Data Mining To Improve Industrial Standards And Enhance Production And Marketing: An Empirical Study In Apparel Industry

摘要

Apparel production is a high value-added industry in the global textile manufacturing chain. Standard size charts are crucial industrial standards for high-tech apparel industries to maintain competitive advantages in knowledge economy era. However, these industries suffering from production management and marketing often find it hard to obtain the accurate standard size charts. In addition to conventional experience approaches, there is an urgent need to develop effective mechanism to find the industrial standards that are the most suitable to their own industries. This study aims to fill the gap by developing a data mining framework based on two-stage cluster approach to generate useful patterns and rules for standard size charts. The results can provide high-tech apparel industries with industrial standards. An empirical study was conducted in an apparel industry to support their manufacturing decision for production management and marketing with various customers' needs. The results demonstrated the practical viability of this approach. Moreover, since the anthropometric database must be repeatedly updated, standard size charts may also be continuously renewed via application of the proposed data mining framework. By applying the proposed framework for solving industrial problems, these industrial standards will remain continually beneficial for both production planning and reducing inventory costs, while facilitating production management and marketing.