A Methodology To Design Product Families Through Big Data

Yacoub, Jamal (2014) A Methodology To Design Product Families Through Big Data. [Dissertation (University of Nottingham only)] (Unpublished)

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Many companies in the online market store huge data about their customers’ preferences. In the same time, there is a fierce competition between companies to attract customers by designing and offering new customer-focused product portfolios. Designing products through big data has been recently trending upwards but there is a need for a methodology to enable companies to extract the useful knowledge from big data in product design. This research presents a new method to help companies to benefit from big data in product design. With regards to the research approach adopted in this study, it involves the application of three techniques on an online case study from the smartphone industry namely: fuzzy technique; Zhang, et al. (2007) data mining tool and; Mohanty & Bhasker (2005) decision making tool. Fuzzy technique is used to translate customers’ language to functional requirements. The data mining clustering tool facilitates the segmentation of customers based on their patterns of preferences whereas the decision making tool facilitates the identification of the satisfaction level of online customers. The findings from this research support that segmenting customers based on their patterns of preferences provide more meaningful information than segmenting customers based on general variables. In addition, these findings have led to the creation of a new tool which enables the determination of the optimal number of clusters needed to segment customers and to reposition product family in a mass customization market. This tool can be of value to manufacturers as well as to the academic literature, thus providing a basis for further research.

Item Type: Dissertation (University of Nottingham only)
Depositing User: EP, Services
Date Deposited: 12 Nov 2014 10:14
Last Modified: 19 Oct 2017 14:00
URI: https://eprints.nottingham.ac.uk/id/eprint/27428

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