Possibility Extent and Possible Alternatives Preorder Type-2 Fuzzy Analytical Hierarchy Process (PE&PAP-AHP) to improve pharmaceutical R&D productivity

The major challenges of the pharmaceutical industry are optimizing global expenditure on medicines and maximizing the effectiveness of pharmaceutical products while minimizing the Research and Development (R&D) costs. The commercial success rate mainly causes the decline in R&D productivity across the pharmaceutical industry, the discovery of new drugs, compliance with regulations, and the R&D cycle time. The lack of comprehensive Multi-Criteria Decision Making (MCDM) methodologies leads to ineffective decisions for setting priorities to improve the productivity of pharmaceutical R&D. To increase the precision and accuracy required to improve productivity in Pharmaceutical R&D, in this study, Possibility Extent and Possible Alternatives Preorder Type-2 Fuzzy Analytical Hierarchy Process (PE&PAP-AHP) type-2 fuzzy logic MCDM methodology is developed. Specifically, the type-2 fuzzy geometric mean of PE&PAP-AHP methodology addresses the weakness of traditional methods and correctly reflects the subtle differences in evaluating decision-makers’ opinions. It also constructs fuzzy positive reciprocal matrices, acknowledging the indifference existing among alternatives. PE&PAP-AHP also makes it more accurate during the defuzzification stage of solving the multi-criteria decision-making problem than the type-1 fuzzy approach. It is achieved using two principles: (I) ranking the alternatives by a type-2 fuzzy partial preorder and (II) ranking the alternatives by a type-2 fuzzy total preorder. To compare the results with a type-1 fuzzy, based on the results of the PE&PAP-AHP methodology, the score of the best alternative is 5.2% more than the second-best alternative, whereas, per the type-1 fuzzy, the score of the best alternative is only 3.6% more than the second-best alternative.