900+ Titles Secure Razorpay Checkout Instant eBook Delivery
[email protected] Main Site
Proceedings / International Conference on Intelligent Engineering & Future Technologies (ICIEFT-2026) / Paper

Impact of Business Analytics on Supply Chain Performance and Operational Efficiency

Harshit Sahu
Student, B.Com(Computer Application) SICA College, DAVV, Indore
pp. 1-11

Abstract

This study examines the impact of business analytics on supply chain performance and operational efficiency across a period of accelerated digital transformation between 2018 and 2025. As global supply chains contended with the disruptions of the COVID-19 pandemic, geopolitical volatility, and rising customer expectations, business analytics emerged as a strategic capability for enabling visibility, resilience, and data-driven decision-making. Drawing exclusively on secondary data compiled from peer-reviewed literature, industry surveys, consulting reports, and publicly available organizational benchmarks, this paper evaluates how descriptive, predictive, and prescriptive analytics influence core supply chain metrics including forecast accuracy, inventory turnover, order fulfilment, logistics cost, and overall operational efficiency. The methodology synthesizes quantitative indicators across multiple industry sources and presents the findings through five comparative tables that track adoption trends, performance differentials, and return on investment. The results indicate a consistent and strengthening positive relationship between analytics maturity and supply chain performance: organizations classified as analytics leaders reported forecast accuracy improvements of 18 to 30 percent, inventory reductions of 15 to 25 percent, and logistics cost savings of 10 to 20 percent relative to analytics laggards. Predictive and prescriptive analytics, although adopted more slowly than descriptive tools, demonstrated the highest marginal impact on operational efficiency. The analysis further reveals that the value of analytics is moderated by data quality, talent availability, organizational culture, and integration with existing enterprise systems. The study concludes that business analytics has shifted from a supporting reporting function to a central driver of competitive supply chain advantage, and it offers implications for managers seeking to prioritize analytics investments and for researchers pursuing future primary studies.

Keywords

business analytics, supply chain performance, operational efficiency, predictive analytics, inventory management, digital transformation

Cite This Paper

Harshit Sahu, "Impact of Business Analytics on Supply Chain Performance and Operational Efficiency," in International Conference on Intelligent Engineering & Future Technologies (ICIEFT-2026), pp. 1-11.

References

  1. Brinch, M., Stentoft, J., Jensen, J. K., & Rajkumar, C. (2021). Practitioners understanding of big data and its applications in supply chain management. The International Journal of Logistics Management, 29(2), 555–574.
  2. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883.
  3. Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73–80.
  4. Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2019). Can big data and predictive analytics improve social and environmental sustainability? Technological Forecasting and Social Change, 144, 534–545.
  5. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.
  6. Gupta, S., Modgil, S., & Gunasekaran, A. (2020). Big data in lean six sigma: A review and further research directions. International Journal of Production Research, 58(3), 947–969.
  7. Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2018). Back in business: Operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research, 270(1), 201–211.
  8. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788.
  9. Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345.
  10. Kamble, S. S., & Gunasekaran, A. (2020). Big data-driven supply chain performance measurement system. International Journal of Production Research, 58(1), 65–86.
  11. Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: The mediating role of dynamic capabilities. British Journal of Management, 30(2), 272–298.
  12. Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance. Information & Management, 57(2), 103169.
  13. Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254–264.
  14. Queiroz, M. M., & Telles, R. (2018). Big data analytics in supply chain and logistics: An empirical approach. The International Journal of Logistics Management, 29(2), 767–783.
  15. Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., & Mehta, A. (2018). Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 21(6), 579–596.
  16. Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48.
  17. Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639.
  18. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.
  19. Waller, M. A., & Fawcett, S. E. (2015). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 36(1), 77–84.
  20. Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors. Computers & Industrial Engineering, 101, 572–591.
International Conference on Intelligent Engineering & Future Technologies (ICIEFT-2026)
· Xilvora Ink
View full volume →
View Volume