This study investigates an apparel business model and the variables that underlie it. To do so, the study draws a general picture of the challenges of the industry nowadays and how the textile industry has been moving from a product based business to a service based one. Under this scope the crucial aspects of a service operating system are analyzed through three data bases; a clerk’s survey, a customer’s survey and the POS system information. The integration of these data bases allows to capture several variables of the system to study the clerk’s influence on the purchase and the purchase event itself: in which cases a customer purchases a good and in which the customer merely visits the store and leaves.
Tasks such as the research of the behavior of these multi-variable scenarios can be managed through the application of business intelligence. This study adopted decision tree analysis to discover interesting rules that are coherent with the general picture of the industry and the current trends.
Indeed, the rules reach to a clear conclusion; the clerks in the apparel retail stores are critical. The help they provide, and their responsiveness towards the customers’ behavior can definitely change the performance of the store.

Dicken P. (2003). Chapter 3: The changing global economic map. Global Shift: Reshaping the Global Economic Map on the 21st Century, 4th edition. The Guildford Press.
Berry M.J.A. and Linoff G.S. (2004). Chapter 1: Why Data Mining? Data Mining Techniques, 2nd edition. Wiley Publishing, Inc.
Reichard R. (2007). Employment- A poor indicator. Textile world Magazine. Nov/Dec Edition. Page 18.
Baker S. (2006). The apparel industry’s top seven mega-trends: Management briefing: The plus-size market keeps growing. Just style Magazine. Oct Edition. Page 9.
Thomassey S., Happiette M., Castelain J-M. (2005). A global forecasting support system adapted to textile distribution. International journal of production economics.No. 96.Pages 81-95.
Jin B, (2004). Apparel industry in East Asian newly industrialized countries: Competitive advantage, challenge and implications. Journal of fashion marketing and management. Vol.8 No. 2.Pages 230-244.
Chen X., Au W-M., Li K. (2004). Consumption of children’s wear in a big city in Central China: Zhengzhou. Journal of fashion marketing and management. Vol.8 No. 2. Pages 154-164.
Hollen N., Saddler J., Langford A. (1994). Introduccion a los textiles, Editorial Limusa.
Abernathy, F.H., Dunlop, J.T., Hammond, J.H. and Weil, D., (1999). A Stitch in time: Lean Retailing and the Transformation of Manufacturing: Lessons from the Apparel and Textile Industries, Oxford University Press, New York, NY.
Golfarelli M., Rizzi S., Cella L. (2004). Beyond Data Warehousing: What’s next in business intelligence? Proceedings of the 7th ACM international workshop on Data warehousing and OLAP, Washington, DC. Session: Business Intelligence. Pages 1-6.
Apte C., Liu B., Pednault E.P.D., Smyth P. (2002). Business Applications of data mining. Communications of the ACM, August 2. Vol.45. No. 8.Pages 49-53.
Chen M-C., Huang C-L., Chen K-Y., Wu H-P. (2005). Aggregation of orders in distribution centers using data mining. Expert systems with applications Vol.28. Pages 453-460. El Sevier.
Brijs T., Goethals B., Swinnen G., Vanhoof K., Wets G. (2000). A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. Boston, USA. Pages 300-304.
Liao S-H., Hsieh C-L., Huang S-P. (2008). Mining product maps for new product development. Expert systems with application. El Sevier. Vol.34. Pages 50-62.
Agard B. and Kusiak A (2004). Data-mining-based methodology for the design of product families. International Journal of Production Research. Vol. 42.No 15. Pages 2955 – 2969.
Ferguson C-J., Lees B., MacArthur E., Irgens C (1998). An application of data mining for product design. IEE Colloquium on Knowledge Discovery and Data Mining. London UK 7-8 May.Pages 5/1-5/5.
Brijs T., Goethals B., Swinnen G., Vanhoof K., Wets G. (1999). Using association rules for product assortment decisions: a case study. Knowledge Discovery and Data Mining Conference, San Diego CA USA.Pages 254-260.
Rygielski C., Wang J-C., Yen D.C. (2002). Data mining techniques for customer relationship management. Technology in Society, Volume 24, Issue 4. Pages 483-502.
Company’s equity research (2005). MasterLink Securities. Retrieved from:
http://web6.masterlink.com.tw/project/english/main_page/files/company_visits/2911_TT_20051028.pdf
Company’s annual reports (2003, 2004, 2005, 2006).
Li & Fung Research Center (2006). Performance of China’s apparel product sectors. China National Commercial Information Center. Industry Series. September. Issue 7.
Tseng M., Ma Q., Su C-J. (1999). Mapping customers’ service experience for operations improvement. Business Process Management Journal.Vol 5. Issue 1. Pages 50-64.
Bateson, J.E (1995). Managing Service Marketing. The Dryden Press, Harcourt Brace College Publisher, Hinsdale, IL.
Howard B.J., The meaning of color. Retrieved from:
http://desktoppub.about.com/od/choosingcolors/p/color_meanings.htm