Publication Date
Authored by
Olivia Claire Goldman, Jordan Michael Leising
Advisor(s): Jarrod Goentzel
Topic(s) Covered:
  • Demand Planning
  • Healthcare
  • Strategy

Biopharmaceutical companies are increasingly exploring cutting-edge novel gene therapies (GTs) in an effort to cure rare diseases. This capstone develops and tests a practical forecasting framework for sharing capacity across Roche’s evolving GT portfolio and driving strategic global supply chain network design. Our problem is challenging, even by the highly regulated pharmaceutical industry standards, with: (1) substantial R&D and mergers and acquisitions investments, (2) some of the world’s smallest disease populations, (3) one-time patients, (4) lacking commercial infrastructure, and (5) scarce historical or long- term pipeline data. We created three forecast types based on the target disease state knowledge available to predict an asset’s prevalence and incidence patient adoption curves. The resulting asset forecasts are also aggregated into a comprehensive portfolio dashboard. Our user-friendly point model enables stakeholders to market size the prospective current pipeline and risk pool portfolio capacity by clinical phase. We then applied simulations to illustrate long-term product launch scenarios. These tools cater to various stakeholders helping address the key GT production planning and asset targeting problems. Roche has already began utilizing our capstone to methodically consider unknown future assets, with unknown orphan disease severity or populations, in their strategic make vs. buy GT network design decisions.

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