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Prakash Jeganathan Perumal

Industrial Engineering

Independent Researcher · United States

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About

A strategic, results-driven professional with over 20 years of extensive experience in Supply Chain, Retail, and 3PL Operations. Proven track record of leading global supply chain initiatives that deliver substantial cost savings, operational efficiencies, and innovative solutions. Expertise includes Warehouse Management Systems (WMS), Warehouse Execution Systems (WES), automation of material handling equipment, and implementation of AI and robotics for warehouse optimization. Skilled in advanced analytics and managing complex global logistics networks. A demonstrated leader and mentor with a strong history of building high-performing, cross-functional teams. Committed to continuous improvement and scalable solutions, with success rooted in collaborative partnerships across clients, leadership, suppliers, operations, and IT stakeholders.

Beyond my industry responsibilities, I remain actively involved in research and academic engagement, particularly in smart logistics, AI-enabled supply chains, resilient network design, and Industry 4.0 applications in warehousing. I have presented at international conferences and continue contributing to discussions around intelligent automation and sustainable logistics systems.

My primary areas of interest include:

1. Smart warehousing and cyber-physical logistics systems

2. Artificial intelligence and machine learning applications in supply chain management

3. Warehouse automation systems (ASRS, AutoStore, shuttle systems, robotics)

4. Optimization models for distribution networks

5. Industry 4.0 and Industry 5.0 in logistics

6. Supply chain resilience and risk modeling

7. Sustainable and green logistics system design

8. Enterprise systems integration (WMS, WES, ERP)

9. Human-automation collaboration in distribution environments

Professional Affiliations:

1. Chartered Member – Chartered Institute of Logistics and Transport (CILT UK)

2. Professional Member – Association for Supply Chain Management (ASCM)

3. Professional Member – Institute of Industrial and Systems Engineers (IISE)

Research keywords

Operations ResearchSupply ChainQuality ManagementOperations Management, Smart Warehousing, Warehouse Automation, AI Robotics, Supply Chain Resilience, Supply Chain Risk ManagementHuman-automation collaboration in distribution environments, Sustainable and green logistics system design, Industry 4.0 and Industry 5.0 in logistics

Publications

Ontology-Driven AI Integration in Supply Chain Management: A Design Science Framework

International Journal of Supply Chain Management · 2026

Supply chain organizations are hitting a wall with AI (Artificial Intelligence) automation initiatives because their data is a fragmented mess. Inconsistent terminology and siloed systems create a structural bottleneck that kills AI effectiveness before it can even scale. This study delivers the essential semantic infrastructure with a specialized ontology framework specifically designed to bridge these existing gaps. It transforms AI from a disconnected tool into a scalable, explainable, and high-performing asset that actually works across the entire supply chain network. Utilizing a rigorous Design Science Research (DSR) approach, this study moves beyond theory to build a high-functioning solution for real-world operational failures. By analysing documented semantic gaps in current supply chains, a domain-specific ontology was engineered to formalize every relationship, attribute, and constraint within the operation. This framework was then stress-tested through scenario analysis across procurement, forecasting, inventory management, and logistics to ensure it demonstrates potential utility and logical coherence. The results are clear, this ontology successfully bridges the gap between fragmented systems, ensuring data remains consistent and actionable across the entire network. By embedding logical rules and explicit relationships directly into the data structure, the framework facilitates interoperability across heterogeneous platforms. This doesn't just make systems talk to each other; it empowers AI to perform complex reasoning and enhances traceability for every decision made, ensuring total transparency. This research redefines the “ontology” as a strategic mandate for AI success rather than a mere modelling exercise. It proves that semantic architecture is the primary driver of AI scalability. For industry leaders, this provides a structured pathway for harmonizing data definitions and building a foundation that can support large-scale, high-stakes AI integration.

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