Advances in Supply Chain Optimization and Intelligent Business Analysis: A Conceptual Review

References

[1] Ahmed, S., Kalsoom, T., Ramzan, N., Pervez, Z., Azmat, M., Zeb, B., & Ur Rehman, M. (2021). Towards supply chain visibility using internet of things: A dyadic analysis review. Sensors, 21(12), 4158. https://

[2] Birkel, H. S., & Hartmann, E. (2020). Internet of Things–the future of managing supply chain risks. Supply Chain Management: An International Journal, 25(5), 535–548.

[3] Chowdhury, M. M. H., Quaddus, M., & Agarwal, R. (2019). Supply chain resilience for performance: Role of relational practices and network complexities. Supply Chain Management: An International Journal, 24(5), 659–676. https:// 2018-0332

[4] IBM. (2022). What is a digital twin? IBM. https://www.ibm.com/topics/what-is-a-digital- twin

[5] Kamble, S. S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2022). Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework. Technological Forecasting and Social Change, 176, 121448.

[6] Katsaliaki, K., Galetsi, P., & Kumar, S. (2022). Supply chain disruptions and resilience: A major review and future research agenda. Annals of Operations Research, 319, 965–1002.

[7] Narwane, V., Raut, R., Yadav, V., Cheikhrouhou, N., Narkhede, B., & Priyadarshinee, P. (2021). The role of big data for supply chain 4.0 in manufacturing organisations of developing countries. Journal of Enterprise Information Management, 34(5), 1452–1480.

[8] Roy, D., Srivastava, R., Jat, M., & Karaca, M. S. (2022). A complete overview of analytics techniques: Descriptive, predictive, and prescriptive. In E. Babulak (Ed.), Decision intelligence analytics and the implementation of strategic business management. IntechOpen.

[9] Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. Journal of Big Data, 7(1), 53.

[10] Talwar, S., Kaur, P., Fosso Wamba, S., & Dhir, A. (2021). Big Data in operations and supply chain management: A systematic literature review and future research agenda. International Journal of Production Research, 59(11), 3509–3534.

[11] Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

[12] Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517.

[13] Van der Valk, H., Strobel, G., Winkelmann, S., & Möller, F. (2022). Supply chains in the era of digital twins: A review. Procedia Computer Science, 204, 156–163.

[14] Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2022). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327.

@article{1722499,
      author = {Mercy Chinenye Chukwu},
      title = {Advances in Supply Chain Optimization and Intelligent Business Analysis: A Conceptual Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {2784-2790},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722499.pdf},
      abstract = {Advanced technology has transformed the concept of supply chain management and how companies are able to leverage data to gain insight into their business. The modern supply chain presents a number of new complexities, volatility and competition and needs intelligent systems to analyse, adapt and respond to situations in real-time, while being able to resist disruption. This conceptual overview investigates progress in the areas of Supply Chain optimization and Intelligent Business analysis, and aims to bring together the emerging global research stream of the six thematic areas of Artificial Intelligence and machine learning, Big Data analytics, Internet of Things, Predictive and Prescriptive analytics, Digital Twin technologies and Resilience and Disruption intelligence. The review was carried out by systematically searching the databases like Scopus, Web of Science, Google Scholar and other databases using the keywords: Supply chain optimization, Intelligent business analysis, Machine learning in supply chains, and Digital twin logistics with the period of the publications mostly from 2020 to 2024, and the articles were selected after the peer review process. The thematic analysis shows high consistency across the themes on how AI-based analytics and data-based systems can impact supply chain performance, and significant gaps in cross-functional integration, human-machine interactions and implementation in new economy contexts. A novel Intelligent Supply Chain Optimization Framework (ISCOF) is proposed to understand the layered relationship between the theoretical foundations, technological enablers and strategic outcomes. The review not only consolidates the theory by combining a vast number of non-related publications under one conceptual framework, but also offers some ideas and practical recommendations for the managers interested in introducing intelligent systems in the supply chain.},
      keywords = {supply chain optimization; intelligent business analytics; artificial intelligence; big data analytics; internet of things (iot); digital twin technology; supply chain resilience.},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722499}
  }