This PhD project will focus on the digital thread-based approach to develop an adaptive optimisation tool to choose the most effective service contract (servitization) for complex engineering assets (e.g. planes, ships, trains). The potential outcome of this research will tackle the existing challenges in product-service systems’ contract design in high-value manufacturing. The goal is to develop a digital representation of the asset lifecycle, which support the decision-makers at the bidding stage to find the optimal service solution for fleet level servitization. Read more Read less

Background

Over the past decade, the high-value manufacturing sectors such as aerospace, transport and marine have shifted from just providing equipment to selling services, which involves making the equipment more available and tailored for their customers. This shift is called servitization, and it has seen the rise in popularity of integrated product and service contracts. Industry 4.0 revolution, on the other hand, is expected to have a significant impact on the supply chain, business models and service solutions, by introducing integrated cyber-physical systems. This exciting PhD focuses on a digital engineering approach based on a digital thread, and advanced data analytics techniques to find the most effective, robust, and sustainable integrated product and service contract for complex engineering assets (e.g. planes, trains, ships). This PhD will focus on fleet levels of the equipment. The potential outcome of this research will address several existing challenges and limitations within the servitization shift, which mainly arise from several uncertainties in product and service data as well as the uncertainties in supply and logistics. 

Aim

The aim of the PhD is to develop a digital thread for servitization, composed of digital business models (i.e. for creating value using digital technologies), and digital twins for product and service offerings. The potential digital thread platform will integrate with several data analytics (e.g. cost estimation, optimisation and uncertainty quantification) to perform as a toolkit. The digital platform is expected to be adaptive (automated/semi-automated) and to support decision-makers in designing the optimal servitization contract. This PhD will bring together several research themes in the field of the digital twin, cost estimation, predictive modelling and uncertainty quantification. 


PhD Objectives

1. Conduct high quality research and literature review on the relevant research area
2. Design and develop a methodology for the integration of fleet-level digital twins for servitization of high value assets
3. Develop ‘adaptive’ architectures that fuse multiple data to enable digital thread of servitization
4. Develop a toolkit for the servitization contract design 

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 for simulating the complex nature of maintenance. The candidate works on his/her research individually and collaborates with other researchers in the field at the Centre 
 

At a glance

  • Application deadlineOngoing
  • Award type(s)PhD
  • Duration of award3 years
  • EligibilityUK, EU, Rest of World
  • Reference numberSATM207

Entry requirements

Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in Computer Science/ Mechanical Engineering / 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.

Funding

This is a self-funded PhD; open to UK, EU and International applicants.

About the sponsor

This is a self-funded PhD that includes the ability to participate in industry-led research initiatives and access to the Cranfield Doctoral Training Network.

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

To apply for this PhD opportunity please complete the application form using the button below. 

Apply now

 

For further information please contact:      

Name: Dr Maryam Farsi 
Email: Maryam.farsi@cranfield.ac.uk
T: (0)1234 753354