Building an AI Portfolio: A Technical Guide for Modern Logistics Specialists

Anoop Sagar Pradhan

doi:10.32628/CSEIT24106198

Authors

Anoop Sagar Pradhan Oracle America Inc., USA Author

DOI:

https://doi.org/10.32628/CSEIT24106198

Keywords:

Artificial Intelligence in Logistics, Supply Chain Optimization, Machine Learning Portfolio, Predictive Analytics, Logistics Automation

Abstract

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.

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References

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