The popularisation of mass customization and the need for integration of the user needs into the design, production and marketing phases has called for more innovative methods to be introduced into this area. At present the continuous growth of the world wide web and its rapid integration into people’s everyday lives and the popularisation of new technologies such as ubiquitous computing making possible the computing everywhere paradigm, offers a more desirable alternative for vendors in reaching their customers using more innovative techniques in an attempt to provide each customer with a one-to-one design, manufacturing and marketing service. The integration of ubiquitous computing technologies with machine learning and data mining techniques, which has been popular in personalization techniques, will serve to bring about innovative changes in this area. In future years this may revolutionise the way in which vendors can reach their customers offering every customer a tailored one-to-one service from design, to manufacturing, to delivery. This chapter will present the state of the art techniques to enable the combination of machine learning, data mining and ubiquitous computing technologies which will serve to provide innovative techniques applications and user interfaces for mass customization systems. This is currently a field of intense research and development activity and some technologies are already on the path to practical application. This chapter will present a state of the art survey of these technologies and their applications.
TopIntroduction
The notion of integrating user needs into the production and design process has had great importance in mass customization. This idea is a promising strategy for companies being forced to react to the growing individualization of demand (Franke & Piller, 2002). In mass customization concepts, goods and services are made to meet individual customer's needs produced with near mass production efficiency (Tseng & Jia, 2001). Mass customization embarks a new paradigm for manufacturing industries (Pine, 1993). It recognises each customer as an individual and provides each of them with tailor made features that can only be offered in the pre-industrial craft systems (Jiao & Tseng, 1999).
Mass customization (Shafer, Konstan & Riedl, 1999) was first popularised by Pine in 1993 (Pine 1993). In his book Pine argues that companies need to shift from the old world of mass production where “standardized products, homogeneous markets, and long product life and development cycles were the rife, to the new world where the variety and customization supplant standardized products.” Pine argues that building one product is simply not adequate any more. Companies need to be able to at a minimum, develop multiple products that meet the multiple needs of multiple customers.
With the ever increasing popularity of the World Wide Web in recent years Rheingold (Rheingold, 2002) states that Web software holds the promise of mass customization and further states that a software's ability to fulfil an individual's needs necessitates the application to be aware of several factors such as, the user's profile, his/her current task or goal, and additional factors such as location, time or device used. The combination of all relevant factors can be termed context and thus a web application which takes them into account is a context-aware application (Kaltz, Wolfgang, Ziegler & Lohmann, 2005).
In this paper we present an overview of how the different techniques in personalization, data mining and ubiquitous computing in particular context-sensitive systems can be integrated with mass customization of services and products to bring innovation in this field of research.