Modeling human performance using simulation at aircraft manufacturing system

• Main Author: Zunnadrah, Zolkepley
• Format: Thesis
• Language: English; English
• Published: 2021
• Subjects: TS155-194 Production management. Operations management; HF5549-5549.5 Personnel Management. Employment
• Online Access: https://etd.uum.edu.my/10132/1/s818968_01.pdf; https://etd.uum.edu.my/10132/2/s818968_02.pdf; https://etd.uum.edu.my/10132/

Manufacturing sectors play one of the important roles in generating economics in Malaysia. In aircraft composite manufacturing, an assembly line, especially in layup processes, is critical and requires high skill workers and a huge amount of capital and cost. However, the resignation of expert workers will impact the human performance in an assembly line which can cause the bottleneck problems, the decline of throughput rates and the increased number of scrapped parts. Therefore, this research develops a hybrid simulation model to analyze the human performance at the assembly line in aircraft composite manufacturing by integrating discrete-event simulation (DES) and system dynamics (SD), namely I-SIMHUPAL. The DES model was run using Arena to get the outputs to cater for the operational level of the assembly line. Then, the outputs such as Work in Process (WIP) and the number of throughput were considered as input data in the SD model. Next, the SD model was run using Vensim to determine the worker workload, skill matured time, and number of required new and expert workers. The SD model was used to measure human performance. The number of throughput from SD model was used again as input data in the DES model to identify a suitable target production based on the number of available new and expert workers. The result showed the number of throughput declined 0.72% and WIP of Product A decreased 0.95% in the assembly line when the number of expert workers decreases. The models help the management identify the human performance in improving the quality of the production at an assembly line. The findings of this study can guide authorities to improve the human performance in an assembly line.