Abstract: An assembly line model incorporating part shortages is developed to address the issue of reduced production efficiency caused by the shortage of critical parts. The impact of these shortages on production performance is systematically analyzed, and strategies for improvement are proposed. The Markov process is employed for precise analysis of two-station scenarios, while a decomposition-based method is adopted to efficiently approximate the performance of complex multi-station systems. Simulation results demonstrate that the decomposition method significantly enhances computational efficiency while maintaining estimation accuracy, with an average error of less than 0.91% and solution time of under 1 second per iteration. Extensive numerical experiments are conducted to analyze the effects of part shortage probability and part arrival rate on system performance. Results show that, for balanced assembly lines, the optimal location of adjusting part arrival rates and shortage probabilities is mainly concentrated in the central region of the line and its vicinity, with greater improvements observed downstream of the center compared to symmetric locations. It is recommended that the part arrival rate must be adjusted to exceed the minimum processing rate to minimize shortage probabilities and maximize production efficiency. Notably, as the shortage probability decreases, further reductions have a progressively greater impact on throughput improvement. The findings provide theoretical insights and methodological support for the modeling and optimization of assembly lines under part shortage conditions.