Purpose: This research examines the intricate interplay between Business Intelligence (BI), Big Data Analytics (BDA), and Artificial Intelligence (AI) within the realm of Supply Chain Management (SCM). While the integration of these technologies has promised improved operational efficiency and decision-making capabilities, concerns about complexities and potential overreliance on technology persist. The study aims to provide insights into achieving a balance between data-driven insights and qualitative factors in SCM for sustained competitiveness. Design/methodology/approach: The research executed interviews with ten Arab Gulf-based consulting firms. These companies’ ability to successfully complete BI projects is well recognised. Findings: Through examining the interplay of human judgement and data-driven strategies, addressing integration challenges, and understanding the risks of excessive data reliance, the research enhances comprehension of the modern SCM landscape. It underscores BI’s foundational role, the necessity of balanced human input, and the significance of customer-centric strategies for lasting competitive advantage and relationships. Practical implications: The research provided information for organizations seeking to effectively navigate the complexities of integrating data-driven technologies in SCM. The research is a foundation for future studies to delve deeper into quantitative measurement methodologies and effective data security strategies in the SCM context. Originality: The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.

Abo-Khalil, A. G. (2023). Digital twin real-time hybrid simulation platform for power system stability. Case Studies in Thermal Engineering, 49, 103237. https://doi.org/10.1016/j.csite.2023.103237

Ahmad, A. Y. A. B., Atta, A. A. M. B., Alawawdeh, H. A., et al. (2023). The Effect of System Quality and User Quality of Information Technology on Internal Audit Effectiveness in Jordan, And the Moderating Effect of Management Support. Applied Mathematics, 17(5), 859-866.

Ain, N., Vaia, G., DeLone, W. H., et al. (2019). Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review. Decision Support Systems, 125, 113113. https://doi.org/10.1016/j.dss.2019.113113

Aldulaimi, S. H., Keir, M. Y. A., & Abdeldayem, M. M. (2022). Implementing Green Human Resources Management to Promote Sustainability Development: Application from Telecommunication Companies in Kingdom of Bahrain. Journal of Statistics Applications & Probability, 11(1), 321–330. https://doi.org/10.18576/jsap/110125

Ali, H., & Morshed, A. (2024). Augmented reality integration in Jordanian fast-food apps: Enhancing brand identity and customer interaction amidst digital transformation. Journal of Infrastructure, Policy and Development, 8(5), 3856.

Al-Okaily, M., Alsmadi, A. A., Alrawashdeh, N., et al. (2023). The role of digital accounting transformation in the banking industry sector: an integrated model. Journal of Financial Reporting and Accounting. https://doi.org/10.1108/jfra-04-2023-0214

Althabatah, A., Yaqot, M., Menezes, B., et al. (2023). Transformative Procurement Trends: Integrating Industry 4.0 Technologies for Enhanced Procurement Processes. Logistics, 7(3), 63. https://doi.org/10.3390/logistics7030063

Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416–436. https://doi.org/10.1016/j.tre.2017.04.001

Babu, M. M., Rahman, M., Alam, A., & Dey, B. L. (2021). Exploring big data-driven innovation in the manufacturing sector: Evidence from UK firms. Annals of Operations Research.

Bergmann, M., Brück, C., Knauer, T., et al. (2020). Digitization of the budgeting process: determinants of the use of business analytics and its effect on satisfaction with the budgeting process. Journal of Management Control, 31(1–2), 25–54. https://doi.org/10.1007/s00187-019-00291-y

Bosio, E., Hayman, G., & Dubosse, N. (2023). The Investment Case for E-Government Procurement: A Cost–Benefit Analysis. Journal of Benefit-Cost Analysis, 14(S1), 81–107. https://doi.org/10.1017/bca.2023.10

Cambra-Fierro, J., Gao, L. (Xuehui), Melero-Polo, I., & Patrício, L. (2022). Theories, constructs, and methodologies to study COVID-19 in the service industries. The Service Industries Journal, 42(7–8), 551–582. https://doi.org/10.1080/02642069.2022.2060209

Constantiou, I., Shollo, A., & Vendelø, M. T. (2019). Mobilizing intuitive judgement during organizational decision making: When business intelligence is not the only thing that matters. Decision Support Systems, 121, 51–61. https://doi.org/10.1016/j.dss.2019.04.004.

Dobbelaere, M. R., Plehiers, P. P., Van de Vijver, R., et al. (2021). Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats. Engineering, 7(9), 1201–1211. https://doi.org/10.1016/j.eng.2021.03.019

El ouarzadi, A., & Karim, C. (2023). Business intelligence & Organizational performance: The role of decision support. Research Bulletin, 3(03), 347–355.

Feng, X., & Goli, A. (2023). Enhancing Business Performance through Circular Economy: A Comprehensive Mathematical Model and Statistical Analysis. Sustainability, 15(16), 12631. https://doi.org/10.3390/su151612631

Friday, D., Ryan, S., Melnyk, S. A., & Proulx, D. (2023). Supply Chain Deep Uncertainties and Risks: The ‘New Normal’. In: Flexible Systems Management. Springer Nature Singapore. https://doi.org/10.1007/978-981-99-2629-9

Gupta, A., Srivastava, A., Anand, R., et al. (2020). Business Application Analytics and the Internet of Things: The Connecting Link. New Age Analytics, 249–273. https://doi.org/10.1201/9781003007210-10

Hald, K. S., & Coslugeanu, P. (2022). The preliminary supply chain lessons of the COVID-19 disruption—What is the role of digital technologies? Operations Management Research, 15(1–2), 282–297. https://doi.org/10.1007/s12063-021-00207-x

Hedlund, M. (2023). Ethicisation and Reliance on Ethics Expertise. Res Publica. https://doi.org/10.1007/s11158-023-09592-5

Horani, O. M., Khatibi, A., AL-Soud, A. R., et al. (2023). Determining the Factors Influencing Business Analytics Adoption at Organizational Level: A Systematic Literature Review. Big Data and Cognitive Computing, 7(3), 125. https://doi.org/10.3390/bdcc7030125

Hou, L., Su, J., & Ye, Y. (2023). Exploring the Influence of Smart Product Service Systems on Enterprise Competitive Advantage from the Perspective of Value Creation. Sustainability, 15(18), 13828. https://doi.org/10.3390/su151813828

Huang, Z., Savita, K. S., & Zhong-jie, J. (2022). The Business Intelligence impact on the financial performance of start-ups. Information Processing & Management, 59(1), 102761. https://doi.org/10.1016/j.ipm.2021.102761

Hurbean, L., Wong, L. H. M., Ou, C. X., et al. (2023). Instant messaging, interruptions, stress and work performance. Information Technology & People. https://doi.org/10.1108/itp-09-2022-0656

Jafari, T., Zarei, A., Azar, A., et al. (2023). The impact of business intelligence on supply chain performance with emphasis on integration and agility–a mixed research approach. International Journal of Productivity and Performance Management, 72(5), 1445–1478. https://doi.org/10.1108/ijppm-09-2021-0511

Javaid, M., Haleem, A., Pratap Singh, R., et al. (2022). Sustainability 4.0 and its applications in the field of manufacturing. Internet of Things and Cyber-Physical Systems, 2, 82–90. https://doi.org/10.1016/j.iotcps.2022.06.001

Jayender, P., & Kundu, G. K. (2021). Intelligent ERP for SCM agility and graph theory technique for adaptation in automotive industry in India. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-021-01361-y

Jin, D.-H., & Kim, H.-J. (2018). Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics. Sustainability, 10(10), 3778. https://doi.org/10.3390/su10103778.

Jreissat, E. R., Khrais, L. T., Salhab, H., et al. (2024). An In-Depth Analysis of Consumer Preferences, Behavior Shifts, and Barriers Impacting IoT Adoption: Insights from Jordan’s Telecom Industry’. Applied Mathematics and Information Sciences, 18(2), 271-281.

Kesici, M., Mohammadpourfard, M., Aygul, K., et al. (2023). Deep learning-based framework for real-time transient stability prediction under stealthy data integrity attacks. Electric Power Systems Research, 221, 109424. https://doi.org/10.1016/j.epsr.2023.109424

Ketchen, D. J., & Craighead, C. W. (2022). Cognitive biases as impediments to enhancing supply chain entrepreneurial embeddedness. Journal of Business Logistics, 45(1). Portico. https://doi.org/10.1111/jbl.12307

Kristoffersen, E., Mikalef, P., Blomsma, F., et al. (2021). Towards a business analytics capability for the circular economy. Technological Forecasting and Social Change, 171, 120957. https://doi.org/10.1016/j.techfore.2021.120957

Li, J., Chen, C.-W., Wu, C.-H., et al. (2020). How do Partners Benefit from IT Use in Supply-Chain Management: An Empirical Study of Taiwan’s Bicycle Industry. Sustainability, 12(7), 2883. https://doi.org/10.3390/su12072883

Li, J., Maiti, A., Springer, M., et al. (2020). Blockchain for supply chain quality management: challenges and opportunities in context of open manufacturing and industrial internet of things. International Journal of Computer Integrated Manufacturing, 33(12), 1321–1355. https://doi.org/10.1080/0951192x.2020.1815853

Longo, F., Padovano, A., & Umbrello, S. (2020). Value-Oriented and Ethical Technology Engineering in Industry 5.0: A Human-Centric Perspective for the Design of the Factory of the Future. Applied Sciences, 10(12), 4182. https://doi.org/10.3390/app10124182

Mannuru, N. R., Shahriar, S., Teel, Z. A., et al. (2023). Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development. Information Development. https://doi.org/10.1177/02666669231200628

Morshed, A. (2020). Role of working capital management in profitability considering the connection between accounting and finance. Asian Journal of Accounting Research, 5(2), 257–267. https://doi.org/10.1108/ajar-04-2020-0023

Morshed, A. (2024). Mathematical Analysis of Working Capital Management in MENA SMEs: Panel Data Insights. Applied Mathematics & Information Sciences, 18, 111–124.

Morshed, A. (2024). Comparative analysis of accounting standards in the Islamic banking industry: a focus on financial leasing. Journal of Islamic Accounting and Business Research.

Morshed, A. (2024). Strategic working capital management in Polish SMES: Navigating risk and reward for enhanced financial performance. Investment Management and Financial Innovations, 21(2), 253-264. doi:10.21511/imfi.21(2).2024.20

Morshed, A., & Ramadan, A. (2023). Qualitative Analysis of IAS 2 Capability for Handling the Financial Information Generated by Cost Techniques. International Journal of Financial Studies, 11(2), 67. https://doi.org/10.3390/ijfs11020067.

Morshed, A., Maali, B., Ramadan, A., et al. (2024). The impact of supply chain finance on financial sustainability in Jordanian SMEs. Uncertain Supply Chain Management. https://doi.org/ 10.5267/j.uscm.2024.4.025

Nabil, D. H., Rahman, Md. H., Chowdhury, A. H., et al. (2023). Managing supply chain performance using a real time Microsoft Power BI dashboard by action design research (ADR) method. Cogent Engineering, 10(2). https://doi.org/10.1080/23311916.2023.2257924

Narwane, V. S., Raut, R. D., Yadav, V. S., et al. (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. https://doi.org/10.1108/jeim-11-2020-0463

Neethirajan, S. (2023). Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. Sensors, 23(16), 7045. https://doi.org/10.3390/s23167045

Patrucco, A. S., Marzi, G., & Trabucchi, D. (2023). The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions. Technovation, 126, 102814. https://doi.org/10.1016/j.technovation.2023.102814

Puica, E. (2023). Improving Supply Chain Management by Integrating RFID with IoT Shared Database: Proposing a System Architecture. In: IFIP International Conference on Artificial Intelligence Applications and Innovations.

Ramadan, A., Alkhodary, D., Alnawaiseh, M., et al. (2024). Managerial Competence and Inventory Management in SME Financial Performance: A Hungarian Perspective.

Ramadan, A., & Morshed, A. (2024). Optimizing retail prosperity: Strategic working capital management and its impact on the global economy. Journal of Infrastructure, Policy and Development, 8(5), 3827.

Ranjan, J., & Foropon, C. (2021). Big Data Analytics in Building the Competitive Intelligence of Organizations. International Journal of Information Management, 56, 102231. https://doi.org/10.1016/j.ijinfomgt.2020.102231

Rejeb, A., Rejeb, K., Zailani, S., et al. (2021). Integrating the Internet of Things in the halal food supply chain: A systematic literature review and research agenda. Internet of Things, 13, 100361. https://doi.org/10.1016/j.iot.2021.100361

Roy, D., Srivastava, R., Jat, M., & Karaca, M. S. (2022). A complete overview of analytics techniques: Descriptive, predictive, and prescriptive. In: Decision Intelligence Analytics and the Implementation of Strategic Business Management.

Sankaran, G., Knahl, M., Siestrup, G., & Vasileiou, I. (2019). Value of Smart Data for Supply Chain Decisions in a Data Rich, Uncertain World. CERC, 2019, 49–54.

Shah, H. M., Gardas, B. B., Narwane, V. S., et al. (2023a). The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review. Kybernetes, 52(5), 1643–1697. https://doi.org/10.1108/k-05-2021-0423

Shah, H. M., Gardas, B. B., Narwane, V. S., et al. (2021b). The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review. Kybernetes, 52(5), 1643–1697. https://doi.org/10.1108/k-05-2021-0423

Shah, N. U., Naeem, S. B., & Bhatti, R. (2023). Digital data sets management in university libraries: challenges and opportunities. Global Knowledge, Memory and Communication. https://doi.org/10.1108/gkmc-06-2022-0150.

Shao, C., Yang, Y., Juneja, S., et al. (2022). IoT data visualization for business intelligence in corporate finance. Information Processing & Management, 59(1), 102736. https://doi.org/10.1016/j.ipm.2021.102736

Sharabati, A. A. A., Ghaith, A. A., Morshed, A., et al. (2024). Balanced Scorecard and Competitive Strategies of Small and Medium Manufacturing Organizations. WSEAS Transactions on Business and Economics, 21, 79-94. https://doi.org/10.37394/23207.2024.21.8

Shiyyab, F. S., & Morshed, A. Q. (2024). The Impact of Credit Risk Mitigation on the Profits of Investment Deposits in Islamic Banks. In Islamic Finance: New Trends in Law and Regulation (pp. 117-129). Cham: Springer Nature Switzerland.

Tan, W. C., & Sidhu, M. S. (2022). Review of RFID and IoT integration in supply chain management. Operations Research Perspectives, 9, 100229. https://doi.org/10.1016/j.orp.2022.100229

Thekkoote, R. (2022). Understanding big data-driven supply chain and performance measures for customer satisfaction. Benchmarking: An International Journal, 29(8), 2359–2377. https://doi.org/10.1108/bij-01-2021-0034

Toubeau, J.-F., Bottieau, J., De Greeve, Z., et al. (2020). Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets with Probabilistic Guarantees on Real-Time Delivery. IEEE Transactions on Power Systems, 36(4), 2815–2828. https://doi.org/10.1109/tpwrs.2020.3046710

Ullah, K., Ullah, Z., Aslam, S., et al. (2023). Wind Farms and Flexible Loads Contribution in Automatic Generation Control: An Extensive Review and Simulation. Energies, 16(14), 5498. https://doi.org/10.3390/en16145498

Waterworth, D., Sethuvenkatraman, S., & Sheng, Q. Z. (2023). Deploying data driven applications in smart buildings: Overcoming the initial onboarding barrier using machine learning. Energy and Buildings, 279, 112699. https://doi.org/10.1016/j.enbuild.2022.112699

Wu, Q., Yan, D., & Umair, M. (2023). Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Economic Analysis and Policy, 77, 1103–1114. https://doi.org/10.1016/j.eap.2022.11.024