This methodology can be applied to any apparel production to enrich the quality while identifying and reducing the defects in the mass production. Considering all information and condition of the production system, feasible solutions are proposed to improve the system and maintain the development. Cause-effect diagram, and Failure mood and effect analysis (FMEA) are applied to get the root causes of the problems and to prioritize the severe risks. The present sigma level of the production is 3.12 which could be improved significantly with appraisal of the total production system. It helps to find a reliable sigma level to understand the condition. Probability distribution is included in the exploratory data analysis for uncertainty quantification and propagation to have a genuine approximation of the total amount of defects from the production batch.
Control chart and Pareto chart are used to find out the problematic production lines and the dominant factors. The crucial defects are identified with experts and workers, and the information are documented with necessary exploratory analysis. The DMAIC (Define, Measure, Analyze, Improve, and Control) approach of six sigma philosophy is used in this research with effective quality tools in different phases. This paper proposes a probability based six sigma approach to improve the process and create a better work environment for the garment industry using quantitative data. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS.
However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry.