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Application of Lean Six Sigma in Minimizing Waste and Enhancing Product Quality in Garment Manufacturing Bhagaskara, Muhammad Nabil; Pradana, Bayu Ilham
Jurnal Kewirausahaan dan Inovasi Vol. 4 No. 4 (2025)
Publisher : Fakultas Ekonomi dan Bisnis Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jki.2025.04.4.16

Abstract

Purpose – This study aims to analyze the causes of defects and waste in the T-shirt production process at PT Magnum Attack Indonesia using the DMAIC (Define, Measure, Analyze, Improve, Control) cycle and to provide improvement recommendations based on the analysis. Design/Methodology/Approach – This study employs an applied quantitative descriptive approach. Data were collected through interviews, observations, and document studies at PT Magnum Attack Indonesia. Interviews involved the company director, production manager, and two production operators. The analysis was conducted using Value Stream Mapping (VSM), p-chart control mapping, and Failure Mode and Effect Analysis (FMEA) to identify waste, defects, and improvement priorities. Findings – The results show that the T-shirt production process contains several non-value-adding activities, including transportation waste, unnecessary inventory, unnecessary motion, and defects. The production process is statistically stable based on the p-chart analysis, although several defects were identified, such as smeared screen printing, peeling screen printing, and uneven stitching. Improvement recommendations based on the highest Risk Priority Number (RPN) include implementing 5S, increasing transportation capacity, and establishing a maintenance plan. The implementation of Lean Six Sigma improves Process Cycle Efficiency (PCE) from 22.01% to 34.23%. Originality/Value This study offers practical evidence of how Lean Six Sigma tools, applied through the DMAIC cycle, can significantly enhance process cycle efficiency in T-shirt production. By combining VSM, control charts, and FMEA, it provides a comprehensive and data-driven analysis that delivers actionable insights for improving quality and operational performance in apparel manufacturing.