| dc.description.abstract | How to reduce order delays and decrease average process time without increasing costs is an ongoing improvement issue for factories. This is aimed at enhancing profitability and reducing expenses. In the current post-pandemic era, global industries are facing the impact of tight profitability and low growth. Therefore, this issue becomes even more crucial. The case in this study previously relied on experiential-based scheduling arrangements in the factory. If data can be processed through a scientific, systematic, continuous, and stable approach using system software, valuable and useful reference information can be obtained. This information, along with relevant user-defined target data, can serve as evaluation indicators for decision-makers in selecting scheduling strategies. Another advantage of computer simulation software is that once the production conditions change, only need to adjust the required parameter values in the established model allow for the observation of corresponding output variations. The system-level impacts provide decision-maker references on the suitable for practical applications.
This research focuses on the front-end process of semiconductor packaging factory and applied Arena simulation software to construct models and use simulation techniques. The research evaluates the scheduling methods used by production control personnel by collecting historical scheduling data, and the dispatching time is randomness to construct the model. The differences in the models are then examined, and an analysis is conducted comparing the original production line schedule with the desired improved dispatching rules. The research compares three dispatching rules: First-In-First-Out (FIFO), Shortest Processing Time (SPT), and Earliest Due Date (EDD), against the original schedule of the production line. The simulation results demonstrate that all three scheduling methods can improve two performance evaluation indicators of the existing scheduling approach: 1. Reduction in average process time, and 2. Decreasing order delays. The SPT method shows the most significant improvement, with an average reduction of 12.7% in the average process time per order lot, equivalent to 28.5 hours. The average order delay rate can be improved by 4.2% per month, equivalent to approximately 2.6 lots | en_US |