Real-Time Analytics and Supply Chain Transformation in the Digital Era
DOI:
https://doi.org/10.61194/sijl.v1i3.613Keywords:
Big Data Analytics, Supply Chain Optimization, Predictive Forecasting, Supply Chain Resilience, Inventory Management, Digital Transformation, IndustryAbstract
Big Data Analytics (BDA) has emerged as a transformative tool in optimizing supply chain management by enabling real-time insights, predictive forecasting, and operational efficiency. This study presents a comprehensive narrative review to evaluate the strategic application of BDA across key supply chain domains. Literature was collected from Scopus, Google Scholar, and related databases using Boolean search strings to identify relevant peer-reviewed studies published between 2018 and 2024. The review synthesized findings across four thematic areas: demand forecasting, inventory and logistics management, supply chain resilience, and technology integration. Results indicate that BDA significantly improves forecasting accuracy, enhances inventory efficiency, supports risk mitigation, and enables agile responses to market changes. BDA-integrated systems such as ERP and IoT provide strategic visibility and faster decision-making. Case studies from various sectors, including retail, healthcare, and agribusiness, demonstrate measurable cost reductions and increased responsiveness. However, challenges such as legacy IT systems, data security concerns, and workforce capability gaps limit implementation. This study discusses the systemic implications of BDA, proposing policies and managerial strategies to overcome integration barriers. It also outlines future research directions in adaptive analytics, sustainable operations, and digital infrastructure. Ultimately, this review underscores BDA's potential to enable dynamic and resilient supply chains, aligning operational goals with long-term sustainability.
References
Babu, K., Ramasamy, R., & Noor, M. (2022). Optimizing logistics through real-time analytics: A case-based approach. Journal of Supply Chain Analytics, 10(2), 112–130.
Bouazizi, A., Rahman, M., & Aziz, F. (2024). Integrating big data for sustainable manufacturing practices. International Journal of Operations Management, 18(1), 50–67.
Cárdenas, J. A., Peña, F. J., & Morán, M. (2021). Sectoral insights on the digital transformation of supply chains in Latin America. Logistics and Strategy Journal, 7(3), 78–95.
Chen, Y., Sun, H., & Wang, J. (2021). Big data analytics and supply chain agility: A multi-sectoral study. Journal of Business Logistics, 42(4), 273–291.
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1889. DOI: https://doi.org/10.1111/poms.12838
Dhamija, P., & Bag, S. (2020). Enhancing e-commerce supply chains with predictive analytics. International Journal of Retail Technology, 15(2), 90–108.
Giuffrida, M., Mangina, E., & Sakas, D. (2022). Context-aware data analytics in global supply chains. Decision Analytics Journal, 3(1), 25–42.
Gupta, H., Singh, R., & Meena, M. L. (2023). Agile supply chain strategies in the age of big data. Journal of Contemporary SCM Studies, 9(1), 45–61.
Gupta, S., Jain, V., & Sharma, A. (2021). Big data for resilient supply chains: A post-COVID analysis. Journal of Global Supply Chain Studies, 8(4), 33–50.
Hrouga, M., & Sbihi, D. (2023). Workforce readiness and analytics capabilities in supply chain transformation. Journal of Supply Chain Workforce Development, 11(1), 20–39.
Jain, V., Kumar, S., & Gupta, S. (2022). Big data in supply chains: Frameworks for resilience and innovation. Journal of Industrial Analytics, 10(3), 155–170.
Ji, Y., Liu, Q., & Zhao, R. (2022). Integrating ERP and BDA for real-time supply chain visibility. Journal of Information Systems in Logistics, 6(2), 77–95.
Li, M., Wong, C. Y., & Yu, W. (2020). Green analytics in agribusiness supply chains. Journal of Environmental Supply Chain Innovation, 4(3), 66–84.
Liu, J., Tao, F., & Ding, X. (2020). IoT and BDA in healthcare logistics: A system approach. International Journal of Healthcare Logistics, 5(2), 101–118.
Ma, R., & Huang, T. (2024). Data-driven customer behavior analytics in e-commerce supply chains. Journal of E-Commerce and Technology, 12(1), 88–103.
Mangina, E., Giannikas, V., & Nithya, S. (2020). From descriptive to predictive analytics in digital supply chains. International Journal of Supply Chain Digitalization, 6(3), 91–109.
Mavutha, P., Daniels, K., & Singh, A. (2024). Machine learning for demand forecasting in the manufacturing sector. Journal of Manufacturing Analytics, 9(1), 27–44.
Nithya, S., Patil, S., & Singh, D. (2023). Collaborative BDA platforms for supply chain transparency. Global Supply Chain Journal, 11(4), 59–75.
Priyanshu, R., Kaur, R., & Sen, P. (2024). BDA-enabled order fulfillment in fast fashion logistics. International Journal of Retail Operations, 8(1), 113–129.
Rai, A., Patnayakuni, R., & Seth, N. (2021). ERP and big data convergence for dynamic supply chains. Journal of Digital Business Transformation, 17(2), 84–102.
Robak, S., Zaharia, M., & Ivan, L. (2016). Human-centric approaches to IT integration in operations. Journal of Systems and People, 12(1), 51–67.
Sánchez, P., Ortega, C., & Rivas, J. (2024). BDA adoption in smart agriculture logistics. Journal of AgriTech and Analytics, 7(1), 100–116.
Santos, L., & Marques, A. (2022). Reducing logistics disruptions through predictive modeling. Transportation Analytics Review, 9(3), 144–162.
Shametova, E., Liu, Y., & Zhang, T. (2023). Post-pandemic analytics strategies in global supply chains. Global Logistics Journal, 10(1), 39–55.
Sharma, M., Yadav, A., & Kumar, P. (2021). Big data for sustainable SCM: A resource-based view. Journal of Sustainable Logistics, 13(2), 58–76.
Sharma, V., Patel, K., & Ali, R. (2024). BDA for supply chain agility under uncertainty: A resilience framework. Journal of Emerging Technologies in Logistics, 8(1), 25–43.
Taluru, R., & Allabanda, R. (2019). Analytics-based emission control in logistics networks. Journal of Clean Transportation, 5(2), 55–71.
Ünal, E., Pal, R., & Shah, P. (2021). Eco-efficiency in supply chains through digital tools. International Journal of Sustainable Value Chains, 9(1), 30–47.
Villacis, D., Ortega, R., & Wang, H. (2024). AI-driven demand sensing for smart retail supply chains. Retail Data Science Journal, 6(2), 89–107.
Zhang, L., Chen, S., & Wu, D. (2021). Customer-centric inventory strategies using big data. Journal of Retail Logistics Optimization, 10(4), 74–92.
Ziaee, M., Ghaffari, M., & Talebi, B. (2023). Infrastructure readiness for big data in emerging economies. Journal of Digital Infrastructure, 7(1), 40–56.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Nisrina Salwa
This work is licensed under a Creative Commons Attribution 4.0 International License.
