Smart Factory: Developing a Digital Twin and Intelligent Network for Self-Adaptive, Flexible Production Environments

  • Simon Rollinson

    Student thesis: Master's ThesisMaster of Science by Research


    The purpose of this research was to examine the feasibility of creating an autonomous closed-loop Digital Twin system based around Discrete Event Simulation in order to increase production efficiency. The dissertation is based on a literature review of the definition and use case examples of Digital Twins in industry. This is followed by interviews with Crown process experts to ascertain the feasibility of utilising the different sources of input data required to create the Digital Twin. The research suggests that there would be a benefit in terms of production order optimisation to be gained from the development of an autonomous Digital Twin of $15.6million globally per annum, if implemented on all lines running small batches. However the use of Discrete Event Simulation in its traditional sense is recommended for the purpose of predicting the line performance impact of capital investment projects.In terms of Digital Twin development, it could be argued that Smartline, Crown’s in house developed process monitoring software, is a visualisation Digital Twin as it monitors and stores the PLC data in real time and the data is available to view and analyse from anywhere in the world, given the required security access. Crown could enhance Smartline as a Digital Twin in four ways: 1. Include the physical layout of the lines and in particular the conveyor sizes to visualise work in progress.2. Link to the Enterprise Resource Planning (ERP) system to enable customer order planning on Smartline3. Migrate the Control system from the PLC computers to Smartline.4. Develop a regional overview system in order to group lines by product which may be in different factories to perform the production planning and logistics across sites.The enhanced visualisation of data would allow Crown to identify methods to increase production and reduce spoilage.Crown has many factories with similar production lines across the globe and could benefit from a big data analysis project, not as a one off, but as an ongoing monitoring and optimisation system. The benefits of clustering, including fuzzy clustering, of machines globally to monitor and compare performance would allow global performance improvements, by targeting improvement projects tobring the lower performing machines closer to the best performers
    Date of Award2020
    Original languageEnglish
    Awarding Institution
    • Coventry University
    SupervisorJim Rowley (Supervisor)

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