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Improving Customer Experience for Automobile Part Retailing Process with Machine Learning Algorithms (Application Ref: SF19/EE/CIS/LI)

  • Full or part time
    Dr H Li
    Dr MN Anwar
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The project aims at developing a system with the reverse image search method for car part business processes, e.g, order fulfilling and stock management process. Traditionally, the car part selling process involves presales activities, order fulfilment, after sales services etc. Many of these processes require the identification of different type of car parts in the stock. The traditional methods to do this is through visual checks manually. The disadvantages for this type of methods are the accuracy and efficiency. It might take quite a long time for the parts to be identified. If parts are returned from customers, labels are missing making it even harder to identify types of parts. It is very typical that the warehouse staff are not familiar with the characteristics of the part and send the wrong product. Many parts are wasted and thrown away due to this reason.

The project proposed the development of an image search based system with the reverse image search method to solve this problem. The reverse image search method is a technique that can use the features of the picture to find the originals of the picture. In the scenario of the car part selling and stocking, an image of the part can be taken with the mobile phones by either the customer or the inventory/warehouse staff. The picture can be used to locate the product from the product database with the reverse image search method. The details of the part is extracted from the database so that the order fulfilment and inventory management process will be facilitated.

An established car part database shall be established for this purpose. LKQ Company, the largest car part company, is willing to sponsor the project by providing the database for the project.

This project is supervised by Dr. Honglei Li.

Eligibility and How to Apply:

Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/EE/CIS/LI) will not be considered.

Start Date: 1 March 2020 or 1 October 2020

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

This is an unfunded research project.

References

1. Jing Tian, Honglei Li, & Rong Chen. (2018) Individual Behavior Change under Smart City Environment—A Proposal of Smart Citizen Concept with Four Dimensions, iConference 2018, Sheffield, 25-28 March, 2018.

2. Nnanyelugo Aham Anyanwu & Honglei Li. (2017) E-Public Engagement: Formulating A Citizen Content Engagement Model, European Conference on Information Systems, Guimaraes, Portugal, 5-10 June, 2017.

3. Zequn Li, Honglei Li & Ling Shao (2016) Improving Online Customer Shopping Experience with Computer Vision and Machine Learning Methods In HCI in Business 2016, Los Angeles, USA, Toronto, Canada, 17-22 July 2016.

4. H. Li & V. S. Lai. (2016) Understanding The Role of Social Situations On Continuance Participation Intention in Online Communities: An Empirical Perspective, Journal of Electronic Commerce Research, 17, 358-380.

5. Li, S. Tryfonas, T. Li, H. (2016) Internet of Things: A Security Point of View, Internet Research, 26, 2, 337-359.

6. Honglei Li, Cemal Tevrizci, Nnanyelugo Aham-Anyanwu, and Robert Xin Luo. (2015). The Interplay between Value and Service Quality Experience: E-Loyalty Development Process through the EtailQ Scale and Value Perception, Electronic Commerce Research, 15, 4, 585-615.

7. Li, H. and Kun-Chang, Lee. (2013) Interpersonal Relationship Framework of Virtual Community Participation Psychology: from Covert to Overt Process, Journal of Social Science Computer Review, 31, 6, 703-724.

8. Cheuk, Bonnie & Honglei Li (2013) Implementing Enterprise 2.0 to engage the whole workforce in the co-creation of a new business strategy, the experience of one global company: Environmental Resources Management. Global Business & Organizational Excellence, 32, 4,

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