change;
  4. Improve flexibility and rescue costs;
  5. Improve and accelerate innovation;
  6. Enable testing of scenarios, options, changes and ideas without impacting upon real operations which could reduce disruption, validate changes, and optimise timelines and planning;
  7. Reduce the maintenance overhead.

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

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)

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)

Supported By

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