Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

AI-Enhanced Decision Support Systems for Project Management: Integrating Big Data for Real-Time Insights

Emily Thompson
Associate Professor of Information Systems, University of California, Berkeley, USA
Cover

Published 17-10-2024

Keywords

  • AI,
  • Decision Support Systems,
  • Project Management,
  • Big Data,
  • Real-Time Insights,
  • Informed Decision-Making
  • ...More
    Less

How to Cite

[1]
Emily Thompson, “AI-Enhanced Decision Support Systems for Project Management: Integrating Big Data for Real-Time Insights”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 65–72, Oct. 2024, Accessed: Nov. 24, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/171

Abstract

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed decision-making processes across various industries. In project management, AI-driven decision support systems (DSS) have emerged as essential tools that leverage big data to provide real-time insights, thereby enhancing the decision-making capabilities of project managers. This paper examines the role of AI-enhanced DSS in project management, focusing on their ability to analyze large datasets, identify patterns, and facilitate informed decision-making. By integrating big data analytics with AI algorithms, these systems enable project managers to make timely and effective decisions, ultimately leading to improved project outcomes. The study discusses the key components of AI-enhanced DSS, their applications in project management, and the challenges organizations face when implementing these technologies. Furthermore, real-world case studies highlight successful applications of AI-driven DSS in project environments, illustrating their impact on project efficiency, risk management, and stakeholder engagement. The findings underscore the importance of embracing AI and big data technologies to remain competitive in today's dynamic project landscape.

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References

  1. Gayam, Swaroop Reddy. "Deep Learning for Image Recognition: Advanced Algorithms and Applications in Medical Imaging, Autonomous Vehicles, and Security Systems." Hong Kong Journal of AI and Medicine 4.1 (2024): 223-258.
  2. Thuraka, Bharadwaj, et al. "Leveraging artificial intelligence and strategic management for success in inter/national projects in US and beyond." Journal of Engineering Research and Reports 26.8 (2024): 49-59.
  3. Ahmad, Tanzeem, et al. "Sustainable Project Management: Integrating Environmental Considerations into IT Projects." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 191-217.
  4. Nimmagadda, Venkata Siva Prakash. "AI in Pharmaceutical Manufacturing: Optimizing Production Processes and Ensuring Quality Control." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 338-379.
  5. Putha, Sudharshan. "AI-Driven Predictive Analytics for Vehicle Health Monitoring and Diagnostics in Connected Cars." Hong Kong Journal of AI and Medicine 4.1 (2024): 297-339.
  6. Sahu, Mohit Kumar. "AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management." Journal of Science & Technology 1.1 (2020): 424-464.
  7. Kasaraneni, Ramana Kumar. "AI-Enhanced Virtual Screening for Drug Repurposing: Accelerating the Identification of New Uses for Existing Drugs." Hong Kong Journal of AI and Medicine 1.2 (2021): 129-161.
  8. Pattyam, Sandeep Pushyamitra. "Data Engineering for Business Intelligence: Techniques for ETL, Data Integration, and Real-Time Reporting." Hong Kong Journal of AI and Medicine 1.2 (2021): 1-54.
  9. Pal, Dheeraj Kumar Dukhiram, et al. "AI-Assisted Project Management: Enhancing Decision-Making and Forecasting." Journal of Artificial Intelligence Research 3.2 (2023): 146-171.
  10. Han, J., & Kamber, M. (2017). Data mining: Concepts and techniques. Morgan Kaufmann Publishers.
  11. Hwang, B. G., & Ng, W. J. (2021). Project scheduling in construction: A literature review. Construction Management and Economics, 39(6), 479-498.
  12. Kim, H., & Lee, S. (2019). A big data analytics framework for project risk management. International Journal of Project Management, 37(8), 1013-1024.
  13. Kuo, R. J. (2020). Big data analytics and decision making: A literature review. Decision Support Systems, 140, 113-129.
  14. Lehtonen, P., & Pellicer, E. (2018). Project management and big data: The new frontier. Project Management Journal, 49(3), 1-4.
  15. Liu, C., & Huang, J. (2021). IoT-based big data analytics for project management: A framework. International Journal of Project Management, 39(7), 762-775.
  16. Maras, M.-H. (2019). Data-driven decision-making in project management. International Journal of Project Management, 37(3), 309-320.
  17. Mehmood, A., & Hayat, A. (2020). Data mining techniques in project management: A review. International Journal of Project Management, 38(4), 1-15.
  18. Mesquita, A. R., & Tavares, S. M. (2021). Project management decision-making in the era of big data. Project Management Journal, 52(6), 588-603.
  19. Neves, C. M., & Silva, J. P. (2019). Big data and project management: A systematic literature review. International Journal of Project Management, 37(3), 379-392.
  20. Pandey, P., & Tiwari, R. (2018). Challenges of implementing big data analytics in project management. International Journal of Project Management, 36(3), 1-11.
  21. Shah, A. S., & Desai, K. (2021). Cultural challenges in adopting AI in project management. International Journal of Project Management, 39(5), 498-511.
  22. Singh, R., & Kumar, R. (2020). Investment decisions in AI technologies for project management. Project Management Journal, 51(1), 25-34.
  23. Smiley, R. (2019). Ethical implications of AI in decision-making: A framework for project management. International Journal of Project Management, 37(7), 902-911.
  24. Smith, A., & Watson, J. (2021). The impact of AI on project management outcomes: A case study analysis. Journal of Business Research, 124, 473-485.
  25. Thomas, A., & Hu, Y. (2020). AI in software development: A case study on project estimation accuracy. Journal of Software: Evolution and Process, 32(4), e2275.
  26. Wang, L., & Zhao, X. (2021). AI-driven decision support in clinical trial management: A case study. Health Informatics Journal, 27(3), 146-158.