Strategic Implementation of Artificial Intelligence and Machine Learning in the Pharmaceutical Sector: A Dual Hesitant Fuzzy Group Decision Making Approach

The pharmaceutical industry is regarded as one of the leading industries in Bangladesh. Further developments and sustainability can be offered through the integration of Artificial Intelligence (AI) and Machine Learning (ML) in this industry as vast amounts of data can quickly be processed and analyzed with accuracy by AI which is crucial for drug discovery, and quality improvement of medicines within minimal costs. However, successful AI implementation requires strategic planning based on various factors. In this study, eight major strategies were proposed to implement and integrate AI and machine learning and also assessed based on eight criteria related to artificial intelligence and machine learning. Feedbacks were taken from eight industry experts as Dual Hesitant Fuzzy Elements (DHFEs) to build the strategy versus criteria matrices. The feedbacks were then aggregated by using a group decision making method: Dual Hesitant Fuzzy Heronian Mean (DHFHM). The scores of those aggregated DHFEs belonging to each criterion were determined and the scores of all criteria under a strategy were summed up to find the ultimate score of that corresponding strategy. The strategies were then ranked based on their scores. Finally, the robustness of the result obtained from DHFHM, sensitivity analysis was conducted by changing the values of parameters and it was found that similar results with negligible deviation were generated by the operator and through this the proposed model was proven to be validated.