Application of K-Means Clustering to Enhance the Quality Control and the Impact of the Productivity in the Apparel Industries

Abstract

Fabric inspection systems for measuring the quality are an essential part of producing best quality product in an apparel industry. The object of the research work is to analyze the acceptable fabric grade point based on the fabric grade points category using k-means clustering and their impact on the production. By applying the machine learning tools k-means clustering we categorized that hundred unsupervised data in three groups (A, B and C) based on acceptable grade points, of up to 15 (A), 15 to 30 (B), and 30 to 40 (C) respectively. The upper grade fabric can be consumed to produce better products. Thus, we can improve customer satisfaction as well as positive impact on the apparel manufacturing efficiency on both quality and quantity.