The University of Exeter Business School (UEBS) trains world-class researchers who will shape our understanding of and responses to the most important societal challenges.
Context:
Traditional manufacturing remains a major contributing factor to greenhouse gas (GHG) emissions. Tackling it will require new research and the exploration of opportunities for reducing GHG emissions (Di and Yang, 2021). The emissions from the manufacturing processes equate to between 15-30% of the UK territorial emissions. Manufacturers need to tackle emissions by rethinking the designs of the products they make for customers, the resources they use in manufacturing these products, the manufacturing processes, and emissions from the wider supply chains. While the goal is clear, a large proportion of manufacturers struggle to define effective strategies to implement sustainable manufacturing that works for them.
About the Project:
In the context of sustainable manufacturing practices and significant trends such as digitalisation, this research will help co-design ways manufacturers can select to achieve Net Zero Carbon targets and wider sustainability impacts. Digitalisation offers the opportunity to leverage digital technologies to improve business processes and to change a business model. Towards achieving the aim of creating sustainable manufacturing practices in the industry through digitalisation, the project has the following two objectives:
• The PhD will investigate quantitative techniques that inform product and process design, decision-making and, management practices. It will focus on transformative opportunities in sustainable manufacturing practices brought about through digitalisation. For the realisation of this objective, the candidate will have an interest, and skills, in quantitative modelling methods such as computer modelling and simulation (M&S), forecasting, etc.
• The PhD will investigate novel methodologies, techniques and frameworks enabled through digitalisation but developed within other disciplines. To achieve the second objective, the student will need to embrace interdisciplinary research.
Digitalisation and the focus on quantitative models (Objective 1):
With the increasing trend in the digitalisation of manufacturing processes, data-driven decision-making will play a key role in the sustainable transformation of industrial production. For example, real-time data can be used with time-based simulation methods, such as discrete-event and agent-based modelling, to develop digital twins that enable real-time experimentation of complex manufacturing systems. What might be the opportunities of leveraging such digital twin simulation for sustainable manufacturing? Another example is using historical and real-time data to find patterns in machine breakdowns through predictive analytics. How might we use such a form of analytics for condition monitoring and predictive maintenance, and how does it contribute towards sustainable manufacturing goals? How might we combine simulation techniques such as DES, ABS and SD and develop hybrid simulations of sustainable manufacturing (Brailsford et al., 2018)? What might we learn from existing research on digitalisation and the circular economy (Okorie et al., 2018; Charnley et al., 2019)? These are some of the questions that the PhD is expected to investigate.
Digitalisation and the focus on interdisciplinary research (Objective 2):
The PhD research will identify cross-disciplinary methods, techniques and frameworks (enabled through digitalisation), which may further facilitate the attainment of sustainable manufacturing practices. Some areas include behavioural change (e.g., developing users’ understanding of sustainable manufacturing through serious games, behavioural change interventions through digital nudges), economics (e.g., new economic models for sustainable manufacturing, sharing economy based on DE platforms), computer vision (e.g., virtual and augmented reality to aid training), Soft Operational Research (e.g., cooperative decision making among a multitude of stakeholders). How might we combine such cross-disciplinary methods, techniques and frameworks for the development of hybrid models (Tolk et al., 2021) and what opportunities might it presents for the sustainable transformation of manufacturing?
Expectations from the PhD:
1. Review and map the landscape of modelling methodologies and different forms of data-driven analytics and examine their applicability in the context of sustainable manufacturing practices.
2. Review and map the interdisciplinary research landscape, for example, identify novel methodologies and techniques from disciplines such as behavioural change, economics and soft OR, and investigate their application towards facilitating sustainable manufacturing practices.
3. Develop a conceptual framework that maps the opportunities for digitalisation with the literature reviews [see (1) and (2)]. The framework will be interdisciplinary in scope and will combine the understanding from the two objectives. The framework will allow for the exploration of methods and techniques (including their combinations) that can be applied in the analysis of manufacturing processes to make them more sustainable.
4. Work in a team with both the industry partner and a wider body of stakeholders to understand and elicit requirements for decision support tools for sustainable manufacturing.
5. In association with the industry partner, design and create exciting public and industry-facing case studies that combine the most appropriate methods and techniques from quantitative modelling (objective 1) and cross-disciplinary methods (objective 2) identified in this research.
Eligibility:
Please note that this is an industry-funded project. Minimum entry qualifications include:
• An Honours degree at 2:1 or above in a relevant science or engineering discipline.
• The candidate may have a Master’s Degree in either Digitalisation, Operational Research (in particular, Computer Simulation), Industrial Engineering or related disciplines.
• Experience in quantitative modelling, e.g., computer simulation, data-driven analytics and computer programming (e.g., Python, Java) is desirable.
• An experience of commercial simulation software (e.g., MATLAB, SIMUL8, Anylogic), qualitative analysis (e.g., NVIVO) will be positively considered, but are not essential.
• An appreciation of interdisciplinary research and the ability to operate outside the disciplinary comfort zone.
• Due to the nature of the funding, this studentship is available to only to UK/Home candidates.
How to apply
For more information and to submit an application please go to https://www.exeter.ac.uk/study/funding/award/?id=4454
Closing date for applications is midnight Tuesday 31st May 2022.
Interviews for shortlisted candidates will be mid June 2022.
Commencement of the PhD will be 1st September 2022.
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