Machine Learning Approach to managing Facilities Management (FM) Supply Chain Risks

The UK facilities management (FM) industry witnessed a growth of 4.8% in 2022, 5.1% in 2023 and is expected to hit £52billion equivalent to 7.5% of the country’s GDP in 2026. Despite this, much of the spotlight has been on the continued risks and uncertainties to its supply chain. In a recent report by the University of Sussex’s UK Trade Policy Observatory (UKTPO), UK businesses including FM are claimed to be struggling with increased costs, labour and skills issues and supply chain (SC) shortages following the country’s exit from European Union. With advances in digital technologies, the facilities management industry has continued to explore the use of abundant digital innovations to mitigate this challenge by transforming its processes including managing its supply chain risks with the ultimate aim of optimising SC performance. 

This PhD studentship offers an exciting opportunity to conduct a research examining Machine Learning as one of the key digital innovations in the UK FM industry to manage SC risks and uncertainties. In this context, the successful candidate will be expected to engage with strategic industry players within the UK FM sector to collect and analyse both qualitative and quantitative data within a a range of case-studies in the sector.  

Applicants should submit a more detailed proposal that expands the broad outline given above. They are encouraged to contact the supervisor to further explore and discuss their proposal before submitting their application. 

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements ideally in Construction Management, Facilities Management, Real Estate, Building, Architecture, or a related Built Environment discipline.

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Dr Dubem Ikediashi (d.ikediashi@napier.ac.uk) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

Contact details

Should you need more information, please email SCEBERDL@napier.ac.uk.

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• List of references

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here. To be considered, the application must use:

• “SCEBE1124” as project code.
• the advertised title as project title 

Download a copy of the project details here