Building an AI Portfolio: A Technical Guide for Modern Logistics Specialists
DOI:
https://doi.org/10.32628/CSEIT24106198Keywords:
Artificial Intelligence in Logistics, Supply Chain Optimization, Machine Learning Portfolio, Predictive Analytics, Logistics AutomationAbstract
This technical article provides a comprehensive framework for logistics specialists to build an effective AI portfolio in modern supply chain management. As the industry processes massive volumes of data annually, the integration of artificial intelligence has revolutionized logistics operations. The article addresses critical aspects, including demand forecasting, route optimization, inventory management, and security implementations. With AI implementations demonstrating significant performance improvements across cycle time reduction, transportation expense optimization, and inventory accuracy enhancement, the need for structured portfolio development has become increasingly important. The article outlines detailed technical requirements, implementation frameworks, and best practices for developing AI solutions in logistics, supported by real-world performance metrics and industry standards. Special attention is given to security considerations, scalability architecture, and professional development strategies, providing logistics professionals with a holistic approach to AI integration. The comprehensive coverage encompasses technical depth and practical implementation guidelines, making it a valuable resource for professionals seeking to advance their careers in AI-driven logistics operations.
Downloads
References
Sachin S. Kamble, Rahul S. Mor, Amine Belhadi, "Big Data Analytics for Supply Chain Transformation: A Systematic Literature Review Using SCOR Framework," SPRINGER LINK IN 2023. https://link.springer.com/chapter/10.1007/978-3-031-19711-6_1 DOI: https://doi.org/10.1007/978-3-031-19711-6_1
N. Patel, V. Garcia, and S. Kim, "Performance Metrics For AI In Supply Chain ," AI in Supply Chain. https://www.restack.io/p/ai-in-supply-chain-knowledge-performance-metrics-cat-ai
Asmaul Husna, S. H. Amin, Bharat Shah, "Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods," Published in Advances in Logistics… 2021. https://www.semanticscholar.org/paper/Demand-Forecasting-in-Supply-Chain-Management-Using-Husna-Amin/682ceb0c38d45c651540264a048d90633e2f6b02
Restack, "Performance Metrics for AI-Driven Logistics Systems: A Comprehensive Framework," AI in Logistics and Distribution/ AI in Logistics Performance Metrics. https://www.restack.io/p/ai-in-logistics-knowledge-performance-metrics-cat-ai
Nithy, "Microservice Architecture Design Patterns: The Secret to Building Scalable, Resilient, and Maintainable Microservices," 2023. https://gnithyanantham.medium.com/microservice-architecture-design-patterns-the-secret-to-building-scalable-resilient-and-82747886c017
D. Gannon, Roger Barga and Neel Sundaresan, "Cloud-Native Applications," IEEE Cloud Computing, vol. 4, Issue 5, Sep 2017. https://ieeexplore.ieee.org/abstract/document/8125550 DOI: https://doi.org/10.1109/MCC.2017.4250939
Restack, "Ai For Knowledge Management In Logistics," AI for Knowledge Management/Ai For Knowledge Management. https://www.restack.io/p/ai-for-knowledge-management-answer-logistics-cat-ai
IEEE, "Building IEEE Communities that Matter Community Building: Key To IEEE Success. https://mga.ieee.org/images/files/sections_congress/SC2014/Building_Technical_Communities.pdf
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
