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Motivated by critical challenges and needs from biopharmaceuticalsmanufacturing, we propose a general metamodel-assisted stochastic simulationuncertainty analysis framework to accelerate the development of a simulationmodel with modular design for flexible production processes. There are oftenvery limited process observations. Thus, there exist both simulation and modeluncertainties in the system performance estimates. In biopharmaceuticalmanufacturing, model uncertainty often dominates. The proposed framework canproduce a confidence interval that accounts for simulation and modeluncertainties by using a metamodel-assisted bootstrapping approach.Furthermore, a variance decomposition is utilized to estimate the relativecontributions from each source of model uncertainty, as well as simulationuncertainty. This information can be used to improve the system meanperformance estimation. Asymptotic analysis provides theoretical support forour approach, while the empirical study demonstrates that it has goodfinite-sample performance.

Stochastic Simulation Uncertainty Analysis to Accelerate Flexible Biomanufacturing Process Development