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

ABSTRACT

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.

KEYWORDS:

• Pharmaceutical supply chain
• data analytics
• advanced analytics
• logistics
• operations
• healthcare

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Angie Nguyen

Angie Nguyen is a PhD student at Arts et Métiers Institute of Technology within the LAMIH-CNRS laboratory. She received her MSc. in Engineering from Mines Nancy (France) and a MSc. in mathematics and computer science from the University of Lorraine (France). She is currently working as an engineer within Cegedim R&D. Her research focuses on artificial intelligence and data science for decision-support in the healthcare sector. She also teaches operations research, demand forecasting, and new technologies of Industry 4.0 at Mines ParisTech PSL and Arts et Métiers Institute of Technology.

Samir Lamouri

Samir Lamouri received his Ph.D. (1989) from the Institut National Polytechnique of Lorraine (INPL), France. He is currently a full professor at Arts et Métiers Institute of Technology (France). He teaches Production Management, Linear Control, and Simulation. His research is conducted as part of the LAMIH-CNRS, the Automatic Control, Computer Science, and Mechanical research laboratory, also in Paris. His areas of interest include distributed and heterarchical control of discrete event systems (manufacturing, transport, logistics, and services). He is a member of the French CNRS research group, MACS, and author of several publications in the manufacturing domain.

Robert Pellerin

Robert Pellerin is a member of the Department of Mathematics and Industrial Engineering at Polytechnique Montréal and holds the Jarislowsky/SNC-Lavalin Research Chair. He has published about 100 scientific articles in international journals and has been a guest speaker in several countries to discuss issues related to manufacturing execution systems and the adoption of practices associated with Industry 4.0. Before joining Polytechnique, he led various reengineering and implementation projects for integrated management systems and manufacturing execution systems. His industrial and academic career has always focused on developing tools dedicated to the piloting and monitoring of operations, both in projects and in manufacturing organizations. He is a member of the CIRRELT, Poly-Industries 4.0 Lab, and IVADO research groups.

Simon Tamayo

Simon Tamayo, is a specialist in Data Science and Machine Learning. He is currently a Data Science Expert at McKinsey & Company, where he leads the QuantumBlack Data Science Guild in Spanish Latin America. He previously was a professor at Mines ParisTech PSL (Paris) at the Center for Robotics.

Béranger Lekens

Béranger Lekens holds a degree in statistics, epidemiology, and public health. He has more than 15 years of experience in conducting projects in data science and statistics for the pharmaceutical industry. He is currently leading the Real World Data team within Cegedim R&D, which works along with industries as well as academics to leverage health data across various projects. He is co-author of several publications in the domain of data science and epidemiology.