Pharmaceutical supply chains are responsible for the manufacturing and distribution of life-saving medicines which requires careful planning of production. The active pharmaceutical ingredient (API) production is a key step which is usually performed on a multi-product manufacturing line in campaign mode since long changeover time are needed when switching products (Marques et al., 2020). These lines can be used to produce both legacy products and products that are under development (Marques et al., 2016) and a decision therefore has to be made about where to produce a given development product and with what capacity. If the product is approved, it enters the launch phase during which the demand rapidly increases and here it is necessary to determine whether to use existing resources to produce the product or construct new lines which requires several years. The new process must then be validated and next the capacity is ramped up which can be described by time- or volume dependent functions (Hansen & Grunow, 2015). An early work considered the production planning effects of introducing a new product on a predefined line with deterministic demands, time-varying capacity parameters and no ramp-up (Sundaramoorthy & Karimi, 2004). The integration of process design and production planning for new product introduction in an existing facility was studied with capacity expansion options for the new product and uncertainty quantification through Monte Carlo sampling (Marques et al., 2016). Stochastic programming has been applied to secondary pharmaceutical campaign production with uncertainties on demand, yield and unplanned downtime with inventory level recourse (Sampat et al., 2020). Additionally, applications of mathematical optimization for pharmaceutical decision support in industry has been reported which shows the maturity of the methods and computational resources for dealing with industry sized problems (Martagan et al., 2019). In this work, the new product introduction in primary pharmaceutical manufacturing is studied from a production planning perspective. A stochastic mixed integer linear programming model is applied which can be used for environments with multiple manufacturing lines on multiple stages where each line can produce a set of products. Campaign scheduling details are included to describe the long changeover times in primary API manufacturing and the intermediate inventories for each product. Allocation variables for a set of new products are included to determine which line(s) in the network should be used to manufacture product for late-stage clinical trials and subsequently for market launch. Capacity variables are included to determine required system capacity depending on the clinical trial outcomes and uncertain demands. Any capacity increase is coupled to a rebuild and validation period with zero production followed by a capacity ramp-up. Exogenous demand uncertainties are included for clinical trial success and market demand and endogenous uncertainties are added to describe the uncertainty involved when producing a new product for the first time. Finally, the Conditional Value-at-Risk (CVaR) is used to model effects of risk-neutral vs. risk-averse decision maker preferences and the model is applied to an industrial case study which shows its applicability to real world decision making.
References
Hansen, K. R. N., & Grunow, M. (2015). Modelling ramp-up curves to reflect learning: improving capacity planning in secondary pharmaceutical production. International Journal of Production Research, 53(18), 5399-5417. https://doi.org/10.1080/00207543.2014.998788
Marques, C. M., Moniz, S., de Sousa, J. P., & Barbosa-Póvoa, A. P. (2016). Optimization and Monte Carlo Simulation for Product Launch Planning under Uncertainty. 38, 421-426. https://doi.org/10.1016/b978-0-444-63428-3.50075-8
Marques, C. M., Moniz, S., de Sousa, J. P., Barbosa-Povoa, A. P., & Reklaitis, G. (2020). Decision-support challenges in the chemical-pharmaceutical industry: Findings and future research directions. Computers & Chemical Engineering, 134, 106672. https://doi.org/10.1016/j.compchemeng.2019.106672
Martagan, T., Limon, Y., Krishnamurthy, A., Foti, T., & Leland, P. (2019). Aldevron Accelerates Growth Using Operations Research in Biomanufacturing. INFORMS Journal on Applied Analytics, 49(2), 137-153. https://doi.org/10.1287/inte.2018.0984
Sampat, A. M., Kumar, R., Pushpangatha Kurup, R., Chiu, K., Saucedo, V. M., & Zavala, V. M. (2020). Multisite supply planning for drug products under uncertainty. AIChE Journal, 67(1). https://doi.org/10.1002/aic.17069
Sundaramoorthy, A., & Karimi, I. A. (2004). Planning in Pharmaceutical Supply Chains with Outsourcing and New Product Introductions. Ind. Eng. Chem. Res., 43, 293-8306.
