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Engineering and Technology OPEN ACCESS

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, USA
DOI Crossref DOI 10.37547/tajet/Volume08Issue05-12
tajet 2026
VOL. 8 / NO. 05 MAY
FULL TEXT PP. 107-118
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VOLUME 8
ISSUE 05
YEAR 2026
PAGES 107-118
RESEARCH OVERVIEW

Abstract

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.

RESEARCH TOPICS

Keywords

IoT Sensing BI Analytics Robotic Automation Business Intelligence Retail Supply Chain

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