"Problems with the Application of Standards Productivity in Sewing Room for Non Typical Garment Factories in Egypt"

non-standard medium-sized garment factories in Egypt. The study proposes a practical and simplified model for implementing scientific production benchmarks tailored to the capabilities of these factories. The approach incorporates basic digital tools like Excel, and advanced optimization techniques such as Genetic Algorithms (GA) using MATLAB to enhance production planning and decision-making.

The study is structured into three main chapters:

• Chapter One (Theoretical Framework):

This chapter outlines the general structure of the research, including the introduction, problem statement, objectives, significance, methodology, and literature review. It explores key theoretical foundations related to industrial engineering and productivity measurement, including time calculation methods from an engineering perspective, equipment efficiency analysis, and productivity assessment in medium-sized factories. The chapter emphasizes the importance of shifting from unit-based to assembly-line production systems, the role of Just-in-Time (JIT) production, and the benefits of simulation in reducing costs prior to implementing new production plans. Moreover, it highlights modern time measurement techniques such as MTM, GSD, PMTS, and discusses the application of Genetic Algorithms to identify optimal solutions in production scheduling.

• Chapter Two (Applied Study):

This section details the implementation stages of the proposed system in real-world settings, including the analysis phase, goal-setting, design, testing, application, and evaluation. The study specifically applies production standards to the manufacturing of T-shirts and men’s shirts in small and medium-sized factories, offering a framework for improving operational accuracy and efficiency. One of the major challenges addressed is the difficulty of applying standard production benchmarks, which often limits these factories’ ability to compete globally. The proposed model was successfully applied using Excel to suit factory capabilities, while Genetic Algorithms in MATLAB were used to generate optimized production schedules for garments (Shirt, T-shirt, Polo shirt), mapping the entire process from cutting and sewing to finishing.

• Chapter Three (Results and Recommendations):

This chapter presents a statistical analysis of the results derived from questionnaires directed at factory owners, apparel industry specialists, and professionals. A comparative analysis before and after the model’s implementation demonstrates a clear improvement in performance and time efficiency. The study also evaluates the suitability of the proposed solutions based on the genetic algorithm outputs. The researcher concludes by recommending the wider adoption of this model in similar factory settings and integrating such systems and standards into academic curricula. Furthermore, it encourages the interdisciplinary integration of fashion production with engineering and computational sciences.