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Integrated framework based on importance performance analysis and fault tree analysis for defect reduction in garment manufacturing Emi Pratiwi; Hartomo Soewardi
JRTI (Jurnal Riset Tindakan Indonesia) Vol. 11 No. 1 (2026): JRTI (Jurnal Riset Tindakan Indonesia)
Publisher : IICET (Indonesian Institute for Counseling, Education and Therapy)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/30036866000

Abstract

Quality improvement is essential for enhancing the competitiveness of export-oriented garment manufacturers. However, production defects during cutting, sewing, and finishing continue to increase rework, material waste, production costs, and delivery delays, indicating weaknesses in existing quality management practices. This study aimed to develop an integrated quality improvement framework by identifying priority production quality attributes and diagnosing the root causes of manufacturing defects at PT Anggun Kreasi Garment. A descriptive quantitative case study was conducted involving **30 respondents** selected through purposive and proportional stratified random sampling. Data were collected using a validated questionnaire (Cronbach's Alpha = 0.969 for importance and 0.962 for performance), supported by observations, interviews, and company quality records. Customer Satisfaction Index (CSI), Importance–Performance Analysis (IPA), and Fault Tree Analysis (FTA) were applied sequentially to evaluate production quality, identify priority attributes in Quadrant I, and determine the underlying causes of defects. The analysis identified 22 priority quality attributes across the cutting, sewing, and finishing departments. The dominant root causes were associated with machine reliability, work methods, human factors, material management, workplace conditions, and quality control practices. The proposed integrated IPA–FTA framework supports evidence-based quality improvement by linking performance evaluation with root-cause analysis, enabling more focused and effective decision-making for continuous quality improvement in garment manufacturing.