Exploring the Impact of Artificial Intelligence on Enterprise Modeling
DOI:
https://doi.org/10.35335/emod.v13i2.8Keywords:
Artificial Intelligence, Enterprise Modeling, Supply Chain, Decision-Making, OptimizationAbstract
This research investigates the impact of artificial intelligence (AI) on enterprise modeling, with a specific focus on supply chain network design. The objective is to explore how AI techniques can enhance decision-making, improve efficiency, and drive cost reduction in enterprise modeling processes. The research utilizes case examples and numerical simulations to demonstrate the benefits and implications of incorporating AI techniques in enterprise modeling. The findings reveal that AI-enabled approaches in supply chain network design lead to cost reduction, improved customer service levels, accuracy improvement, efficiency gains, enhanced decision-making, and collaboration facilitation. The research highlights the importance of data availability, ethical considerations, organizational readiness, and interoperability in realizing the full potential of AI-enabled enterprise modeling. However, the research acknowledges the limitations, such as simplified examples and the specific context of supply chain network design. Future research is needed to validate the findings in diverse industry settings and address challenges related to data availability, ethical considerations, organizational readiness, and interoperability. This research contributes to the understanding of the positive impact of AI on enterprise modeling, providing valuable insights for organizations seeking to leverage AI techniques to optimize their decision-making processes and drive operational improvements.
References
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160.
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: the simple economics of artificial intelligence. Harvard Business Press.
Al-Debei, M. M., & Avison, D. (2010). Developing a unified framework of the business model concept. European journal of information systems, 19(3), 359-376.
Asadi, H., Rostamizadeh, K., Salari, D., & Hamidi, M. (2011). Preparation of biodegradable nanoparticles of tri-block PLA–PEG–PLA copolymer and determination of factors controlling the particle size using artificial neural network. Journal of microencapsulation, 28(5), 406-416.
Bagheri, M., Bazvand, A., & Ehteshami, M. (2017). Application of artificial intelligence for the management of landfill leachate penetration into groundwater, and assessment of its environmental impacts. Journal of Cleaner Production, 149, 784-796.
Brodie, M. L., Mylopoulos, J., & Schmidt, J. W. (Eds.). (2012). On conceptual modelling: Perspectives from artificial intelligence, databases, and programming languages. Springer Science & Business Media.
Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda (pp. 23-57). University of Chicago Press.
Ceccaroni, L., Bibby, J., Roger, E., Flemons, P., Michael, K., Fagan, L., & Oliver, J. L. (2019). Opportunities and risks for citizen science in the age of artificial intelligence. Citizen Science: Theory and Practice, 4(1).
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.
Fox, M. S., & Gruninger, M. (1998). Enterprise modeling. AI magazine, 19(3), 109-109.
Glymour, C., Scheines, R., & Spirtes, P. (2014). Discovering causal structure: Artificial intelligence, philosophy of science, and statistical modeling. Academic Press.
Gregan‐Paxton, J., Hibbard, J. D., Brunel, F. F., & Azar, P. (2002). “So that's what that is”: Examining the impact of analogy on consumers' knowledge development for really new products. Psychology & Marketing, 19(6), 533-550.
Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25.
Khawas, P., Dash, K. K., Das, A. J., & Deka, S. C. (2016). Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm. Drying Technology, 34(4), 491-503.
Korbicz, J., Koscielny, J. M., Kowalczuk, Z., & Cholewa, W. (Eds.). (2012). Fault diagnosis: models, artificial intelligence, applications. Springer Science & Business Media.
Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149-157.
McFarlane, D., Sarma, S., Chirn, J. L., Wong, C., & Ashton, K. (2003). Auto ID systems and intelligent manufacturing control. Engineering Applications of Artificial Intelligence, 16(4), 365-376.
McFarlane, D., Sarma, S., Chirn, J. L., Wong, C., & Ashton, K. (2003). Auto ID systems and intelligent manufacturing control. Engineering Applications of Artificial Intelligence, 16(4), 365-376.
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220-229).
Nemati, H. R., Steiger, D. M., Iyer, L. S., & Herschel, R. T. (2002). Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33(2), 143-161.
Neto, A. H., & Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and buildings, 40(12), 2169-2176.
Park, S. H., & Han, K. (2018). Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 286(3), 800-809.
Pidd, M. (1997). Tools for thinking—Modelling in management science. Journal of the Operational Research Society, 48(11), 1150-1150.
Rahwan, I., & Simari, G. R. (Eds.). (2009). Argumentation in artificial intelligence (Vol. 47). Heidelberg: Springer.
Richardson, G. P., & Pugh III, A. L. (1997). Introduction to system dynamics modeling with DYNAMO. Journal of the Operational Research Society, 48(11), 1146-1146.
Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135-146.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56.
Wenger, E. (2014). Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann.
Zaheer, A., McEvily, B., & Perrone, V. (1998). Does trust matter? Exploring the effects of interorganizational and interpersonal trust on performance. Organization science, 9(2), 141-159.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International Journal of Educational Technology in Higher Education, 16(1), 1-27.
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762.
Zhu, X., & Wu, X. (2004). Class noise vs. attribute noise: A quantitative study. The Artificial Intelligence Review, 22(3), 177.
Zhu, Y., Xie, C., Sun, B., Wang, G. J., & Yan, X. G. (2016). Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models. Sustainability, 8(5), 433.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Denis Denunciar Otros, Vistos Otras
This work is licensed under a Creative Commons Attribution 4.0 International License.
