Data-Driven Decision Making in SAP Supply Chains: Leveraging AI and ML on Google Kubernetes Engine for Predictive and Prescriptive Insights
DOI:
https://doi.org/10.15662/IJARCST.2021.0406002Keywords:
Data-Driven Decision-Making, SAP Supply Chain, Artificial Intelligence (AI), Machine Learning (ML), Google Kubernetes Engine (GKE), Predictive Analytics, Prescriptive Insights, Cloud-Native Infrastructure, Demand Forecasting, Supply Chain OptimizationAbstract
Modern SAP-driven supply chains generate massive amounts of operational and transactional data, creating opportunities for smarter, data-driven decision-making. This paper proposes an AI- and machine learning–enabled framework deployed on Google Kubernetes Engine (GKE) to deliver predictive and prescriptive insights for end-to-end supply chain optimization. By leveraging scalable cloud-native infrastructure, the system orchestrates distributed learning models that analyze real-time demand, inventory, and logistics data within SAP environments. Predictive analytics modules are employed to forecast demand fluctuations, supplier performance, and replenishment needs, while prescriptive models recommend optimal strategies for procurement, production planning, and warehouse management. GKE ensures elasticity, high availability, and fault tolerance, enabling organizations to efficiently handle dynamic workloads and large-scale datasets. Experimental evaluations highlight improvements in forecasting accuracy, decision support effectiveness, and overall supply chain resilience. The proposed approach underscores the transformative potential of integrating AI, ML, and cloud-native platforms for building intelligent, adaptive, and future-ready SAP supply chains.
References
1. SAP. (2018). What is a supply chain control tower? SAP. Retrieved from https://www.sap.com/india/resources/supply-chain-control-tower SAP
2. T. Yuan, S. Sah, T. Ananthanarayana, C. Zhang, A. Bhat, S. Gandhi, and R. Ptucha. 2019. Large scale sign language interpretation. In Proceedings of the 14th IEEE International Conference on Automatic Face Gesture Recognition (FG’19). 1–5.
3. Sasidevi Jayaraman, Sugumar Rajendran and Shanmuga Priya P., “Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud,” Int. J. Business Intelligence and Data Mining, Vol. 15, No. 3, 2019.
4. Machine learning demand forecasting and supply chain performance. (2020). International Journal of Logistics Research and Applications, 25(2), 119-142. https://doi.org/10.1080/13675567.2020.1803246 Taylor & Francis Online.
5. Tirkolaee, E. B., Rabbani, M., & Jodar, L. B. (2021). Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas. Mathematical Problems in Engineering, 2021, Article ID 1476043. https://doi.org/10.1155/2021/1476043 Wiley Online Library
6. Chellu, R. (2021). Secure containerized microservices using PKI-based mutual TLS in Google Kubernetes Engine. International Journal of Communication Networks and Information Security, 13(3), 543–553. https://doi.org/10.5281/zenodo.15708256
7. Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. (2021). Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702 ScienceDirect
8. Lekkala, C. (2019). Strategies for Effective Partitioning Data at Scale in Large-scale Analytics. European Journal of Advances in Engineering and Technology, 6(11), 49–55.
9. Radanliev, P., De Roure, D., Page, K., Nurse, J., Mantilla Montalvo, R., Santos, O., Maddox, L. T., & Burnap, P. (2019). Cyber Risk at the Edge: Current and future trends on Cyber Risk Analytics and Artificial Intelligence in the Industrial Internet of Things and Industry 4.0 Supply Chains. arXiv. arXiv
10. Badmus, A., & Adebayo, M. (2020). Compliance-Aware Devops for Generative AI: Integrating Legal Risk Management, Data Controls, and Model Governance to Mitigate Deepfake and Data Privacy Risks in Synthetic Media Deployment.
11. Devaraju, Sudheer. " Optimizing Data Transformation in Workday Studio for Global Retailers Using Rule-Based Automation."Journal of Emerging Technologies and Innovative Research 7 (4), 69 – 74
12. Sugumar, R. (2016). An effective encryption algorithm for multi-keyword-based top-K retrieval on cloud data. Indian Journal of Science and Technology 9 (48):1-5.
13. SAP. (2021, September 9). Build Supply Chain Resilience and Agility in 2022. SAP India. SAP News Center
14. Bertsimas, D., & Kallus, N. (2014). From predictive to prescriptive analytics. arXiv preprint arXiv:1402.5481. https://arxiv.org/abs/1402.5481
15. Vayyasi, N. K. (2019). Reimagining financial compliance automation: Using Java microservices and generative AI on AWS Bedrock for regulatory intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 2(3), 1992–1210.
16. Sarabu, V. B. (2018). Architecting Financially Compliant Enterprise Point-of-Sale Systems: A Scalable Data Integrity and Revenue Recognition Framework for Global Retail Platforms. International Journal of Computer Technology and Electronics Communication, 1(2), 329-341.
17. Parasa, M. (2020). Control-mapped AI governance for high-risk HR decisions in SAP SuccessFactors: Audit-ready metrics for recruiting, performance calibration, and internal mobility. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(2), 153–168. https://doi.org/10.18090/samriddhi.v12i02.15
18. Sengupta, J. (2019). Automated Inception Network based Cardiac Image Segmentation Analysis. International Journal of Advanced Science and Technology, 28(20), 953-962.
19. Boddupally, H. L. (2020). Model driven engineering of robust data pipelines: Leveraging Entity Framework constructs with SQL Server execution layers. Available at SSRN 6266000.
20. Vankayala, S. C. (2016). Advancing software integrity in regulated financial systems through intelligent CI/CD orchestration. Journal of Scientific and Engineering Research, 3(4), 582–597. https://doi.org/10.5281/zenodo.17839557
21. Yamsani, N. (2017). Enterprise-Scale Data Stewardship Enablement Using Workflow-Driven Governance Mechanisms in Financial Services. International Journal of Technology, Management and Humanities, 3(01), 18-31.
22. Prasad, P. K. (2019). DevSecOps: Securing infrastructure in the age of automation. International Journal of Research Publication in Engineering, Technology and Management, 2(1), 930–938.
