Handloom micro-enterprises face persistent productivity challenges arising from long changeover times, ergonomically inefficient workstations, unbalanced operations, and variable process parameters. This study presents an integrated portfolio of optimization techniques—including 5S and visual management, time study and work sampling, Single-Minute Exchange of Dies (SMED) adapted for warp/weft changeovers, ergonomics-guided workstation redesign, line balancing, Overall Equipment Effectiveness (OEE) monitoring, and advanced experimental designs such as the Taguchi method and Response Surface Methodology (RSM). The portfolio is augmented with dimensional analysis for scale-independent optimization and metaheuristic/artificial intelligence (AI) methods for multi-parameter control. An eight-stage implementation in a representative handloom unit demonstrated an OEE increase from 0.52 to 0.84, a 29–35% rise in pieces per shift, a 3.1% reduction in defect rate, and a 35–45% decrease in changeover time. These results confirm that low-cost lean methods, combined with structured experimentation and AI-driven optimization, can deliver sustainable performance gains in small- scale textile operations.
Mahantare et al. (2026) studied this question.