Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments
Name
Goldman_Leising_Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments.pdf
Description
Supply Chain Management capstone research project
Size
2.2 MB
Format
Adobe PDF
Checksum (MD5)
113c94147c4ea53b7f53d1cbb285ccec
Author(s)
Leising, Jordan Michael
Goldman, Olivia Claire
Date Issued
June 16, 2021
Abstract
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.
Subjects
Demand Planning
Healthcare
Supply Chain Strategy
Terms of Use
CC0 1.0 Universal
Persistent DSpace Link
