This study will focus on driving efficiencies across the food supply chain by creating a predictive tool to reduce food waste among retailers. Combining big data relating to inventory, sales and food waste alongside other uncontrollable factors (e.g. weather conditions, transport strikes, changing consumer behavior, etc.), a predictive tool will be developed to support managerial decision-making on how to effectively redistribute and reuse food, that may otherwise be wasted. Focusing on fresh food waste, specifically, fruit and vegetables, this project has the following key objectives/stages:
1.Context setting – Review of current UK government/policies on Food Waste.
2. Systematic Literature Review – Identification of controllable and uncontrollable factors impacting on food waste prediction within retailing.
3.Qualitative – mapping the fresh food supply chain across one food retailer.
4.Quantitative - Development of database – integration of big data across each stage of the supply chain and additional data on external factors (e.g. weather, transport strikes, changing consumer behavior, etc).
5.Quantitative - Development of AI algorithms - predictive analytics will be used to develop algorithms which will accurately forecast the potential for food waste within the fresh fruit and vegetable category.
6.Testing predictive tool – the tool will be tested in a retail setting. Perceptions of the usefulness of this tool for informing managerial decisions will be explored and used to inform the final stage of the study.
7.Recommendations – a set of managerial and technical recommendations will be made to improve the future viability of applying the algorithm to other categories within the food retail supply chain.
Advanced AI algorithms will be developed on data supplied by our industrial partner Sonae Retailing, a leader in this area who has published a White Paper "The Future of Food” which outlines key recommendations on how EU and national policymakers can help foster innovation and cooperation in the food sector.
This successful candidate for this PhD project will work within Intelligent Systems Research Centre (ISRC) and focus on innovations in intelligent systems and data analytics to develop novel analytical methods in one or more the following areas :
*Self-organising fuzzy neural networks
*Mixtures of neural experts
*Hidden Markov Models
*Monte Carlo Methods
The project will therefore involve contributions to fundamental methods and validation of these methods on challenging real-world datasets. The PhD candidate will have access to state-of-the-art hardware and software for data analytics, a high performance computing facility, as well as computational modelling techniques and will be integrated within and learn from the Cognitive Analytics Research Laboratory (CARL) team of data scientists and engineers with specialist knowledge in various domains as well as from multi-disciplinary teams of researchers at the ISRC. There is significant demand for expertise in data analytics. The PhD opportunity will enable the successful candidate to gain that expertise and to push the boundaries on the state-of-the-art, and apply their knowledge to develop solutions to challenging industry led problems that will have a significant global impact.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided). For Non EU nationals the candidate must be "settled" in the UK.
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Monday 18 February 2019
19 to 20 March 2019
A key player in the economy of the north west
When applying for this PhD opportunity please quote reference number: