DATA-DRIVEN PROCESS IMPROVEMENT IN TEXTILE SPINNING THROUGH LEAN SIX SIGMA METHODOLOGY

Abstract

The textile spinning industry is a cornerstone of the textile value chain but continues to face pressure to deliver higher quality and efficiency under growing customer expectations. This study applies Lean Six Sigma (LSS) principles using the DMAIC framework to analyze and improve quality performance in a ring-spinning mill. A dataset of 193,695 yarn cones produced over three months was examined. Baseline analysis revealed 20,705 defects (10.69%), yielding a sigma level of ≈2.7 (Z bench 1.24, +1.5σ shift). Using Pareto analysis, yarn breakage emerged as the most critical defect. Root causes were explored through a fishbone diagram and 5-Why analysis, while a Value Stream Map identified non-value-added activities consuming 26% of total cycle time (70 min of 269.5 min). Improvement actions focused on maintenance practices, standard operating procedures, and training. After intervention, defects were reduced to 9,053 (4.67%), and the sigma level improved to ≈3.2, representing a 56.3% reduction in defects. The estimated financial benefit was PKR ~25.3 million per month in reduced losses. The study demonstrates how transparent application of LSS tools can enable spinning mills to enhance quality, reduce waste, and achieve sustainable cost savings.