Multi-Agent Big Data Framework for Wholesale Food Logistics
Keywords:
Wholesale Perishable Food Supply Chains, Supply Chain Optimization, Agentic Decision-Making Architecture, Sustainable Supply Chain Systems, Privacy-Preserving Data Ingestion, Distributed Data Ecosystems, Agentic Orchestration Layer, Agent-Based Modelling, Event-Driven Orchestration, Trigger-Action Workflows, Social Computing Protocols, Big Data Analytics, Real-Time Demand Forecasting, Inventory Allocation Optimization, Path-Dependence Theory, Complex Adaptive Systems, Human-Computer Interaction Design, Cloud And On-Premise Integration, Polybase Big Data Technologies, Value-Sharing Sentiment Analysis.Abstract
Supply chain optimization in the wholesale perishable food sector is complicated by the challenge of minimizing waste and profit loss from missed demand. An agentic, sustainable, and privacy-preserving architecture to ingest private data from distributed members is outlined, which supports the orchestration of decision-making workflows either directly in Cloud frameworks or privately in members’ premises. The agentic orchestration layer integrates an Agent-Based Modelling architecture with event-driven and trigger-action orchestration. Social-Computing protocols enable external agent coordination.
The framework uses Big Data modelling and Agents to support six decision-making workflows for real-time optimization of wholesale perishable food supply chains. These workflows forecast total demand from private Big Data events and optimize inventory allocation, sensing, and fulfillment. Path-Dependence Theory, complex adaptive systems, and Human-Computer Interaction design the architecture. Built with Big Data technologies and Polybase integration of SQL Server, Azure SQL Data Warehouse, and Hadoop, the framework seeks to stimulate supply chain member investment through value-sharing sentiment analysis.
References
1. Koot, M., Mes, M., & Iacob, M. E. (2021). A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics. Computers & Industrial Engineering, 154, 107076.
2. Liu, S., Hennequin, S., & Roy, D. (2021). Enterprise platform of logistics services based on a multi-agents mechanism and blockchains. IFAC-PapersOnLine, 54(1), 825–830.
3. Moshood, T. D., Nawanir, G., Sorooshian, S., & Okfalisa, O. (2021). Digital twins driven supply chain visibility within logistics: A new paradigm for future logistics. Applied System Innovation, 4(2), 29.
4. Davuluri, P. S. L. N. (2021). Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence. Journal of International Crisis and Risk Communication Research, 339-354.
5. Busse, A., Gerlach, B., Lengeling, J. C., Poschmann, P., Werner, J., & Zarnitz, S. (2021). Towards digital twins of multimodal supply chains. Logistics, 5(2), 25.
6. Gerlach, B., Zarnitz, S., Nitsche, B., & Straube, F. (2021). Digital supply chain twins—Conceptual clarification, use cases and benefits. Logistics, 5(4), 86.
7. Hobbs, J. E. (2021). Food supply chain resilience and the COVID-19 pandemic: What have we learned? Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 69(2), 189–196.
8. Ali, I., Nagalingam, S., & Gurd, B. (2021). A resilience framework for managing supply chains during the COVID-19 pandemic. International Journal of Logistics Management, 33(2), 389–409.
9. Mukherjee, U., Dutta, S., & Bandyopadhyay, S. K. (2021). Assembling blockchain and IoT for smart food-supply chain. Asian Journal of Advances in Agricultural Research, 16(3), 49–58.
10. Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
11. Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2021). Sustainable industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 151, 112–133.
12. Rejeb, A., Keogh, J. G., & Rejeb, K. (2022). Big data in the food supply chain: A literature review. Journal of Data, Information and Management, 4, 33–47.
13. Margaritis, I., Madas, M., & Vlachopoulou, M. (2022). Big data applications in food supply chain management: A conceptual framework. Sustainability, 14(7), 4035.
14. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
15. Lee, I., & Mangalaraj, G. (2022). Big data analytics in supply chain management: A systematic literature review and research directions. Big Data and Cognitive Computing, 6(1), 17.
16. Aljabhan, B., & Abeyie, M. (2022). Big data analytics in supply chain management: A qualitative study. Journal of Advanced Transportation, 2022, 9573669.
17. Sun, J.-Y., Tang, J.-M., & Chen, Z.-R. (2022). Multi-agent learning mechanism design and simulation of multi-echelon supply chain. Computers & Industrial Engineering, 168, 108034.
18. Orjuela-Castro, J. A., Orejuela-Cabrera, J. P., & Adarme-Jaimes, W. (2022). Multi-objective model for perishable food logistics networks design considering availability and access. OPSEARCH, 59, 1244–1270.
19. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260.
20. Lagin, M., Håkansson, J., Nordström, C., Nyberg, R. G., & Öberg, C. (2022). Last-mile logistics of perishable products: A review of effectiveness and efficiency measures used in empirical research. International Journal of Retail & Distribution Management, 50(13), 116–139.
21. Luo, Z., Zhu, J., & Sun, T. (2022). Application of the IoT in the food supply chain from the perspective of carbon mitigation. Environmental Science & Technology, 56(15), 10567–10576.
22. Marchese, A., & Tomarchio, O. (2022). A blockchain-based system for agri-food supply chain traceability management. SN Computer Science, 3, 279.
23. Khanna, A., Jain, S., Burgio, A., Bolshev, V., & Panchenko, V. (2022). Blockchain-enabled supply chain platform for Indian dairy industry: Safety and traceability. Foods, 11(17), 2716.
24. Bandi, V. D. V. K. (2023, November). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205.
25. Compagnucci, L., Lepore, D., Spigarelli, F., Frontoni, E., Baldi, M., & Di Berardino, L. (2022). Uncovering the potential of blockchain in the agri-food supply chain: An interdisciplinary case study. Journal of Engineering and Technology Management, 65, 101700.
26. Hashemi-Amiri, O., Ghorbani, F., & Ji, R. (2023). Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach. Computers & Industrial Engineering, 175, 108845.
27. Nitsche, B., Brands, J., Treiblmaier, H., & Gebhardt, J. (2023). The impact of multiagent systems on autonomous production and supply chain networks: Use cases, barriers and contributions to logistics network resilience. Supply Chain Management: An International Journal, 28(5), 894–908.
28. Xu, L., Proselkov, Y., Schoepf, S., Minarsch, D., Minaricova, M., & Brintrup, A. (2023). Implementation of autonomous supply chains for digital twinning: A multi-agent approach. IFAC-PapersOnLine, 56(2), 11076–11081.
29. Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
30. Pan, L., Shan, M., & Li, L. (2023). Optimizing perishable product supply chain network using hybrid metaheuristic algorithms. Sustainability, 15(13), 10711.
31. Mantravadi, S., & Srai, J. S. (2023). How important are digital technologies for urban food security? A framework for supply chain integration using IoT. Procedia Computer Science, 217, 1678–1687.
