Quality Improvement of NH1X36B Pre-Printed Box with QM-CRISP DM Approach at PT X
DOI:
https://doi.org/10.22441/oe.2021.v13.i3.028Keywords:
data mining, decision tree, FP-Growth, QM–CRISP DM, six sigmaAbstract
PT X is a manufacturer of cardboard box whose products are indispensable for various fields. The problem identified in NH1X36B pre-printed box, which is a shoes box, is the high defect rate that exceed company target of 2%. This study aims to reduce the defect rate of the product. The Quality-Management (QM) and Cross Industry Standard Process for Data Mining (CRISP-DM) approach was conducted by integrating Six Sigma and data mining. The Business Understanding phase was intended to define business and data mining objectives, SIPOC (Supplier-Input-Process-Output-Customer) diagram, and Critical-to-Quality (CTQ). In Data Understanding phase, it is known that the Defects Per Million Opportunities (DPMO) value is 1210.12. Data preparation phase was carried out with data cleaning, reduction, and discretization. Based on the Modeling result using Decision Tree C4.5 and FP-Growth algorithm, it is known that the dominant attributes causing high rejection are smeared ink, white spots, uneven varnish, and delamination. Decision Tree model accuracy of 90.24% indicates that the model is performing well. Analysis using FMEA yielded priority correction to the causes of smeared ink, uneven varnish, and delamination. Process improvement in Deployment phase was the application of plate cleaning and mounting form, printing process checklist, and SOP for sheet inspection. The improvement plans managed to improve the quality by rising sigma level from 4.533 to 4.648 sigma and decrease defect rate to 1.559%.Downloads
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