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Kiran Reddy
Independent Researcher
Andhra Pradesh, India
Abstract
In today’s fast-paced business environment, effective inventory management and robust data management in SAP systems are critical for maintaining competitiveness and operational efficiency. This manuscript explores the integration of artificial intelligence (AI) with cloud-based systems to enhance inventory management and data handling within SAP frameworks. By leveraging AI technologies such as machine learning, predictive analytics, and real-time data processing, organizations can optimize inventory levels, forecast demand more accurately, and improve overall data governance. This paper discusses the current challenges in inventory and data management, examines existing literature on AI applications in these areas, outlines a methodological framework for implementation, presents the results of case studies, and concludes with recommendations for future research.
Keywords
AI, inventory management, SAP data management, cloud computing, predictive analytics, machine learning
References
- Dommari, S., & Khan, S. (2023). Implementing Zero Trust Architecture in cloud-native environments: Challenges and best practices. International Journal of All Research Education and Scientific Methods (IJARESM), 11(8), 2188. Retrieved from http://www.ijaresm.com
- Cahoon, J., Wang, W., Zhu, Y., Lin, K., Liu, S., Truong, R., Singh, N., Wan, C., Ciortea, A. M., Narasimhan, S., & Krishnan, S. (2022). Doppler: Automated SKU recommendation in migrating SQL workloads to the cloud. Proceedings of the VLDB Endowment, 15(12), 3532–3544. https://doi.org/10.14778/3557828.3558294
- Haase, C., Röseler, T., & Seidel, M. (2022). METL: A modern ETL pipeline with a dynamic mapping matrix. arXiv preprint arXiv:2203.10289. https://doi.org/10.48550/arXiv.2203.10289
- Kirchoff, D. F., Xavier, M. G., Mastella, J., & de Rose, C. A. F. (2019). A preliminary study of machine learning workload prediction techniques for cloud applications. In Proceedings of the 2019 Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp. 1–6). IEEE. https://doi.org/10.1109/EMPDP.2019.8671604
- Manias, D. M., Hawilo, H., & Shami, A. (2020). A machine learning-based migration strategy for virtual network function instances. In Proceedings of the 2020 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICC45622.2020.9149047
- Nahhas, A., Haertel, C., Daase, C., Volk, M., Ramesohl, A., Steigerwald, H., Zeier, A., & Turowski, K. (2022). On the integration of Google Cloud and SAP HANA for adaptive supply chain in retailing. Procedia Computer Science, 217, 1857–1866. https://doi.org/10.1016/S1877-0509(22)02471-1
- Perumallaplli, R. (2015). Inventory management automation in SAP using machine learning algorithm. SSRN. https://doi.org/10.2139/ssrn.5228691
- Rossi, A., Visentin, A., Carraro, D., Prestwich, S., & Brown, K. N. (2023). Forecasting workload in cloud computing: Towards uncertainty-aware predictions and transfer learning. arXiv preprint arXiv:2303.13525. https://doi.org/10.48550/arXiv.2303.13525
- Shaik, M., & Siddique, K. Q. (2023). Predictive analytics in supply chain management using SAP and AI. Journal of Computer Sciences and Applications, 11(1), 1–6. https://doi.org/10.12691/jcsa-11-1-1
- Wang, Z., Zhou, L., Das, A., Dave, V., Jin, Z., & Zou, J. (2020). Survive the schema changes: Integration of unmanaged data using deep learning. In 2020 IEEE International Conference on Big Data (BigData) (pp. 4837–4847). IEEE. https://doi.org/10.1109/BigData50022.2020.9378299
- Xu, M., Song, C., Wu, H., Gill, S. S., Ye, K., & Xu, C. (2022). EsDNN: Deep neural network based multivariate workload prediction approach in cloud environment. IEEE Transactions on Cloud Computing, 10(3), 1335–1347. https://doi.org/10.1109/TCC.2022.3183746a