Strategic decision-making in the pharmaceutical industry: A unified decision-making framework. (2nd November 2018)

• Record Type: Journal Article
• Authors: Marques, Catarina M.; Moniz, Samuel; de Sousa, Jorge Pinho
• Is Part Of: Computers & chemical engineering. Volume 119(2018)
• Page Start: 171
• Page End: 189
• Publication Date: 2018-11-02
• Subjects: Uncertainty -- Strategic decisions -- Process design -- Capacity planning -- Multi-Objective Integer Programming -- Pharmaceutical industry; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.09.010
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A new unified decision-making framework to address the product launch planning problem under uncertainty, integrating strategic (process design) and tactical (production plan) decisions; development of a Multi-Objective Integer Programming model to determine the strategic process design decisions that "maximizes" productivity, simultaneously considering the decision-maker risk attitude; a Pareto analysis based on the productivity level as a solution performance indicator, and a risk analysis to support and guide the decision-maker final solution is developed; results clearly show the influence of different risk attitudes in the final design strategy to be adopted by the company; the model has proven to be effective in determining the unique strategic solution, balancing investment costs to develop a new drug and the production capacity allocation to accommodate the uncertain future needs. Abstract: The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to tackle these problems the widely used multi-stage/scenario-based optimization formulations are still ineffective, especially for the first-stage ( here-and-now ) solutions where uncertainty has not yet been revealed. This study extends the authors' previous work addressing the stochastic product-launch planning problem, by developing a new Multi-Objective Integer Programming model, embedded in a unified decision-making framework, to obtain the final design strategy that "maximizes" productivity while considering the decision-maker preferences. An approximation of the efficient Pareto-front is determined, and a subsequent Pareto solutions analysis is made to guide the decision process. The developed approach clearly identifies the process designs and production capacities that "maximize" productivity as well as the most promising solutions region for investment. Moreover, a good balance between investment and capacity allocation was achieved.