Large-scale production of a new vaccine, such as the COVID-19 vaccine, is characterized by evolving demand and production yield uncertainties. Further, following its FDA approval for efficacy and safety, a vaccine may not even be manufacturable due to its low production yield and the resulting lack of profitability. To deal with those challenges in practice, a vaccine manufacturer can (stochastically) increase the uncertain yield of a vaccine prior to launching large-scale production by learning through small-scale, experimental production runs about manufacturing conditions that are conducive to raising that yield. After starting large-scale production and receiving revenues from the sale of the vaccine, the manufacturer can continue to stochastically improve vaccine yield by acquiring knowledge from real-time production data. The two key decisions faced by a vaccine manufacturer who seeks to maximize its total expected profit over the time horizon concern the optimal timing and capacity of the vaccine’s large-scale production. Our goal with this paper is to help optimize those decisions and thus contribute to bringing new vaccines to market sooner and more profitably. In particular, to determine the structure of manufacturer’s optimal decisions, we formulate and solve a stochastic, multi-period, sequential decision model, while incorporating the dynamic evolution of vaccine yield uncertainty under those two yield improvement strategies. We establish the optimality of a threshold stopping policy for the timing of the large-scale vaccine production. This policy is found to depend in a fundamental way on the relative stochastic rates of the two yield improvement strategies. We characterize the manufacturer’s optimal capacity decision and identify conditions under which, for a new vaccine, optimal capacity and production yield become substitutes. We analyze the implications of our results and underlying yield improvement strategies for rendering a new vaccine large-scale manufacturable, and bringing it to the market sooner during a pandemic period
