Stochastic Simulation Uncertainty Analysis to Accelerate Flexible Biomanufacturing Process Development

TL;DR

This paper introduces a metamodel-assisted stochastic simulation framework to quantify and decompose uncertainties in biomanufacturing process models, enabling faster and more reliable process development.

Contribution

It presents a novel uncertainty analysis framework that accounts for both simulation and model uncertainties using bootstrapping and variance decomposition, supported by asymptotic theory.

Findings

Framework effectively produces confidence intervals considering uncertainties.

Variance decomposition identifies dominant sources of uncertainty.

Empirical results show good finite-sample performance.

Abstract

Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we propose a general metamodel-assisted stochastic simulation uncertainty analysis framework to accelerate the development of a simulation model with modular design for flexible production processes. There are often very limited process observations. Thus, there exist both simulation and model uncertainties in the system performance estimates. In biopharmaceutical manufacturing, model uncertainty often dominates. The proposed framework can produce a confidence interval that accounts for simulation and model uncertainties by using a metamodel-assisted bootstrapping approach. Furthermore, a variance decomposition is utilized to estimate the relative contributions from each source of model uncertainty, as well as simulation uncertainty. This information can be used to improve the system mean performance estimation. Asymptotic analysis provides theoretical support for our approach, while the empirical study demonstrates that it has good finite-sample performance.