Digitalisation for Facilitating Sustainable Manufacturing Practices in Industry. Ref: 4499
About the award
Professor Nav Mustafee - University of Exeter Business School.
Dr Justyna Rybicka - Senior Research Engineer at MTC. (Justyna.Rybicka@the-mtc.org)
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. If you are interested in an exciting industry-facing PhD project that investigates the opportunities for sustainable manufacturing practices brought about through digitalisation and approaches that are likely to facilitate its adoption, this is the opportunity! The PhD will explore the facets related to design, decision-making and management practices, and will help tackle urgent challenges related to sustainability of manufacturing practices.
The PhD is funded by the Manufacturing Technology Centre (MTC) and the Business School. The PhD can be conducted either full-time or part-time. Students will receive a tax-free stipend of £19,000 that covers the 3-year full-time PhD and a tuition fee waiver. The studentship will be pro-rata for part-time students. Students are additionally eligible for funding to support their research, development and conference attendance. Due to the nature of the funding, this studentship, including full tuition fees and maintenance allowance, is available to UK/Home candidates only.
About the Project:
Significant trends such as digitalisation have brought to the forefront Industry 4.0 technologies like Industrial Internet-of-Things (IIoT), symbiotic simulation and smart factories. However, traditional manufacturing continues to be a major contributing factor to greenhouse gas emissions. Can digital technologies be leveraged to improve conventional manufacturing and business processes and thereby help manufacturers to achieve Net Zero Carbon targets and wider sustainability impacts? The PhD will focus on transformative opportunities in sustainable manufacturing practices through digitalisation. It has the following two objectives:
• The PhD will investigate computer modelling and simulation (M&S) techniques that inform product and process design, decision-making and management practices.
• It will investigate novel methodologies and frameworks developed in fields such as Circular Economy and behaviour change, for example, the behaviour change wheel (Mitchie et al., 2011). The aim is to achieve the maximum likelihood of shifting towards sustainable manufacturing.
Digitalisation and the focus on computer modelling (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 toward 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 developing hybrid models (Tolk et al., 2021), and what opportunities might it present 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 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.
Brailsford, S. C., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2019). Hybrid simulation modelling in operational research: A state-of-the-art review. European Journal of Operational Research, 278(3), 721-737.
Charnley, F., Tiwari, D., Hutabarat, W., Moreno, M., Okorie, O., & Tiwari, A. (2019). Simulation to enable a data-driven circular economy. Sustainability, 11(12), 3379.
Okorie, O., Salonitis, K., Charnley, F., Moreno, M., Turner, C., & Tiwari, A. (2018). Digitisation and the circular economy: A review of current research and future trends. Energies, 11(11), 3009.
Tolk, A., Harper, A., & Mustafee, N. (2021). Hybrid models as transdisciplinary research enablers. European Journal of Operational Research, 291(3), 1075-1090.
Michie, S., Van Stralen, M. M., & West, R. (2011). The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation science, 6(1), 1-12.
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. For example, a candidate with a BSc or MSc degree in Digitalisation, Operational Research/Computer Simulation, Business Analytics, Industrial Engineering, Smart Manufacturing, Operations Management, Supply Chain Management 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 with commercial simulation software (e.g., MATLAB, SIMUL8, Anylogic) will be positively considered but are not essential.
• An appreciation of interdisciplinary research and the ability to operate outside the disciplinary comfort zone.
• The studentship is open to students who may wish to pursue PhD study in either full-time or part-time (minimum 0.5) mode. The studentship is open only to UK/home students,
How to apply
You need an undergraduate Honours degree in a relevant discipline (see eligibility) at 2:1 or above.Most incoming students also hold a Masters degree in a relevant area.
You need to give details of two referees. One referee should be a faculty member who know your work well and can judge your research potential. Please also attach a CV. An essay outlining the reasons for applying for the studentship and suitability for the project forms an essential part of the application, so ensure that you give this sufficient attention.
To apply, click on the “Apply Now” button above. You will be asked to complete the application form, and to upload the documents listed below by midnight on Monday 31st October 2022.
Note, your application will not be considered if you do not upload all of the documents listed below in the specified form; please do not upload any additional documents as they will not be considered.
1. An essay of no more than 750 words maximum explaining (1) why you are applying for the scholarship and (2) why you are a suitable recipient of the scholarship (please focus on the eligibility criteria).
2. A CV of no more than two pages.
3. The official transcript or certified copy of university grades from all past degrees
4. Two academic reference letters (one must be from your most recent institution)
5. If relevant, valid, original Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS) or Cambridge score report (if required).
Closing date for applications is midnight on Monday 31st October 2022.
Interviews for short-listed candidates will be late November 2022.
PhD to commence January 2023 and can be conducted on either full time or part time.
The academic supervisory team will also consist of Dr Okechukwu Okorie from the Exeter Centre for Circular Economy.
For more information about the scholarship and informal enquiries, please contact email@example.com.
|Application deadline:||31st October 2022|
|Number of awards:||1|
|Value:||Students will receive a tax-free stipend of £19,000 that covers the 3-year full-time PhD and a tuition fee waiver. The studentship will be pro-rata for part-time students.|
|Duration of award:||per year|
|Contact: PGR Admissions Teamfirstname.lastname@example.org|