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In recent years, data analytics in pharmaceutical supply chains has aroused much interest as it has the potential of enabling better supply and management of healthcare products by leveraging data generated by modern systems. This article presents the current state, opportunities, and challenges of data analytics in pharmaceutical supply chains through a systematic literature review surveying the Scopus, ScienceDirect, and Springerlink databases. 85 publications from 2012 to 2021 were reviewed and classified based on the research approach, objective addressed, and data used. The contributions of this paper are threefold: (i) it proposes a framework focused on challenges and data resources to assess the current state of data analytics in pharmaceutical supply chains; (ii) it provides examples of techniques exemplified that will serve as inspiring references; and (iii) it gathers and maps existing literature to identify gaps and research perspectives. Findings outlined that despite promising results from machine learning algorithms to address drug shortages and inventories optimisation, the various data resources have not yet been fully harnessed. Unstructured data have barely been used and combined with other types of information. New challenges related to green practices adoption and medicines supply during crises call for further applications of advanced analytics techniques.

Data analytics in pharmaceutical supply chains: state of the art, opportunities, and challenges