Edge-to-Cloud Order Management with Generative AI
Keywords:
Generative AI in Order Management, Edge AI Systems, Hybrid Cloud Architectures, Real-Time Order Processing, Low-Latency AI Systems, Distributed Edge Computing, Order Scheduling Optimization, Work Order Forecasting, AI Decision Automation, Latency-Aware AI Models, Edge Deployment Strategies, Operational AI Systems, Real-Time Inference, AI Performance Metrics, Cost-Efficiency in AI, Discriminative vs Generative Models, Time-to-Decision Optimization, Scalable Edge AI, Intelligent Order Systems, AI-Driven Operations.Abstract
Next-generation order management powered by generative AI is a timely concept, aligned with ongoing developments in edge computing and hybrid cloud data centers. Most existing work focuses on online inference of generative models without strict latency requirements, yet workloads demanding fast, real-time responses and distributed edge execution increasingly face latency-sensitive challenges. Reliable, low-delay order management stands to benefit significantly from generative AI, particularly as active exploration of these methods for operational decision automation — such as order scheduling and work-order forecasting — promises greater responsiveness and shorter time-to-decision.
However, implementation and use-case analyses in this space remain sparse or entirely absent. Performance evaluations are typically limited to standard generative metrics, namely output accuracy, while latency metrics receive comparatively little attention despite their equal importance. Moreover, generative methods are often assumed to be more computationally costly than discriminative approaches — an assumption that merits closer scrutiny in decision-automation contexts, especially when models are deployed locally at the edge.
References
1. Azizi, N., Malekzadeh, H., Akhavan, P., Haass, O., Saremi, S., & Mirjalili, S. (2021). IoT–Blockchain: Harnessing the power of Internet of Thing and Blockchain for smart supply chain. Sensors, 21(18), 6048.
2. Cao, K., Hu, S., Shi, Y., Colombo, A. W., Karnouskos, S., & Li, X. (2021). A survey on edge and edge-cloud computing assisted cyber-physical systems. IEEE Transactions on Industrial Informatics, 17(12), 8719–8730.
3. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
4. Chang, Z., Liu, S., Xiong, X., Cai, Z., & Tu, G. (2021). A survey of recent advances in edge-computing-powered artificial intelligence of things. IEEE Internet of Things Journal, 8(18), 13849–13875.
5. Deng, C., & Liu, Y. (2021). A deep learning-based inventory management and demand prediction optimization method for anomaly detection. Wireless Communications and Mobile Computing, 2021, 9969357.
6. Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.
7. Islam, S., Amin, S. H., & Wardley, L. J. (2021). Machine learning and optimization models for supplier selection and order allocation planning. International Journal of Production Economics, 242, 108315.
8. Laroui, M., Nour, B., Moungla, H., Cherif, M. A., Afifi, H., & Guizani, M. (2021). Edge and fog computing for IoT: A survey on current research activities & future directions. Computer Communications, 180, 210–231.
9. Loganathan, R., & Kolla, S. H. (2024). Harnessing generative AI for adaptive risk policy generation in multi-regulatory data center environments. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 505-515.
10. Mijuskovic, A. (2021). Towards integration of logistics processes from a cloud/fog-edge computing perspective. In 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 349–355). IEEE.
11. Pereira, M. M., & Frazzon, E. M. (2021). A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains. International Journal of Information Management, 57, 102165.
12. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
13. Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250.
14. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517.
15. Zhu, X., Ninh, A., Zhao, H., & Liu, Z. (2021). Demand forecasting with supply-chain information and machine learning: Evidence in the pharmaceutical industry. Production and Operations Management, 30(9), 3231–3252.
16. Reddy, V. A. R. (2024). Generative Intelligence for Healthcare Claims Processing and Personalized Benefits Management. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14099.
17. Wang, T., Chen, H., Dai, R., & Zhu, D. (2022). Intelligent logistics system design and supply chain management under edge computing and Internet of Things. Computational Intelligence and Neuroscience, 2022, 1823762.
18. Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387-2399.
19. Preil, D., & Krapp, M. (2022). Artificial intelligence-based inventory management: A Monte Carlo tree search approach. Annals of Operations Research, 308, 415–439.
20. Gautham, J., Kurian, J., & colleagues. (2022). Deep reinforcement learning-based ordering mechanism for performance optimization in multi-echelon supply chains. Applied Stochastic Models in Business and Industry.
21. Rosendo, D., Costan, A., Valduriez, P., & Antoniu, G. (2022). Distributed intelligence on the edge-to-cloud continuum: A systematic literature review. Journal of Parallel and Distributed Computing, 166, 71–94.
22. Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
23. Wang, Y., Wang, X., & others. (2022). AI technologies and their impact on supply chain resilience during COVID-19. International Journal of Physical Distribution & Logistics Management, 52(2), 130–149.
24. Supply chain visibility: A Delphi study on managerial perspectives and priorities. (2022). International Journal of Production Research, 60, 2927–2942.
25. Visibility model for enhancing supply chains resilience. (2022). IFAC-PapersOnLine, 55(10), 2521–2525.
26. Zhang, Y., & others. (2022). A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges. IEEE Internet of Things Journal, 9(17), 15435–15459.
27. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
28. Wamba, S. F., Queiroz, M. M., Jabbour, C. J. C., & Shi, C. V. (2023). Are both generative AI and ChatGPT game changers for 21st-century operations and supply chain excellence? International Journal of Production Economics, 265, 109015.
29. Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management, 59(3), 65–76.
30. Richey, R. G., Jr., Chowdhury, S., Davis-Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics.
31. Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2024). ChatGPT and generative artificial intelligence: An exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676–5696.
32. Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Computer Fraud and Security.
33. Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120–6145.
34. Li, L., Liu, Y., Jin, Y., Cheng, T. C. E., & Zhang, Q. (2024). Generative AI-enabled supply chain management: The critical role of coordination and dynamism. International Journal of Production Economics, 277, 109388.
35. Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2024). Benchmarking operations and supply chain management practices using generative AI: Towards a theoretical framework. Transportation Research Part E: Logistics and Transportation Review, 189, 103689.
36. Mahadevan, S. (2024). Clouding the Future: Innovating Towards Net-Zero Emissions. International Journal of Computing and Engineering, 6(2), 17-23.
37. Al-Khatib, A. W., Al-Shboul, M. A., & Khattab, M. (2024). How can generative artificial intelligence improve digital supply chain performance in manufacturing firms? Analyzing the mediating role of innovation ambidexterity using hybrid analysis through CB-SEM and PLS-SEM. Technology in Society, 78, 102676.
38. Haddud, A. (2024). ChatGPT in supply chains: Exploring potential applications, benefits and challenges. Journal of Manufacturing Technology Management, 35(7), 1293–1312.
39. Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. Journal of Purchasing and Supply Management, 30(1), 100896.
40. Ashraf, M., Eltawil, A., & Ali, I. (2024). Disruption detection for a cognitive digital supply chain twin using hybrid deep learning. Operational Research, 24, Article 23.
41. Kosasih, E. E., Papadakis, E., Baryannis, G., & Brintrup, A. (2024). A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches. International Journal of Production Research, 62(4), 1510–1540.
42. Seyedan, M., Mafakheri, F., & Wang, C. (2023). Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning. Supply Chain Analytics, 3, 100024.
43. Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026.
44. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
45. Islam, S., Amin, S. H., & Wardley, L. J. (2021). Machine learning and optimization models for supplier selection and order allocation planning. International Journal of Production Economics, 242, 108315.
46. Alsolbi, I., Shavaki, F. H., Agarwal, R., Bharathy, G. K., Prakash, S., & Prasad, M. (2023). Big data optimisation and management in supply chain management: A systematic literature review. Artificial Intelligence Review, 56, 253–284.
