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Optimising resource availability using digital twins in manufacturing PhD

   School of Aerospace, Transport and Manufacturing (SATM)

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  Dr Christina Latsou, Prof John Ahmet Erkoyuncu  No more applications being accepted  Self-Funded PhD Students Only

About the Project

This PhD is within the context of digital twins for complex manufacturing systems, enabling successful human-machine collaboration. Cranfield University is currently seeking a top class candidate to undertake fundamental research in developing a novel digital twin framework for complex manufacturing system design. The proposed solution will enable the automated detection of anomalies in data (e.g. unexpected increase in work in progress, cycle times, etc.), identification of bottlenecks that deteriorate the system’s performance and mitigation of bottlenecks by optimising the use of resources (i.e. people, material, equipment, facilities). With the help of interactive user interface designs, the digital twin will provide recommended actions to the users to eliminate the identified bottlenecks. The decision-making activities taken by the users, within an otherwise automated process flow, will be tracked by the digital twin. 

A digital twin is a digital representation of a physical asset that can be used to describe its properties, condition, and behaviour through modelling, analysis, and simulation. The digital representation holds information from multiple sources and is updated through the exchange of information between the physical and virtual spaces. Effective visualisation of this information allows the prompt and accurate prediction of current and future conditions in both design and operational environments and enhance decision making.

Currently in Industry 4.0, despite the plethora of academic and industrial research, digital twin has not yet been properly understood and adopted by many industries. This is linked to the lack of guidance available to support the development of accurate digital twins. Additionally, the absence of true digital twins is often related to the challenge of having bi-directional communication between the physical asset and its digital counterpart. Moreover, mechanisms to enable automated anomaly detection, self-diagnosis and response in manufacturing related digital twins are relatively new. Neglecting the detection of deviations from expected behaviour or bottlenecks limits the benefits that we can get from digital twins. Anomalies in the context of manufacturing systems may include variations in daily orders and delivery rates, cycle times or faulty product type, batch size, etc. In the case of anomalies are detected, the system’s performance can be monitored in terms of throughput, lead times, inventory levels and resource utilisations to uncover bottlenecks emerged from the anomalies. In the context of complex manufacturing systems, current research on anomaly detection and bottlenecks analysis is, typically, conducted using top-down approaches lacking a formal comprehensive method for capturing the root causes and impacts. The development of mathematical and computational models and simulation techniques to design modular and comprehensive digital twins for complex manufacturing systems is also scarce.

In order to handle the aforementioned challenges, this exciting PhD is aiming to create a new approach whereby the digital twin can: i) detect anomalous values in sensor/manual data; ii) identify if the anomaly can cause a bottleneck to the system; (iii) predict how the bottleneck propagates and the impact to the system; and (iv) remove the bottleneck by optimising the resources utilisation. Anomalies considering variations in data will be automatically detected and the emergence of bottlenecks will be quantified in terms of throughput, lead times, etc. Optimisation will act as feedback from the virtual to the physical space to remove identified bottlenecks. Contextual feedback provided through interactive visualisation tools (e.g. dashboards) will enhance users’ decision making and system’s productivity and performance.


The PhD aims to develop a digital twin framework, in the context of complex manufacturing systems, for automated anomaly detection, bottleneck diagnosis, identification of the bottleneck propagation path and bottleneck mitigation through the optimal utilisation of resources. Techniques including agent-based modelling and simulation, mathematical modelling, discrete-event simulation, queuing models and simulation optimisation approaches will be investigated. Emphasis will be given to develop a solution considering effective collaboration between the humans and machines in decision-making. This will be achieved by developing interactive communication and interface designs (e.g. dashboards) to support mutual understanding and trust in human–machine collaboration.


  1. Conduct high-quality research and literature review on the relevant research area.
  2. Develop the conceptual model and framework of a digital twin-driven optimisation model considering the role of humans in complex manufacturing systems.
  3. Build a digital twin model to continuously monitor the performance of complex manufacturing systems and enable prompt and automated anomaly detection, bottleneck identification and removal for different optimisation scenarios.
  4. Develop interactive user interface designs (e.g. dashboards) to facilitate effective human–machine collaboration in decision-making.
  5. Test through use cases to evaluate the functionality and validity of the digital twin to optimise the performance and productivity of complex manufacturing systems being under disruption.

At Cranfield, the candidate will be based at the Centre for Digital Engineering and Manufacturing which hosts cutting-edge digital engineering facilities. The student will have access to high-end computers and digital technologies in the Centre for ontology-based and knowledge-based systems development, Digital twin development, advanced dynamic modelling and simulations, AI, VR, AR developments. The candidate works on his/her research individually and collaborates with other researchers in the field at the Centre.

Entry requirements

Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in Statistics, Computer Science/ Industrial Engineering / Mathematics / Operations Research but candidates in other degrees related to Engineering or related quantitative fields would be considered. Candidates with an MSc degree in these disciplines will be desirable.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

If you are eligible to apply for this PhD, please complete the online application form.

For further information please contact Dr Christina Latsou - [Email Address Removed]

Funding Notes

This is a self-funded research opportunity, open to UK, EU and International applicants.

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