Abstract:
This study introduces a quality indicator for the apparel industry that can be used to identify
the critical styles out of a new set of styles. This indicator can be incorporated in designing the
production procedure by taking proactive decisions on the critical styles. The study presents
a Bayesian classification in the context of supervised learning. In particular, na¨ıve Bayes algorithm is employed to classify the apparel styles when a new set of styles is received to the
company. Pareto chart is then used to identify the critical styles based on 80% rule. In order
to apply the procedure, the end-line quality inspection data for 80 styles of 4 different brands
were collected for 4 months. The defect/non-defect status and corresponding defect categories
for each style were recorded. Style descriptions for all styles were collected from customer
specification sheets. In order to reflect the key qualities of the style, the variables for the style
descriptions were chosen. Afterwards, the classification model was applied to the training set
of 60 style descriptions, and the predictions were made for the rest of the styles. Based on the
“confusion-matrix” of the classifier, obtained from Python programming, the final precisions
of the classifier for brands Lululemon, Lacoste, CK, and Nike were 0.85, 0.50, 0.85 and 0.50
respectively, with a 0.875 overall accuracy of the classifier. For an apparel manufacturer, what
the study proposes is of great importance, for it enables saving time, cost, labour, and most
importantly the quality, while being efficient in production.
