Harnessing Artificial Intelligence and Machine Learning for Cognitive Enterprise Resource Planning

Authors

  • N. Anandharaj Author

Keywords:

Enterprise Resource Planning (ERP), Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Real-time Insights, Operational Efficiency, Decision-making, Agility

Abstract

Enterprise Resource Planning (ERP) systems are undergoing a transformative shift with the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies, enabling organizations to create cognitive ERP solutions that drive business excellence through intelligent automation, predictive analytics, and real-time insights. This paper explores the convergence of AI, ML, and ERP, highlighting the benefits of cognitive ERP systems in enhancing operational efficiency, improving decision-making, and fostering innovation in the digital era. By harnessing the power of AI and ML, organizations can unlock new levels of agility, responsiveness, and competitiveness, positioning themselves for success in a rapidly changing business landscape.

References

Jo H, Park DH. Mechanisms for successful management of enterprise resource planning from user information processing and system quality perspective. Sci Rep. 2023 Aug 4;13(1):12678.

Sam Goundar., et al. How Artificial Intelligence is Transforming the ERP Systems. July 2021. In book: Enterprise Systems and Technological Convergence: Research and Practice. Publisher: Information Age Publishers.

Sure, T. A. R. (2024). Human-Computer Interaction Techniques for Explainable Artificial Intelligence Systems, Recent Trends in Artificial Intelligence & It’s Applications, 3(1), 1-7.

Ghani U, Signal N, Niazi IK and Taylor D (2021) Efficacy of a Single-Task ERP Measure to Evaluate Cognitive Workload During a Novel Exergame. Front. Hum. Neurosci. 15:742384.

Zerbino P, Aloini D, Dulmin R, Mininno V. (2021) Why enterprise resource planning initiatives do succeed in the long run: A case-based causal network. PLoS ONE 16(12): e0260798.

Sure, T. A. R. (2023). The Internet of Things: Securing Smart Technologies for the Mobile Age, Journal of IOT Security and Smart Technologies, 2(3), 21-25.

Judy E. Scott, Steven Walczak. Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Information & Management. Volume 46, Issue 4, May 2009, Pages 221-232.

Gomez-Pinilla F, Hillman C. The influence of exercise on cognitive abilities. Compr Physiol. 2013 Jan;3(1):403-28.

Sure, T. A. R. (2023). Motion tracking in iOS applications using augmented reality. Journal of Android and iOS Applications and Testing, 8(3), 1–5.

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

Marr, B. (2018). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.

Tharun Anand Reddy S. (2022). Ambient Computing: The Integration of Technology into Our Daily Lives. Journal of Artificial Intelligence & Cloud Computing. 1(4). 1-6.

Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(10), 78-83.

Kumar, V., & Reinartz, W. (2016). Creating Enduring Customer Value. Harvard Business Review Press.

Bresnick, J. (2020). The top 5 artificial intelligence trends for 2020. Healthcare IT News. Retrieved from https://www.healthcareitnews.com

Sure, T. A. R. (2023). An analysis of telemedicine and virtual care trends on iOS platforms. Journal of Health Education Research & Development, 11(05), 1-3.

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.

Brynjolfsson, E., & McElheran, K. (2016). The rapid rise of data-driven decision-making. MIT Sloan Management Review, 58(2), 15-23.

Jaggard, J. (2021). The impact of artificial intelligence on enterprise resource planning systems. Journal of Enterprise Information Management, 34(1), 124-136.

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Published

2024-08-10

How to Cite

N. Anandharaj. (2024). Harnessing Artificial Intelligence and Machine Learning for Cognitive Enterprise Resource Planning. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 14(2), 89-101. https://ijcserd.in/index.php/home/article/view/IJCSERD_14_02_007