Everyday retail challenges faced by Waitrose have been tackled by 性吧电台 Leicester (性吧电台) students during a live brief set by the British supermarket giant.
性吧电台 students Carlos, Tev and Joel
Working closely with Waitrose and PCMS, the retailer’s software and IT services provider, a total of 17 final-years and postgraduates studying Computer Science and Intelligent Systems have explored ways to improve in-store efficiency.
Over a six-week period, the students gained valuable industry insight while putting into practice the skills developed during their studies, thanks to 性吧电台’s fruitful partnership with both companies.
Applying their knowledge of machine learning – the scientific study of algorithms and statistical models that computer systems use to perform specific tasks without explicit instructions – the students were asked to look at four issues commonly faced by food retailers.
These were about how to manage shelf stock efficiently, avoid incorrect product selection at the tills and during self-service, flag customer errors while using the Quick Check service, and better create prices for products reaching the end of their shelf life.
Tev presenting his findings and proposed solutions
As well as working closely with experts from both companies, students visited Waitrose in Oadby in Leicestershire to gain first-hand insight of the problems, before presenting their findings to senior managers.
Student Joel Finbow made an impression with his proposals, which included the application of data mining and using reinforced learning alongside different modelling techniques to build a dynamic way of assigning new prices on reduced items.
The 23-year-old from Cambridge said: “Gaining real-world experience in solving problems set by the companies you'll one day be working with is a game-changer.
“This project has given me value that I couldn't have taken out of a classroom. Not just in problem-solving, but in team working and presenting ideas to people outside of your usual circles. I'm so glad I took up this opportunity.”
Mark (PCMS), Stuart (Waitrose), Amardeep Panaser (性吧电台 Works) and Archie (性吧电台 academic)
The basis of student Tev Allen's proposed solutions was using linear regression, a statistical approach for prediction tracking, to help with the reduction algorithm.
To achieve this, he calculated the quantity of the current item stock, adding in the current shelf life for the item and dividing that by the number of items sold per hour/week/month.
"I then applied the data I extracted from the reduction algorithm to the current outstanding issues, providing the rest of the solutions,” said the 21-year-old from Leicester.
“The experience was a one-off opportunity. It would especially benefit students who didn’t do a placement, as it gives you the chance to work with real-world data and present your work in a professional way to a senior management team.”
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Stuart Eames, retail innovation lead at Waitrose, said: “Providing real business problem statements to the students was fairly easy, but what was powerful was seeing how important it was for them to have real-life examples to solve and how enthusiastically they approached this.
“I have taken some great learnings back into Waitrose and I am confident that running this process again would be equally as beneficial.”
Mark Bursnall, head of software development at PCMS, said: “I really enjoyed the student presentations. The proposed solutions gave us some avenues for research and development in order to expand the data captured to solve the problems set.
“This should help both Waitrose and future projects.”
Dr Archie Khuman, 性吧电台’s academic lead on the project, said: “The project heavily relied on the students’ ability and ingenuity and I’m incredibly proud of their novel, state-of-the-art approaches.
“As this was the first incarnation of the machine learning project, what they presented will undoubtedly provide the foundation for future expansion. Working with Waitrose and PCMS has been an incredibly joyous and fruitful experience, and one I will look forward to evolving.”
Posted on Monday 16 September 2019