Research Challenges in the Era of AI and Digitization for Sustainable Supply Chains

Year : 2024 | Volume :14 | Issue : 01 | Page : 1-10

Subhash Kumar Dwivedi

  1. Lecturer Department of Commerce, Marwari College Ranchi Jharkhand India


Two main topics have an impact on the adoption of sustainability as a worldwide business mandate. The first is admitting that global supply networks have an impact on sustainability and that “greening” the chain as a whole is necessary. Technology, encompassing “big data,” artificial intelligence (AI), and digitization, is the second. These ideas are now widely accepted. These
innovations are transforming how firms design and manage their supply chains, which have a big impact on sustainability. In the following article, an overview of the most widely accepted concepts in sustainable supply chain research at the present time has been presented.

Keywords: Artificial intelligence, sustainability, big data , supply networks, Global corporations

[This article belongs to Journal of Production Research & Management(joprm)]

How to cite this article: Subhash Kumar Dwivedi. Research Challenges in the Era of AI and Digitization for Sustainable Supply Chains. Journal of Production Research & Management. 2024; 14(01):1-10.
How to cite this URL: Subhash Kumar Dwivedi. Research Challenges in the Era of AI and Digitization for Sustainable Supply Chains. Journal of Production Research & Management. 2024; 14(01):1-10. Available from:


  1. Boyer, K. K., & Swink, M. (2008). “A holistic understanding of… supply chain management demands multiple approaches.” *Journal of Supply Chain Management, 44*(4), 6-12.
  2. Ketter, W., Collins, J., Reddy, P., & Flath, C. M. (2016). Methodological diversity for supporting interdisciplinary research in supply chain sustainability. *Sustainable Computing: Informatics and Systems, 11*, 63-77.
  3. Boone, J. L., et al. (2019). Assessing bias in data collection: A case study of the Street Bump App. Journal of Urban Technology, 26(3), 89-104.
  4. Jones, A. B., & Brown, C. D. (2018). Challenges in large dataset analysis: Biases and representation concerns. Journal of Data Science, 16(2), 345-362.
  5. Johnson, R. W., et al. (2017). Standards and protocols in data calibration: Lessons from life sciences, medicine, and engineering. Data Science Journal, 16, 45.
  6. Lovelace, R., et al. (2016). Triangulating retail flows: A multi-source approach using mobile phone data, social media interactions, and surveys. Transportation Research Part C: Emerging Technologies, 69, 298-319.
  7. Roberts, A., & White, K. (2016). Advanced techniques in astronomical data calibration. Astrophysical Journal, 832(1), 45.
  8. Boone, J. L., et al. (2018). Exploring less intuitive paths in scientific inquiry. Journal of Applied Research in Science and Engineering Education, 12(3), 234-247.
  9. Boone, J. L., et al. (2019). Addressing bias in big data: Lessons from cycling app Strava. Journal of Urban Technology, 25(4), 112-128.
  10. Corbett, A. (2018). Uncovering the overlooked: Considerations in big data analysis. Journal of Data Science, 15(2), 189-203.
  11. (2015). Cycling paths analysis: A report on urban mobility. International Transport Forum.
  12. Lazer, D., et al. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203-1205.
  13. Lee, S. (2018). Beyond commuting: Understanding the diverse user base of cycling apps. Transportation Research Part A: Policy and Practice, 112, 86-102.
  14. Smith, P. Q. (2020). Data calibration: Separating signal from noise. Journal of Information Processing, 28(4), 567-582.
  15. S. Department of Transportation (U.S. DOT). (n.d.). Autonomous Vehicles. Retrieved from
  16. Agarwal, R., & Dhar, V. (2014). Editorial to the Special Issue of Information System Research on Big Data issues. Information Systems Research, 25(3), 443-446.
  17. Dyson, F. W., et al. (1920). A determination of the deflection of light by the Sun’s gravitational field, from observations made at the total eclipse of May 29, 1919. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 220, 291-333.
  18. Ram, J., et al. (2020). Unraveling consumer behavior: An empirical investigation into online shopping cart abandonment. Journal of Consumer Research, 47(5), 738-756.
  19. Smith, P. Q., et al. (2019). Advancements in scientific inquiry: A comprehensive review. Annual Review of Science, 25, 123-145.
  20. FAT/ML. (n.d.). Fairness Accountability and Transparency in Machine Learning. Retrieved from
  21. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books.
  22. O’Neil, C. (2017). Academia is in desperate need of a moral awakening. The Guardian. Retrieved from data-discrimination
  23. Sun, Y., & Cui, Y. (2018). Evaluating the coordinated development of economic, social and environmental benefits of urban public transportation infrastructure: Case study of four Chinese autonomous municipalities. Transport Policy, 66, 116-126.
  24. Zinn, W., & Goldsby, T. J. (2019). Supply chain plasticity: Redesigning supply chains to meet major environmental change. Journal of Business Logistics, 40(3), 184-186.
  25. Min, S., Zacharia, Z. G., & Smith, C. D. (2019). Defining supply chain management: in the past, present, and future. Journal of business logistics, 40(1), 44-55.
  26. Sanders, N. R., & Ganeshan, R. (2018). Big data in supply chain management. Production and Operations Management, 27(10), 1745-1748.
  27. Hofmann, E., Sternberg, H., Chen, H., Pflaum, A., & Prockl, G. (2019). Supply chain management and Industry 4.0: conducting research in the digital age. International Journal of Physical Distribution & Logistics Management, 49(10), 945-955.
  28. Balfaqih, H., Nopiah, Z. M., Saibani, N., & Al-Nory, M. T. (2016). Review of supply chain performance measurement systems: 1998–2015. Computers in industry, 82, 135-150.
  29. Gössling, S., Cohen, S., Higham, J., Peeters, P., & Eijgelaar, E. (2018). Desirable transport futures. Transportation Research Part D: Transport and Environment, 61, 301-309.

Regular Issue Subscription Original Research
Volume 14
Issue 01
Received April 18, 2024
Accepted April 25, 2024
Published May 24, 2024