Articles Open Access
Real-Time Supply Chain Intelligence Using Predictive Analytics for Secure SAP Retail Network Operations and Demand Forecasting
Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
Abstract
Real-time supply chain intelligence has become essential for retail enterprises seeking to manage demand volatility, inventory uncertainty, logistics disruptions, and increasingly sophisticated cybersecurity threats. This study proposes an integrated predictive analytics framework for secure SAP retail network operations and demand forecasting. The framework combines SAP enterprise transaction data, point-of-sale records, inventory levels, supplier information, order histories, seasonal patterns, promotions, and logistics indicators to generate continuous supply chain insights. Data engineering processes are applied to cleanse, normalize, integrate, and securely transform heterogeneous retail data before predictive modeling. Machine learning models, including Random Forest, Gradient Boosting, Long Short-Term Memory networks, and ensemble forecasting, are employed to predict product demand and identify potential supply chain disruptions. A security intelligence layer monitors authentication events, network activity, API interactions, and abnormal transactional behavior across SAP-connected retail environments. Predictive outputs are integrated with operational dashboards to support inventory replenishment, procurement planning, warehouse allocation, and distribution decisions. The proposed methodology emphasizes real-time processing, predictive accuracy, data confidentiality, access control, and operational resilience. By combining demand forecasting with secure SAP network intelligence, the framework enables retailers to reduce stockouts, minimize excess inventory, improve response to disruptions, and strengthen enterprise security. The study demonstrates how predictive analytics can support intelligent, secure, and adaptive retail supply chain management.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 7 |
| Number | 6 |
| Pages | 13638-13645 |
| Published | 2023-12-19 |
| Issue | Vol. 7 No. 6 (2023): International Research Journal of Innovative Engineering (IRJIE) |
References
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2. Kalaitzi, D., & Tsolakis, N. (2022). Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage. International Journal of Production Economics, 248, 108466. https://doi.org/10.1016/j.ijpe.2022.108466
3. Neela, S. (2022). Toward intelligent enterprise integration: Cloud-native middleware design patterns and adaptive stream orchestration architectures for autonomous real-time decisioning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 154–164.
4. Kollu, R. K. (2023). CRM architecture deep dive: Building for scale on Salesforce. International Journal of Engineering & Extended Technologies Research, 5(5), 7309–7312.
5. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
6. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Research Journal of Innovative Engineering, 6(6), 11433–11444.
7. Kalabhavi, V. (2023). Enterprise quote management using AI for product configuration, pricing and approval workflows in SAP CPQ. International Journal of Science, Research and Technology, 6(1), 9231–9242.
8. Mishra, D., Behera, D., Challa, A., & Vemu, C. (2021). PDCloudEX software defined network. In Integrated emerging methods of artificial intelligence & cloud computing (pp. 66–70). Springer International Publishing.
9. Pochincharla, S., & Devineni, A. (2023). Automated compliance-driven patch management and security hardening in multi-cloud banking infrastructure. International Journal of Advances in Signal and Image Sciences, 57–62.
10. Reddy, K. V. (2022). Power-aware verification closure using AI-enhanced UVM and dynamic voltage and frequency scaling models. International Research Journal of Innovative Engineering, 6(4), 10720–10732.
11. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
12. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology (IJSRAT), 5(3), 7777–7783. https://www.ijsrat.com/index.php/ijsrat/issue/view/23
13. Soundappan, S. J. (2022). Distributed Cloud Architecture for Intelligent Performance Optimization and Proactive Real-Time Threat Detection. International Research Journal of Innovative Engineering, 6(6), 11426-11432.
14. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162-8169.
15. Puram, S. (2021). Reliable and Scalable Mobile Technology Architecture: A Formal Model, Synchronization Protocol, and Reference Design. The USA Journals TAJET, 3(08), 33-48.
16. Yadavalli, V. R. (2022). Continuous trust scoring for cross-layer access governance in S/4HANA, Fiori, and SAP BTP. International Journal of Engineering & Extended Technologies Research, 4(1), 4347–4357.
17. Bitragunta, S. L. V., & Paramasivan, M. (2023). Midterm dynamic simulation for the governance of reserves in systems with elevated renewable energy integration. Journal of Artificial Intelligence, 1(1), 1956–1962.
18. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
19. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
20. Selvarajan, K. (2023). Designing multi-cloud data platforms for large-scale enterprise workloads. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5992–6002.
21. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466-5469.
22. Vimal Raja, G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
23. Bhattacharjee, B., Khan, M. A. N., Enayet, M. A., Gazi, M. S., Tasnim, M., Jakir, T., ... & Himeluzzaman, M. (2021). Adversarial Machine Learning and Cognitive AI for Autonomous Defense against Advanced Cyber Threats. International Journal of Computer Technology and Electronics Communication, 4(6), 4327-4337.
24. Chinnam, N. B. (2023). Modernizing retail fulfillment operations through automated load execution and microservices. International Journal of Computer Technology and Electronics Communication, 6(4), 7367–7371.
25. Kundavaram, R. R., Bandhela, R. R., & Onteddu, A. R. (2022). AI-driven predictive modeling in healthcare: A data science perspective on U.S. healthcare data. South Eastern European Journal of Public Health. https://doi.org/10.70135/seejph.vi.6691
26. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
27. Shaik, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985-9996.
28. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
29. Ramasamy, M. (2022). A reference architecture for AI-driven network assurance in large-scale enterprise networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4336–4346.
30. Bag, S., Rahman, M. S., Srivastava, G., Chan, H.-L., & Bryde, D. J. (2022). The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events. International Journal of Production Economics, 251, 108541. https://doi.org/10.1016/j.ijpe.2022.108541
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