The Influence of Big Data Analytics on Supply Chain Decision-Making.

References

[1] Aghaei, R., Kiaei, A. A., Boush, M., Vahidi, J., &Barzegar, Z. (2018). The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation. arXiv preprint arXiv:2501.15411. https://arxiv.org/abs/2501.15411​arXiv

[2] Bag, S., Wood, L. C., Xu, L., &Dhamija, P. (2019). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. International Journal of Production Economics, 231, 107830. https://doi.org/10.1016/j.ijpe.2020.107830

[3] Baryannis, G., Dani, S., & Antoniou, G. (2017). Prescriptive analytics: A review of methods and applications in supply chain management. International Journal of Production Research, 59(15), 4701–4727. https://doi.org/10.1080/00207543.2020.1788747

[4] Brinch, M., Stentoft, J., Jensen, J. K., &Rajkumar, C. (2019). Big data analytics and decision-making in supply chain management: A review and agenda for future research. International Journal of Operations & Production Management, 41(3), 202–242. https://doi.org/10.1108/IJOPM-02-2020-0088

[5] Chae, B., Olson, D., &Sheu, C. (2014). The impact of supply chain analytics on operational performance: A resource-based view. International Journal of Production Research, 52(16), 46954710.https://doi.org/10.1080/00207543.2013.861616

[6] Chatterjee, S., Rana, N. P., Tamilmani, K., Sharma, A., &Dwivedi, Y. K. (2018). The role of big data analytics in predicting the behavior of B2B customers: An exploratory study. Technological Forecasting and Social Change, 167, 120732.https://doi.org/10.1016/j.techfore.2021.120732

[7] Choi, T. M., Wallace, S. W., & Wang, Y. (2019). Big data analytics in operations management. Production and Operations Management, 30(3), 839–850. https://doi.org/10.1111/poms.13249​

[8] Dolgui, A., Ivanov, D., &Sokolov, B. (2019). Reconfigurable supply chain: The X-network. International Journal of Production Research, 58(13), 41384163.https://doi.org/10.1080/00207543.2019.1657363

[9] Dubey, R., Bryde, D. J., Foropon, C., &Gunasekaran, A. (2019). Explaining the impact of reconfigurability on supply chain performance using big data analytics: The moderating effect of environmental dynamism. International Journal of Production Economics, 231, 107831. https://doi.org/10.1016/j.ijpe.2020.107831

[10] Dubey, R., Gunasekaran, A., Childe, S. J., &Wamba, S. F. (2016). The impact of big data analytics on operational supply chain performance: A resource-based view. Production Planning & Control, 32(1), 1–13. https://doi.org/10.1080/09537287.2020.1712487​

[11] Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T. (2018). Agile supply chain management: The role of artificial intelligence and digital technologies. International Journal of Production Research, 58(1), 63–82. https://doi.org/10.1080/00207543.2019.1657361

[12] Jabbour, C. J. C., de Sousa Jabbour, A. B. L., Foropon, C., &GodinhoFilho, M. (2016). When titans meet—Can industry 4.0 revolutionize the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18–25. https://doi.org/10.1016/j.techfore.2018.01.017

[13] Jannelli, V., Schoepf, S., Bickel, M., Netland, T., &Brintrup, A. (2019). Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking. arXiv preprint arXiv:2411. 10184.https://arxiv.org/abs/2411.10184​arXiv

[14] Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2019). Impact of big data and predictive analytics capability on supply chain sustainability: A case study of the UK manufacturing industry. International Journal of Production Research, 59(5), 1534–1551. https://doi.org/10.1080/00207543.2020.1725051

[15] Kamble, S. S., Gunasekaran, A., &Gawankar, S. A. (2017). Big data analytics capability and decision-making performance in emerging market firms: The role of knowledge integration capability. International Journal of Production Economics, 236, 108113. https://doi.org/10.1016/j.ijpe.2021.108113

[16] Kamble, S. S., Gunasekaran, A., Subramanian, N., &Ghadge, A. (2017). A machine learning based approach for predicting demand in supply chain: A case of COVID-19 pandemic. Computers & Industrial Engineering, 161, 107658. https://doi.org/10.1016/j.cie.2021.107658

[17] Mangla, S. K., Luthra, S., Mishra, N., &Rana, N. P. (2017). Applications of prescriptive analytics in supply chain decision-making: A framework and future research directions. Technological Forecasting and Social Change, 173, 121107. https://doi.org/10.1016/j.techfore.2021.121107

[18] Maroufkhani, P., Wan Ismail, W. K., &Ghobakhloo, M. (2019). Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(2), 250–275. https://doi.org/10.1108/JSTPM-02-2020-0020

[19] Melo, M. T., Nickel, S., &Saldanha-da-Gama, F. (2019). Optimization models for logistics network design: A comprehensive review. European Journal of Operational Research, 263(2), 339–365. https://doi.org/10.1016/j.ejor.2017.04.002

[20] Mikalef, P., Krogstie, J., Pappas, I. O., & Giannakos, M. (2018). Investigating the effects of big data analytics capabilities on firm performance: A dynamic capabilities perspective. Information & Management, 59(2), 103509. https://doi.org/10.1016/j.im.2021.103509

[21] Mikalef, P., Krogstie, J., Pappas, I. O., &Pavlou, P. A. (2018). Investigating the effects of Big Data analytics capabilities on firm performance: The mediating role of dynamic capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.003

[22] Monk, E., & Wagner, B. (2017). Concepts in Enterprise Resource Planning (2nd ed.). Thomson Course Technology.

[23] Nguyen, T. H., Ngo, L. V., Ruël, H., &Kanchanapibul, M. (2018). Big data analytics capabilities and decision-making quality: A resource orchestration perspective. Journal of Business Research, 143, 522–534. https://doi.org/10.1016/j.jbusres.2021.11.063

[24] Nguyen, T. H., Nguyen, N. P., Ngo, L. V., &Ruël, H. (2018). Artificial intelligence and big data analytics capability in forecasting supply chain demand: A resource orchestration perspective. International Journal of Information Management, 66, 102500. https://doi.org/10.1016/j.ijinfomgt.2022.102500

[25] Quan, Y., & Liu, Z. (2014). InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains. arXiv preprintarXiv:2407. 11384.https://arxiv.org/abs/2407.11384

[26] Schoenherr, T., &Speier-Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132. https://doi.org/10.1111/jbl.12082

[27] Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2017). Developing a framework for enhancing survivability of sustainable supply chains during and post-COVID-19 pandemic. International Journal of Logistics Research and Applications, 24(5), 437456.https://doi.org/10.1080/13675567.2020.1810213

[28] Tiwari, S., Wee, H.-M., &Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2020: Insights to industry and academia. Computers & Industrial Engineering, 153, 107078.https://doi.org/10.1016/j.cie.2020.107078

[29] Wamba, S. F., Gunasekaran, A., Dubey, R., & Ngai, E. W. T. (2017). Big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498. https://doi.org/10.1016/j.ijpe.2019.09.019​

[30] Wamba, S. F., Kala Kamdjoug, J. R., TchatchouangWanko, C. E., & Lefebvre, L.-A. (2017). Influence of big data analytics capabilities on supply chain agility and performance: The moderating effect of environmental dynamism. Journal of Business Research, 131, 183–196. https://doi.org/10.1016/j.jbusres.2021.03.042

[31] Wang, Y., Han, J., Beynon-Davies, P., & Forest, D. (2019). Understanding the role of big data in retail supply chain management: A resource-based view. Supply Chain Management: An International Journal, 25(5), 465–477. https://doi.org/10.1108/SCM-03-2019-0094

[32] Wichmann, M., Lorentz, H., &Hekkala, R. (2019). Building dynamic capabilities for data-driven supply chain management. Supply Chain Management: An International Journal, 26(2), 231–248. https://doi.org/10.1108/SCM-01-2020-0010

@article{1709806,
      author = {Ogechi Thelma Uzozie, Osazee  Onaghinor, Odira Kingsley Okenwa},
      title = {The Influence of Big Data Analytics on Supply Chain Decision-Making.},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {2},
      pages = {754-777},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709806.pdf},
      abstract = {This study investigates the transformative impact of Big Data Analytics (BDA) and intelligent decision-making systems on supply chain management, with a focus on predictive, prescriptive, and autonomous analytics applications. The primary objective is to examine how data-driven technologies are reshaping decision-making processes and enhancing operational performance across various supply chain domains, including demand forecasting, logistics optimization, procurement, and inventory management. Using a systematic literature review methodology, the research synthesizes findings from peer-reviewed academic journals published. The review includes 38 high-quality studies selected from databases such as Scopus, Web of Science, and ScienceDirect, applying rigorous inclusion, exclusion, and thematic coding criteria. Key findings reveal that AI, machine learning, and large language models are enabling supply chains to shift from reactive to proactive and even autonomous decision-making. Real-time data utilization, predictive maintenance, last-mile optimization, and collaborative planning are among the most widely adopted analytics-driven innovations. However, barriers such as data integration challenges, skills shortages, high implementation costs, and ethical concerns persist. The study emphasizes the need for digital upskilling among professionals and the development of regulatory frameworks to support responsible and inclusive adoption. It also highlights gaps in longitudinal impact studies and calls for further interdisciplinary research into ethical AI, sustainability, and SME inclusion. In conclusion, the integration of intelligent analytics systems offers significant potential for building agile, resilient, and sustainable supply chains. However, realizing this potential requires strategic alignment, cultural transformation, and stakeholder collaboration.},
      keywords = {Big Data Analytics, Intelligent Decision-Making, Supply Chain Management, Autonomous Systems},
      month = {August},
  }