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Optimization of Fit for Mass Customized Apparel Ordering Using Fit Preference and Self Measurement.

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Master's thesis,

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Mass customized apparel production holds great promise for revitalizing garment manufacturing in this country. Improved production processes like flexible manufacturing, Computer Assisted Design CAD, Computer Assisted Manufacturing CAM, and single-ply cutting have allowed customized, single garment production runs to become cost effective. In order for firms in the apparel industry to successfully implement this promising new production program, ordering methodologies must be established that provide customized garments with acceptable fit for the consumer. My first hypothesis is that the inclusion of fit preference queries in an apparel ordering model will improve the accuracy of size predictions. My second hypothesis is that these fit preference queries combined with an optimized method of self-measurement have the greatest potential to predict accurate garment sizes. This study is designed to evaluate the effectiveness of such ordering models using mens casual shorts as the primary test garment. I conducted research in two phases with a pilot and primary test. Male, college students within a specified waist size range were recruited and asked to report self- measurements and fit preferences on a mock internet website. These subjects were then scheduled for a fit testing session where they were measured by expert evaluators and tried on a series of test shorts. The first three short sizes presented were predicted using a size prediction model and data from self-measurement, expert measurement, and self-measurement plus reported fit preferences. In order to determine their optimum size, test subjects assessed up to a total of six shorts until they selected a pair with the perceived best fit. Subjects were also presented with a background questionnaire that asked demographic and apparel purchasing questions.

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  • Logistics, Military Facilities and Supplies

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