SPC and Kaizen Reveal Dominant Yarn Production Defects: SPC dan Kaizen Mengungkap Cacat Produksi Benang yang Dominan - mukamat dwiki hariantoro; indah apriliana sari wulandari (Academia Open, 2025)
General Background: Quality control in textile manufacturing is essential to maintain production consistency and minimize nonconforming products that can disrupt operational performance. Specific Background: PT EQY, a yarn spinning manufacturer, recorded 1,475 bales of nonconforming products from a total production of 39,719 bales during August 2023–July 2024, exceeding the company’s tolerance threshold and indicating quality deviations within the production process. Knowledge Gap: Prior quality monitoring within the company lacked integrated analytical methods that systematically combined statistical monitoring tools with continuous improvement strategies to comprehensively identify defect patterns and corrective actions. Aims: This study aims to identify dominant defect types in yarn production, evaluate process stability using Statistical Process Control tools, and formulate corrective strategies based on Kaizen principles. Results: The analysis identified three dominant defect categories, including swallot defects totaling 560 bales, products without tail totaling 473 bales, and color inconsistency totaling 442 bales. Control chart analysis indicated several production periods exceeding statistical control limits, reflecting unstable production processes. Root cause analysis using fishbone diagrams identified machine maintenance scheduling, production methods, workforce discipline and training, and environmental conditions as primary contributing factors. Novelty: This research integrates Statistical Process Control with Kaizen-based analytical frameworks, including 5W-1H analysis, Five M checklist, and 5S implementation, within yarn production quality monitoring. Implications: The proposed analytical framework provides structured guidance for identifying production deviations, supporting systematic waste reduction and continuous quality monitoring in textile manufacturing operations. Highlights: Swallot Category Recorded the Highest Nonconforming Quantity During the Observation Period. Statistical Monitoring Identified Several Production Months Exceeding Control Boundaries. Root Cause Mapping Identified Maintenance Routines, Operator Training, Procedural Practices, and Workplace Conditions as Primary Contributors. Keywords: Statistical Process Control, Kaizen, Yarn Production, Quality Monitoring, Production Defects