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@article{202772,
        author = {Aryaman Raj and Dr Biranchi Prasad Panda},
        title = {Comparing Smart Warehousing Technologies Adopted in the Process Industry},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9985-10000},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202772},
        abstract = {The process industry encompassing chemical manufacturing, petroleum refining, pharmaceuticals, food processing, and allied sectors operates in a uniquely demanding warehousing environment characterized by stringent regulatory requirements, hazardous materials management, and highly complex inventory ecosystems. The advent of smart warehousing technologies presents transformative opportunities for these industries, yet a systematic comparative evaluation of available solutions remains underexplored in the academic literature.
This dissertation undertakes a rigorous comparative analysis of eight dominant smart warehousing technologies: Warehouse Management Systems (WMS), Automated Guided Vehicles (AGV) and Autonomous Mobile Robots (AMR), Internet of Things (IoT) and sensor networks, Artificial Intelligence and Machine Learning (AI/ML), Radio Frequency Identification (RFID), Digital Twin technology, Blockchain for supply chain visibility, and Automated Storage and Retrieval Systems (AS/RS). The analysis is structured across five critical dimensions: operational efficiency, safety and regulatory compliance, total cost of ownership (TCO), technology maturity, and scalability within the process industry context.
Findings indicate that no single technology constitutes a universal solution; rather, an integrated architecture combining WMS, IoT, and AI/ML delivers the highest value for process-intensive environments. RFID and Digital Twins emerge as particularly impactful for regulatory compliance and predictive maintenance, respectively. The dissertation concludes with a decision framework to guide technology selection based on facility scale, product risk classification, and digital maturity level.},
        keywords = {Smart Warehousing, Process Industry, WMS, AGV/AMR, IoT, AI/ML, RFID, Digital Twin, Blockchain, AS/RS, Supply Chain Technology},
        month = {May},
        }