Analytical Data for Sewing Production Efficiency: A Model Based on Artificial Neural Networks (ANNs) - Fadil Abdullah; Afriani Kusumadewi; Tina Martina; - Kuswinarti; Fandi Achmad (Politeknik STTT Bandung, 2025)
The labor-intensive apparel manufacturing sector is continually focused on meeting output goals, necessitating continuous improvements in production efficiency. Achieving targets at the lowest feasible cost is crucial for production management efficiency, especially in clothing production. The sewing component plays a vital role in enhancing the usefulness of clothing through a series of steps to produce ready-made garments. To optimize this process, we developed a model using an artificial intelligence (AI)-based method, specifically Artificial Neural Networks (ANNs), to enhance sewing production efficiency. The model focused on optimizing parameters that significantly influenced efficiency. Our results demonstrate that the ANNs model, with 1000 iterations, successfully replicates empirical data with an R-squared value of 0.98. The research introduces the novel use of an ANNs model with a five-node configuration and 1000 iterations, proving effective in optimizing sewing process parameters. This AI-based approach is a powerful tool for improving production efficiency in the textile industry, making significant theoretical and practical contributions. The findings offer substantial practical implications for practitioners in the textile industry and provide a robust framework for optimizing sewing production process parameters to achieve higher efficiency.