(599u) Capacity Planning in Novel Pharmaceutical Supply Chains Under Clinical Trials and Demand Uncertainties

Upon successful completion of clinical trials and the FDA process, a new drug is introduced commercially (launched) into the market under patent protection. The launch phase includes capacity planning for meeting customer demands, and optimizing the production and inventory from launch to peak demands. Since time to market is the major driver in the pharmaceutical industry, decisions pertaining to the launch phase are usually made in parallel with the development process. Clearly, these decisions have to be made under the uncertainties of clinical trials and market demands. Thus, a holistic framework to facilitate the above strategic decision-making process in an integrated manner is highly desired (Shah, 2004).

Existing approaches for simultaneous drug development and capacity planning were developed for traditional pharmaceutical supply chains in which the primary and secondary production facilities are operated in geographically different locations (Maravelias and Grossmann, 2001; Shah, 2004). Furthermore, the batch mode of manufacturing has dominated the pharmaceutical industry for decades. Recently, the Novartis-MIT Center for Continuous Manufacturing has embarked on an innovative project to shift from the batch mode of manufacturing to the continuous mode, where raw materials through the active ingredients to the finished products are processed seamlessly in an integrated facility. Such an integrated end-to-end production scheme is very promising for several reasons: a) low costs of inventory, logistics, and production, b) short supply-chain cycle times, c) less exposure to supply-chain disruptions, and d) resilient supply chain. Thus, the option of continuous integrated manufacturing in the pharmaceutical industry brings in additional challenges and opportunities in the context of drug development and investment strategy. In this work, we develop a holistic optimization under uncertainty framework to address capacity planning for pharmaceutical supply chains that employ novel integrated production schemes. Since the outcomes of clinical trials as well as the market demands for products are uncertain, the problem naturally leads to a stochastic programming problem, which is formulated as a mixed-integer linear programming (MILP) model. However, the model size can grow dramatically with the number of scenarios, resulting in intractable large-scale MILP models. The underlying problem structure motivates a novel solution method to solve these large-scale models, and we illustrate the application of the proposed framework using several case studies.

References
Shah, N., 2004. Pharmaceutical supply chains: key issues and strategies for optimization. Comput. Chem. Eng. , 28, 929–941.
Maravelias, C.T., Grossmann, I.E., 2001. Simultaneous planning for new product development and batch manufacturing facilities. Ind. Eng. Chem. Res., 40, 6147–6164.