Engineering and Technology
| Open Access |
DOI: Advancing U.S. Retail Supply Chain Efficiency and Resilience by Integrating IoT Sensing, Artificial Intelligence (AI)-driven BI Analytics, and Robotic Automation.
Md Ashiqur Rahman Khan , Executive MBA (Management), University of Dhaka, Dhaka, Bangladesh Foysal Mahmud , Masters of IT Project Management, Westcliff University, Irvine, CA, USA Mehedi Hasan , MBA, American International University, Dhaka, Bangladesh Achhia Khanam , MBA in Accounting & Business Analytics, Maharishi International University, Fairfield, Iowa. Songeta Dhar , DBA (Doctor of Business Administration), Westcliff University, Los Angeles, California, USA Kazi Obaidur Rahman , MBA (Business Analytics), Gannon University, Erie, Pennsylvania, USA. Shamina Sharmin Jishan , MBS (Accounting), National University, Dhaka, Bangladesh. Mohammad Tameem Hossain Azmi , Master of Applied Business Analytics, University of Toledo, Toledo, OH, USAAbstract
The global retail supply chain ecosystem has been restructuring with the digital transformation driven by demand volatility, geopolitical instability (recent tariff and counter tariff effects), and consumer preference. The U.S. retail faces challenges like persistent inflation squeezing consumer spending, skilled labor shortages, operational inefficiency and supply chain volatility. Traditional retailers are struggling to manage inventory, adapt to shifting consumer demands for omnichannel experiences, and cope with high operational costs and increased security risks. Therefore, the integration of IoT (Internet of Things) sensing, BI (Business Intelligence) analytics, and robotic automation is crucial here in improving U.S. retail supply chain efficiency and strengthen resilience.
This article focuses on developing framework by integrating IoT sensing, BI analytics, and robotic automation to enhance efficiency and resilience in U.S. retail supply chains. The framework enables retailers with comprehensive knowledgebase on how to capture quality data through IoT devices, transforming streaming signals into predictive and prescriptive insights using BI system, and executing optimized responses autonomously by integrating robotic platforms. This integration will facilitate retail operations in evaluating performance impacts across stock/inventory accuracy, fulfillment speed, downtime reduction, forecast precision, and disruption recovery. The findings demonstrate that integrated IoT–BI–robotics architectures represent a strategic pathway for strengthening U.S. retail competitiveness and supply chain resilience.
Keywords
IoT Sensing, BI Analytics, Robotic Automation, Business Intelligence, Retail Supply Chain
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Copyright (c) 2026 Md Ashiqur Rahman Khan, Foysal Mahmud, Mehedi Hasan, Achhia Khanam, Songeta Dhar, Kazi Obaidur Rahman, Shamina Sharmin Jishan, Mohammad Tameem Hossain Azmi
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