This paper presents the simulation of a typical cyclic polymer processing operation with respect to the performance of different control strategies for part quality when subjected to disturbances with realistic load-profiles. Disturbances enter the simulation in a complex, probabilistic manner creating a unique flexibility in load representation which helps the simulation more accurately capture the performance of real processing operations. Realistic simulations can provide necessary information and be less costly than benchmark studies on actual processes. The objective of the research is to examine the effect of different control strategies on the overall control of part-to-part quality, not the control of the continuous processes that occur in part fabrication. The performance of six different control strategies is compared; two strategies are conventional feedback, and four strategies are statistically based. The statistically based algorithms effect closed loop control, but only when a true load exists. Results are analyzed using statistical analysis of variance (ANOVA) via a completely randomized block experimental design, and Duncan＇s multiple range test is used to rank the control strategies. The results indicate that as more disturbances enter the system, the conventional controllers perform better than the other strategies. When fewer disturbances are present, however, the statistically based controllers perform better. The most notable result is that one statistically based controller, the Western Electric runs rules controller, performs well over the entire range of disturbances.