  In biopharmaceutical manufacturing, fermentation processes play a critical
role in productivity and profit. A fermentation process uses living cells with
complex biological mechanisms, leading to high variability in the process
outputs, namely, the protein and impurity levels. By building on the biological
mechanisms of protein and impurity growth, we introduce a stochastic model to
characterize the accumulation of the protein and impurity levels in the
fermentation process. However, a common challenge in the industry is the
availability of only a very limited amount of data, especially in the
development and early stages of production. This adds an additional layer of
uncertainty, referred to as model risk, due to the difficulty of estimating the
model parameters with limited data. In this paper, we study the harvesting
decision for a fermentation process (i.e., when to stop the fermentation and
collect the production reward) under model risk. We adopt a Bayesian approach
to update the unknown parameters of the growth-rate distributions, and use the
resulting posterior distributions to characterize the impact of model risk on
fermentation output variability. The harvesting problem is formulated as a
Markov decision process model with knowledge states that summarize the
posterior distributions and hence incorporate the model risk in
decision-making. Our case studies at MSD Animal Health demonstrate that the
proposed model and solution approach improve the harvesting decisions in real
life by achieving substantially higher average output from a fermentation batch
along with lower batch-to-batch variability.
