Introduction
The ready-made garment (RMG) industry faces significant challenges like quickly changing fashion trends, tough competition, and complex supply chains. To maintain its pace and remain profitable, the manufacturers need to make their production line management as efficient as possible.
Line balancing is an important aspect of production line management, where the right number of workers are placed at each step of the production process to prevent delays and keep things running smoothly. This allows companies to produce quality garments at a faster rate and at a lower cost.
This project uses AnyLogic simulation software, which can help in testing various setups in the garment industry. With AnyLogic, manufacturers can safely experiment with different production scenarios in a virtual environment and find the best ways to organize their lines. This helps to solve problems before they occur and optimize the manufacturing process.
Simulation model
In this study of manufacturing process optimization, the researchers used AnyLogic software to model a T-shirt production line.
The initial scenario showed that even though production line management was efficient, low operator utilization did not let it achieve its daily target of 1,600 T-shirts. The simulation suggested that extended work hours could help meet production goals without hiring more staff.
Subsequent tests tried to better balance the workload, which is an integral part of production line management. In fact, though redistributing tasks made the workers happier, it reduced overall output. Attempts to optimize the manufacturing process by reducing the number of operators led to bottlenecks and inefficiencies.
In the end, merging tasks smartly showed success by maintaining high efficiency and full production capacity with fewer operators. This simulation project identified tangible adjustments that changed the situation in a way that assured the changes would increase productivity before being executed on the actual production floor.
Results
The results have shown that operational hours, task allocation, and workforce can be optimized to achieve productivity targets by cost-effectively adjusting these inputs. The techniques identified in this respect have helped determine how cost penalties could be reduced and productivity gains maximized.
The most successful strategy was the one that showed that intelligent consolidation of tasks enabled an improvement in efficiency and output. There was no significant increase in operational costs.
Overall, these results provide a roadmap for improved production practices. Additionally, they make it clear that production line management must be flexible to align production plans with fluctuating demand levels throughout the year.
Read also: Discover how simulation modeling can revolutionize manufacturing by optimizing processes and reducing costs, as demonstrated through various successful case studies.