Project Portfolio Management at Novartis Pharma
In an award-winning case study, researchers at the London Business School used @RISK, PrecisionTree, and RISKOptimizer to demonstrate some of the analytical techniques used by Novartis for R&D project selection and prioritization.
R&D project selection and prioritization problems are a recurrent issue of strategic importance for Novartis. In the pharmaceutical industry, project portfolio decisions are crucial to the viability and success of a company, and require huge investment commitments. The case study developed by the LBS researchers illustrates the usefulness of management science methods for this purpose. In particular, decision analysis, simulation and optimization are used to analyze and optimize project portfolio decisions. This is relevant in today’s pharmaceutical industry, as it is facing an increasingly tough environment and needs to improve the quality of decision-making in order to maintain profitability.
The London Business School case starts with an overview of the pharmaceutical industry and the challenges in the drug development process, including the massive required R&D investments, possibility of failure, and commercial uncertainty. Subsequently, the case discusses the work performed by the project portfolio group at Novartis. In other pharma companies, this group is sometimes referred to as the “project management group” or the “decision analysis group.” They collect the project data and requirements submitted by the individual therapy areas and collate them to analyze the global company portfolio. The case reports Novartis’s decision process, focusing on the role of the Innovation Management Board (IMB), which takes the portfolio decisions at Novartis Pharma. It also presents an extensive discussion of the issues in project portfolio management.
The London Business School case study won the 2004 INFORMS Case Competition, a prestigious competition for the best case study in Operations Research/Management Science, organized by the Institute for Operations Research and Management Science.