Applications of Statistical Process Control Quality Improvement Tools and Techniques and a Simulation Model in a Garment Manufacturing Company

Abstract

In the dynamic landscape of contemporary industrial production achievement in consistent product quality is of a paramount importance for any manufacturing enterprise. Among the various industries that require precision and attention to detail the garment manufacturing sector stands as a prime example. In this context this study aims to improve the process of detection and prevention of substandard fabrics entering the production process in a garment manufacturing company. The study utilizes statistical process control techniques such as the Individual-Moving Range (I-MR) control charts to monitor fabric critical-to-quality characteristics. Furthermore acceptance sampling methods are implemented to ensure that only fabrics meeting specifications are accepted for production. Additionally the project addresses sewing-related issues by improving the quality control checkpoints along the sewing line by using simulation. By conducting experiments within the simulated environment different quality control checkpoint configurations are tested to identify the most effective setup. The findings contribute to enhancing quality management practices in fabric procurement and sewing line operations minimizing financial losses improving customer satisfaction and ultimately bolstering the competitiveness of the garment manufacturing company. Furthermore the results shed light on the benefits of using Arena simulation for conducting comprehensive and cost-effective quality control evaluations. © 2024 Elsevier B.V. All rights reserved.