A Simulation Based Analysis for Sewing Lines of Apparel Industry

Abstract

One of the frequently encountered problems in the labour-intensive apparel industry is the deviation from the target deadlines. The biggest observed reason for this problem is that the standard operation times and therefore production targets are being defined as deterministic values in the stochastic environment. The statistical studies carried out in this project show that the standard deviation of the operation times is too significant to be ignored. The natural randomness in the performance of the workers and additional factors such as fatigue and learning effect are some of the most important reasons that increase the variability in the system. The project is carried out in conjunction with one of Turkey’s top companies within the apparel sector TYH Tekstil A.Ş. Different policies are applied by the company to dynamically solve the problems encountered during production. In the literature review different simulation-based policy methods for a sample order were examined. However since the company’s sewing line has its unique characteristics the reviewed policies alone were determined not to be suitable for use. This project aims to determine the mentioned randomness parameters by analyzing the day-hour variability of the operation times statistically and provide the company with a tool that will simulate the sewing lines ahead of production using the order-specific inputs and data analysis dynamically analyze and improve the performance of the sewing lines by Tabu search algorithm and visually/verbally report any problematic operations/occasions that may arise and performance improvements made by the decision policies for minimizing deviations from target deadlines. To increase the flexibility of the system and make it dynamic the Python programming language was used. © 2023 Elsevier B.V. All rights reserved.