On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

Abstract

Structured abstractObjectives. We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment.Through three structured fuzzy inference systems (FIS), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of innovation risk.Methods and Materials. Three FIS, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to the strategic choices they face.Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base were agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and validated the model. Results.In an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret the results. We have also run regressions to better understand the implications of our results.ii Conclusions. The main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of the system, which may be easily applied to other settings. KeywordsFuzzy inference systems; 2-tuple-based method; innovation risk; R&D; Pharmaceutical laboratories Highlights -Consensual fuzzy sets and rules are used to model innovation risk in large pharmaceutical laboratories, clarifying the system for pharmaceutical decision-makers and stakeholders.-With the aid of experts, we have combined knowledge with large and diverse datasets and embedded it into the fuzzy inference systems.-The fuzzy evaluation model captures both the financial side and the pipeline side of innovation risk of pharmaceutical companies operating in uncertain environments.-The fuzzy evaluation model we develop in this work has been applied to evaluate innovation risk in a sample of 31 large pharmaceutical laboratories covering the period from 2008 to 2013. Acronyms ATCAnatomical, Therapeutic, Chemical (classification system) CFO Chief Financi...

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“…The process followed by the experts to determine a partition started with estimating their preferences regarding the potential location of each label core. Later, once the cores were agreed upon using a 2-tuple method [46], the complete labels were obtained by symmetry [45] in order to achieve strong partitions [50], which have better comprehensibility properties and satisfy important semantic constraints [51]. Figure 7 shows the agreed partitions of all model variables, the parameters of which are shown in Figure 8.…”

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