Advertisement

Aligning Data Analytics and Supply Chain Strategy in the Biopharmaceutical Industry

  • Mark Holder
  • Amit Devpura
  • Anthony Lee
  • Suresh ChandranEmail author
Chapter
Part of the Advances in Analytics and Data Science book series (AADS, volume 1)

Abstract

Much has been written recently about the important role that data and analytics will play in improving productivity and profitability of companies in the biopharmaceutical industry. Data analytics will be a source for value creation and sustained competitive advantage for companies as new technologies like the Internet of Things and digitization of supply chain play a role in transitioning this industry into a more customer-centric model. This paper provides an overview of the status of the pharmaceutical industry and role that data analytics plays in supply chain management. The objective of this paper is to provide a use case example of implementation of a supply chain blueprint model including specifics of technology platforms, planning and optimization tools, and value stream mapping that have enabled tremendous cost savings at AstraZeneca. Lessons learned from experience with consulting to other companies in the biopharmaceutical space in the area of data analytics and strategy are outlined. The importance of fostering a two-way dialogue between members of the business community and educators and introducing new programs like the future leaders program and Supply Chain Boards in bridging the gap between theory and practice through meaningful partnerships is also discussed.

Keywords

Pharmaceutical industry Supply chain management Data analytics Technology platforms Planning and optimization tools Value stream mapping 

References

  1. AstraZeneca. (2017). AstraZeneca Website. Retrieved from https://www.astrazeneca.com/our-company.html
  2. Champagne, D., Hung, A., & Leclerc, O. (2015). The road to digital success in pharma. McKinsey White Paper. Retrieved from http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-road-to-digital-success-in-pharma
  3. Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 4(36), 1165–1188.Google Scholar
  4. Copping, R., & Li, M. (2016). Analytics: The promise and challenge of big data for pharma. Harvard Business Review. Retrieved from https://hbr.org/2016/11/the-promise-and-challenge-of-big-data-for-pharma
  5. Fox, B., Paley, A., Prevost, M., & Subramanian, N. (2016). Closing the digital gap in pharma. McKinsey White Paper. Retrieved from http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/closing-the-digital-gap-in-pharma
  6. Gartner. (2017). When – and when not – to buy additional analytical capabilities for your supply chain planning process. Retrieved from https://www.linkedin.com/pulse/when-buy-additional-analytical-capabilities-your-supply-pradhan
  7. Genpact. (2014). Supply chain analytics. Genpact White Paper. Retrieved from http://www.genpact.com/docs/resource-/supply-chain-analytics
  8. Lee, A. (2014). Big data: An inevitable paradigm shift and two impacts for pharma. Philadelphia, PA: Digital Pharma East Summit.Google Scholar
  9. Lee, A. (2016). Accuracy for profitability – Is this achievable? CBO Bio/Pharma Product Forecasting Summit, Philadelphia, PA.Google Scholar
  10. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt Publishing Company.Google Scholar
  11. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90, 60–68.Google Scholar
  12. Mentesana, M., Rotz, G., Strang, D., & Swanick, M. (2017). 2017 Pharmaceuticals and life sciences trends. Strategy& White Paper. Retrieved from https://www.strategyand.pwc.com/trend/2017-life-sciences-trends
  13. Muhtaroglu, F. C. P., Demir, S., Obali, M., & Girgin, C. (2013, October). Business model canvas perspective on big data applications. In Big Data, 2013 IEEE International Conference on (pp. 32–37). IEEE.Google Scholar
  14. Otto, R., Santagostino, A., & Schrader, U. (2014). Raid growth in biopharma: Challenges and opportunities. McKinsely Quarterly. Retrieved from https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/rapid-growth-in-biopharma
  15. SAS. (2010). Supply-chain analytics: Beyond ERP & SCM. SAS White Paper. Retrieved from https://www.sas.com/resources/asset/SAS_IW_FinalLoRes.pdf
  16. Statista. (2017). U.S. pharmaceutical industry – Statistics and facts. Retrieved from https://www.statista.com/topics/1719/pharmaceutical-industry/
  17. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.CrossRefGoogle Scholar
  18. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246 Retrieved from  https://doi.org/10.1016/j.ijpe.2014.12.031CrossRefGoogle Scholar
  19. Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110 Retrieved from  https://doi.org/10.1016/j.ijpe.2016.03.014CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Mark Holder
    • 1
  • Amit Devpura
    • 1
  • Anthony Lee
    • 2
  • Suresh Chandran
    • 3
    Email author
  1. 1.AstraZenecaCambridgeUK
  2. 2.A4P Inc.PhiladelphiaUSA
  3. 3.Drexel UniversityPhiladelphiaUSA

Personalised recommendations