Die storage improvement with k-means clustering algorithm: A case of paper packaging business

• Main Authors: Wimalin Laosiritaworn, Pornpailin Kitjongtawornkul, Melin Pasui, Warocha Wansom
• Format: Conference Proceeding
• Published: 2018
• Subjects: Computer Science; Decision Sciences
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85005939740&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/55497
• Institution: Chiang Mai University

© 2016 IEEE. This paper presents die storage improvement for a case study company, who is a manufacturer of made-to-order paper packaging product. One of the critical equipment used to produce paper packaging is the dies used in die cutting machine. These dies are stored in the separate storage room and they are placed on any available shelf slot. Due to the wide variety of product design, number of die stored in die storage room is large and continues to grows every year due to the increasing number of customers. Die storage room has become untidy and packed, which make the die retrieve process become more difficult. K-means clustering, one of the data mining algorithms, was applied to cluster dies into groups based on their size, price and frequency of use. Then the layout of storage room was re-designed based on the new cluster to improve space utilization. After improvement, the time used for die retrieval was significantly reduced.