The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
R&D Productivity in the Pharmaceutical Industry: Scenario Simulations Using a Bayesian Belief Network
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.
Related Content
Yu Bin, Xiao Zeyu, Dai Yinglong.
© 2024.
34 pages.
|
Liyin Wang, Yuting Cheng, Xueqing Fan, Anna Wang, Hansen Zhao.
© 2024.
21 pages.
|
Tao Zhang, Zaifa Xue, Zesheng Huo.
© 2024.
32 pages.
|
Dharmesh Dhabliya, Vivek Veeraiah, Sukhvinder Singh Dari, Jambi Ratna Raja Kumar, Ritika Dhabliya, Sabyasachi Pramanik, Ankur Gupta.
© 2024.
22 pages.
|
Yi Xu.
© 2024.
37 pages.
|
Chunmao Jiang.
© 2024.
22 pages.
|
Hatice Kübra Özensel, Burak Efe.
© 2024.
23 pages.
|
|
|