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R&D Productivity in the Pharmaceutical Industry: Scenario Simulations Using a Bayesian Belief Network

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  • F.W. (Ward) van Vierssen Trip

    (Delft University of Technology, Delft, The Netherlands)

  • Nam C. Nguyen

    (University of Adelaide, Adelaide, Australia)

  • Ockie J.H. Bosch

    (University of Adelaide, Adelaide, Australia)

Abstract

The pharmaceutical industry is in a R&D productivity crisis. Rapidly increasing development costs, decreasing profitability of new medical entities and missing breakthrough innovations are negatively affecting the future of the pharmaceutical industry. This complex problem requires a systems thinking approach to find effective solutions. In this study, a general pharmaceutical R&D productivity system has been modeled as a Bayesian Belief Network (BBN). This model is based on a literature review and the mental model of experts in the pharmaceutical field. The model does not only support users to understand the system but is also able to simulate different future scenarios. A blockbuster drug scenario, a generic drug scenario, and a personalized drug scenario has been modeled with three different corresponding outcomes. These simulations enables decision makers to identify the leverage points of the pharmaceutical R&D productivity system. These leverage points could be the foundation of any further strategy development. The R&D productivity system archetype is potentially applicable for other R&D intensive industries.

Suggested Citation

  • F.W. (Ward) van Vierssen Trip & Nam C. Nguyen & Ockie J.H. Bosch, 2015. "R&D Productivity in the Pharmaceutical Industry: Scenario Simulations Using a Bayesian Belief Network," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 6(1), pages 1-16, January.
    • Handle: RePEc:igg:jsds00:v:6:y:2015:i:1:p:1-16
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