Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration

Year : 2025 | Volume : 03 | Issue : 02 | Page : 8 17
    By

    Prem Narayan Ahirwar,

  1. Professor, School of Mechanical Engineering, Madhyanchal Professional University, Bhopal, Madhya Pradesh, India

Abstract

Demand forecasting has under gone major changes because to the incorporation of automated analytics into supply chain management (SCM), which has improved company productivity, accuracy, and responsiveness. Central to this transformation is the application of machine learning (ML), which enables the analysis of large and complex datasets to identify patterns, detect trends, and generate precise forecasts. Conventional methods for predicting frequently rely on linear models and historical sales data, which can be difficult to change to dynamic and quickly shifting market conditions. Inefficiencies including overstocking, stockouts, and interrupted supply chain procedures are often caused by this restriction. AI-driven prediction models, on the other hand, provide far more precise and flexible projections by combining historical data with current market signals and external factors like consumer behavior, changes in the season, and economic indicators. In the framework of contemporary supply chain systems, this research investigates the breakthrough significance of machine learning in demand anticipating. It specifically looks to how machine learning (ML) improves inventory optimization, lowers uncertainty, and enhances data-driven strategic decision-making. The paper also looks at critical implementation issues such data quality, system integration difficulties, and ethical concerns like algorithmic openness and data sovereignty. Despite the obvious positives, ML adoption necessitates thorough preparation, a strong computer system, and a trained employees. By reviewing current academic research, real-world industrial applications, and case studies, this paper highlights best practices for leveraging ML in demand forecasting. Ultimately, the paper underscores how predictive analytics can foster resilience and sustainability within supply chains, offering a competitive advantage in an increasingly complex and volatile global market.

Keywords: Machine learning (ML), predictive analytics, demand forecasting, supply chain management (SCM), inventory management

[This article belongs to International Journal of Industrial and Product Design Engineering ]

How to cite this article:
Prem Narayan Ahirwar. Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration. International Journal of Industrial and Product Design Engineering. 2025; 03(02):8-17.
How to cite this URL:
Prem Narayan Ahirwar. Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration. International Journal of Industrial and Product Design Engineering. 2025; 03(02):8-17. Available from: https://journals.stmjournals.com/ijipde/article=2025/view=235247


References

  1. Aamer, A., Eka Yani, L., & Alan Priyatna, I. (2020). Data analytics in supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1–13. https://doi.org/10.31387/oscm0440281
  2. Abolghasemi, M., Hurley, J., Eshragh, A., & Fahimnia, B. (2020). Demand forecasting in the presence of systematic events: Cases in capturing sales promotions. International Journal of Production Economics, 230, 107892. https://doi.org/10.1016/j.ijpe.2020.107892
  3. Agrawal, R., Wankhede, V. A., Kumar, A., & Luthra, S. (2023). A systematic and network-based analysis of data-driven quality management in supply chains and proposed future research directions. The TQM Journal. Advance online publication. https://doi.org/10.1108/tqm-12-2020-0285
  4. Akbari, M., & Do, T. N. A. (2021). A systematic review of machine learning in logistics and supply chain management: Current trends and future directions. Benchmarking: An International Journal, 28(10), 2977–3005. https://doi.org/10.1108/bij-10-2020-0514
  5. Bag, S., Telukdarie, A., Pretorius, J. C., & Gupta, S. (2021). Industry 4.0 and supply chain sustainability: Framework and future research directions. Benchmarking: An International Journal, 28(5), 1410–1450. https://doi.org/10.1108/bij-03-2018-0056
  6. Bai, R., Chen, X., Chen, Z. L., Cui, T., Gong, S., He, W., Jiang, X., Jin, H., Jin, J., & Zhang, H. (2023). Analytics and machine learning in vehicle routing research. International Journal of Production Research, 61(1), 4–30. https://doi.org/10.1080/00207543.2021.2013566
  7. Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993–1004. https://doi.org/10.1016/j.future.2019.07.059
  8. Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2021). Building supply-chain resilience: An artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 1–21. https://doi.org/10.1080/00207543.2021.1950935
  9. Bodendorf, F., Merkl, P., & Franke, J. (2021). Intelligent cost estimation by machine learning in supply management: A structured literature review. Computers & Industrial Engineering, 160, 107601. https://doi.org/10.1016/j.cie.2021.107601
  10. Boute, R. N., Gijsbrechts, J., van Jaarsveld, W., & Vanvuchelen, N. (2021). Deep reinforcement learning for inventory control: A roadmap. European Journal of Operational Research. Advance online publication. https://doi.org/10.1016/j.ejor.2021.07.016
  11. Burggräf, P., Steinberg, F., Sauer, C. R., & Nettesheim, P. (2024). Machine learning implementation in small and medium-sized enterprises: Insights and recommendations from a quantitative study. Production Engineering, 1–14. https://doi.org/10.1007/s11740-024-01274-2
  12. Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
  13. Chowdhury, R. H. (2024). The evolution of business operations: Unleashing the potential of artificial intelligence, machine learning, and blockchain. World Journal of Advanced Research and Reviews, 22(3), 2135–2147. https://doi.org/10.30574/wjarr.2024.22.3.1992
  14. Chen, T., Sampath, V., May, M. C., Shan, S., Jorg, O. J., Aguilar Martín, J. J., Stamer, F., Fantoni, G., Tosello, G., & Calaon, M. (2023). Machine learning in manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’. Applied Sciences, 13(3), 1903. https://doi.org/10.3390/app13031903
  15. Farahani, M. A., McCormick, M. R., Gianinny, R., Hudacheck, F., Harik, R., Liu, Z., & Wuest, T. (2023). Time-series pattern recognition in smart manufacturing systems: A literature review and ontology. Journal of Manufacturing Systems, 69, 208–241. https://doi.org/10.1016/j.jmsy.2023.05.025
  16. Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119–142. https://doi.org/10.1080/13675567.2020.1803246
  17. Fu, W., & Chien, C. F. (2019). UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution. Computers & Industrial Engineering, 135, 940–949. https://doi.org/10.1016/j.cie.2019.07.002
  18. Ganjare, S. A., Satao, S. M., & Narwane, V. (2024). Systematic literature review of machine learning for manufacturing supply chain. The TQM Journal, 36(8), 2236–2259. https://doi.org/10.1108/tqm-12-2022-0365
  19. Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using distributed random forest and gradient boosting machine learning techniques. Journal of Big Data, 7(1), 1–22. https://doi.org/10.1186/s40537-020-00345-2
  20. Islam, M. K., Ahmed, H., Al Bashar, M., & Taher, M. A. (2024). Role of artificial intelligence and machine learning in optimizing inventory management across global industrial manufacturing & supply chain: A multi-country review. International Journal of Management Information Systems and Data Science, 1(2), 1–14. https://doi.org/10.62304/ijmisds.v1i2.105
  21. Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021). Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions. Applied Sciences, 11(12), 5725. https://doi.org/10.3390/app11125725
  22. Kumar, V., Bak, O., Guo, R., Shaw, S. L., Colicchia, C., Garza-Reyes, J. A., & Kumari, A. (2018). An empirical analysis of supply and manufacturing risk and business performance: A Chinese manufacturing supply chain perspective. Supply Chain Management: An International Journal, 23(6), 461–479. https://doi.org/10.1108/scm-10-2017-0319
  23. Liu, Y., Xu, Y., & Zhou, S. (2024). Enhancing user experience through machine learning-based personalized recommendation systems: Behavior data-driven UI design. Applied and Computational Engineering, 112, 42–46. https://doi.org/10.54254/2755-2721/2024.17905
  24. Ma, Q., Li, H., & Thorstenson, A. (2021). A big data-driven root cause analysis system: Application of machine learning in quality problem solving. Computers & Industrial Engineering, 160, 107580. https://doi.org/10.1016/j.cie.2021.107580
  25. Makkar, S., Devi, G. N. R., & Solanki, V. K. (2020). Applications of machine learning techniques in supply chain optimization. In ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management: Applications to Electrical, Electronics and Computer Science and Engineering (pp. 861–869). Springer Singapore.
Regular Issue Subscription Original Research
Volume 03
Issue 02
Received 12/09/2025
Accepted 13/12/2025
Published 26/12/2025
Publication Time 105 Days
128

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