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 the two-station scenario, while decomposition methods are used 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 times under 1 second for each run. Extensive numerical experiments are conducted to analyze the effects of shortage probability and part arrival rate on system performance. For balanced production lines, the optimization of part arrival rates and shortage probabilities is most effective near the center of the line, with greater improvements observed downstream of the center compared to symmetric locations. It is recommended that the part arrival rate be adjusted to exceed the minimum processing rate, while minimizing shortage probabilities to enhance production efficiency. Notably, as the shortage probability decreases, further reductions have a progressively greater impact on throughput improvement. The findings provide a theoretical basis and methodological support for the modeling and optimization of production lines under part shortage conditions.