Quality improvement is a particularly challenging engineering problem, particularly in multistage manufacturing where various processes and activities are performed. The interdependence between these activities and other factors, such as the nature of raw materials, variations in product characteristics, operator qualifications, and machinery, propagates defects and complicates their identification. To address these challenges, this paper presents a generic framework for identifying critical factors that impact product quality, applied to a real-world case study of the dyeing process in textile manufacturing. The framework integrates root cause analysis and statistical analysis, providing a systematic procedure to identify underlying quality issues and the potential factors causing them. An experimental study has illustrated the effectiveness of the proposed methodology. This framework contributes by equipping decision-makers with a methodology to define critical factors needing more effort for improving quality performance characteristics, enabling them to better understand and develop design solutions.
Mecheri et al. (Thu,) studied this question.