Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach

Main Article Content

Resty Ayu Ramadhani
Universitas Trisakti

Rina Fitriana
Universitas Trisakti

Anik Nur Habyba
Universitas Trisakti

Yun-Chia Liang
Yuan Ze University

Keywords

Six Sigma, Data mining, FMEA, Apriori Algorithm

Abstract

Six Sigma is of paramount importance to organizations as it provides a structured and data-driven approach, fostering continuous improvement, minimizing defects, and optimizing processes to meet and exceed customer expectations. In response to the increasing defects of packaging product in a cosmetics industry in Indonesia, surpassing the specified 3% tolerance limit, this research conducts a thorough investigation into the root causes, corrective measures, and improvement proposals to elevate product quality. By leveraging the Six Sigma method and data mining techniques, the study systematically addresses the complexities associated with defect reduction in packaging for cosmetics product. The research methodology encompasses defining the problem through SIPOC and Critical to Quality (CTQ) diagrams, measuring via control charts and sigma level calculations, and analyzing using tools like pareto diagrams, Apriori algorithms, fishbone diagrams, and Fault Mode and Effect Analysis (FMEA). Key findings reveal a notable correlation between spot defects and varying colors, leading to pearl defects as identified by the Apriori algorithm. FMEA identifies critical failures, including suboptimal printing plate conditions, clumpy ink usage, and insufficient operator attention to ink filling. The improvement stage proposes practical solutions, such as implementing alarms and buzzers, color-indicator-adjusted ink storage labels, and a structured form for cleaning and monitoring printing plates. These findings carry significant implications, providing a tailored roadmap for enhancing the quality of cosmetic packaging. The anticipated implementation of proposed improvements aims to elevate customer satisfaction by addressing specific pain points in the production process. Furthermore, the research contributes valuable insights to the broader cosmetics industry, offering effective methodologies for defect reduction and quality enhancement in packaging processes.

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Published
Dec 20, 2023
DOI https://doi.org/10.25077/josi.v22.n2.p197-214.2023

Article Details

How to Cite
[1]
R. A. Ramadhani, R. Fitriana, A. N. Habyba, and Y.-C. Liang, “Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach”, J. Optimasi Sist. Ind., vol. 22, no. 2, pp. 197–214, Dec. 2023.
Issue
Vol. 22 No. 2 (2023): Published in December 2023
Section
Articles
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