Matching Rule Discovery Using Classification for Product-Service Design

Product-service design plays an important role in offering an optimal mix of product and accompanied service for the best customer experience and satisfaction. Previously, there are a number of design methods in literature that are focused at proposing the best combination of product and service package pertaining to customer requirements. However, the relationship between product and service elements from the perspective of customer demographics is less emphasized. In this study, we proposed a methodology to discover the matching relationship between product and service from the perspective of customer demographics. We detailed how a survey can be designed and conducted using openly available product and service information. A classification algorithm, C4.5, is applied to discover the possible product-service relationships. In order to showcase our approach, a case study of mobile phone choices and telecommunication services is presented. We have also discussed our results with some indication for future works.

[1]  P. Green,et al.  Conjoint Analysis in Consumer Research: Issues and Outlook , 1978 .

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  Xin Guo Ming,et al.  Modular Development of Product Service Systems , 2011, Concurr. Eng. Res. Appl..

[4]  Xinyu Shao,et al.  Integrating data mining and rough set for customer group-based discovery of product configuration rules , 2006 .

[5]  Liya Wang,et al.  Product service system configuration based on support vector machine considering customer perception , 2013 .

[6]  T S Baines,et al.  State-of-the-art in product-service systems , 2007 .

[7]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[8]  Tamio Arai,et al.  Service Engineering – Methods and Tools for Effective PSS Development , 2009 .

[9]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[10]  Tomohiko Sakao,et al.  Service Engineering: a novel engineering discipline for producers to increase value combining service and product , 2007 .

[11]  Sung Ho Ha,et al.  Applying knowledge engineering techniques to customer analysis in the service industry , 2007, Adv. Eng. Informatics.

[12]  Li Yu,et al.  Identification of product definition patterns in mass customization using a learning-based hybrid approach , 2008 .

[13]  Liya Wang,et al.  A methodology based on Fuzzy Extended Quality Function Deployment for determining optimal engineering characteristics in product-service system design , 2008, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.

[14]  Zaifang Zhang,et al.  An integrated approach for rating engineering characteristics' final importance in product-service system development , 2010, Comput. Ind. Eng..

[15]  Roger Jianxin Jiao,et al.  A Kansei mining system for affective design , 2006, Expert Syst. Appl..

[16]  Tracy Bhamra,et al.  The transfer and application of Product Service Systems: from academia to UK manufacturing firms , 2006 .

[17]  Chih-Chieh Yang.  A classification-based Kansei engineering system for modeling consumers' affective responses and analyzing product form features , 2011, Expert Syst. Appl..

[18]  Michele Germani,et al.  A QFD-based methodology to support Product-Service design in manufacturing industry , 2014, 2014 International Conference on Engineering, Technology and Innovation (ICE).

[19]  Xinguo Ming,et al.  A rough set approach for evaluating vague customer requirement of industrial product-service system , 2013 .

[20]  Steven L. Salzberg,et al.  Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993 , 1994, Machine Learning.

[21]  Zaifang Zhang,et al.  An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design , 2012 .