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A multi-criteria decision making scheme to classify and forecast the demand for perishable products: its empirical adoption and effectiveness for sustainability

Faculty of Business and Law

About the Project

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the Faculty of Business and Law and will be supervised by: Dr Huijing Chen, Dr Maria Barbati and Professor Salvatore Greco.

The work on this project will:
-Understand models and methods for short-term demand forecasting and the literature on judgmental forecasting;
-Examine the use of multi-criteria decision making (MCDM) techniques to enrich forecasting methods;
-Develop a user friendly MCDM scheme to categorise demand for perishable products;
-Design a training regime for planners in case organisation;
-Measure ongoing improvement in forecasting practice and culture.

Project description
Demand forecasting is a task faced by many organisations. Typically large amount of forecasts are prepared at Stock-Keeping Unit level for production, replenishment and stock purposes. There are many demand classification schemes, often devised with the aid of MCDM techniques, in order to find the best forecasting methods and inventory policies for different classes [3]. Many of these classifications are developed specifically for spare parts and intermittent demand [2]. This project, however, focuses on fast-moving and perishable products. No such scheme exists, where the requirements can be very different from spare parts and accurate and timely forecasting is all the more important to reduce waste and ensure sustainability.

Modelling and computational advances over the years have seen fast development and availability of model-based forecasting methods. This project will develop a demand classification specifically suited for perishable products and identify appropriate forecasting methods. A portfolio of these methods can be suggested to the company, according to a variety of objectives to be taken into account and that can be explicated thanks to the interaction with the planners [1].

Using a real organisation as a case study, the multi-criteria classification scheme will be tested and benchmarked against the company’s current forecasting practice, which is mainly judgmental, prepared by planners with little or no statistical background. This project will also investigate how training can improve forecasting performance by (1) aiding understanding and acceptance of model-based forecasting, (2) guiding towards value-added judgmental adjustment where necessary and (3) fostering a continuous improvement culture.

The main research objectives of this project are:
1.Develop a MCDM scheme based on demand classification and identify appropriate forecasting methods for the scheme;
2.Empirically test and measure the performance of such a scheme;
3.Design effective training for planning personnel to adopt the scheme and improve judgmental decision making;
4.Measure ongoing improvement in forecasting practice and organizational benefits in sustainability.

Entry requirements
You’ll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

How to apply
We’d encourage you to contact Dr Huijing Chen () to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form (please follow the institution website link to access it) . Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

Please also include a research proposal of 1,000 words outlining the main features of your proposed research design – including how it meets the stated objectives, the challenges this project may present, and how the work will build on or challenge existing research in the above field.

When applying please quote project code: O&SM4711020

Funding Notes

Self-funded PhD students only.

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

The Faculty of Business and Law offers funding to attend conferences (currently £550), training (currently £450), and a work-based placement (currently a maximum of £3,000 tied up to the period of 12 weeks).


Barbati, M., Greco, S., Kadziński, M., & Słowiński, R. (2018). Optimization of multiple satisfaction levels in portfolio decision analysis. Omega, 78, 192-204.

Hu, Q., Boylan, J. E., Chen, H., & Labib, A. (2018). OR in spare parts management: A review. European Journal of Operational Research, 266(2), 395-414.

Roda, I., Macchi, M., Fumagalli, L., & Viveros, P. (2014). A review of multi-criteria classification of spare parts: From literature analysis to industrial evidences. Journal of Manufacturing Technology Management, 25(4), 528-549.

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