ORCID
- Abubakr Siddig: 0000-0002-4464-6538
Abstract
This paper presents an AI-driven smart shopping cart system designed to enhance retail efficiency and customer experience through real-time data analytics and machine learning. Traditional shopping carts lack capabilities for adaptive tracking, inventory management, and personalized customer interaction. Our system addresses these gaps with a multi-layered architecture that integrates person-specific tracking, reinforcement learning (RL) for navigation, and Long Short-Term Memory (LSTM) networks for demand forecasting, alongside seamless Point-of-Sale (POS) integration for automated billing. The architecture comprises real-time data capture, edge computing for low-latency decisions, and cloud processing for customer profiling and inventory management. Experimental results demonstrate notable improvements in tracking accuracy, navigation efficiency, inventory forecasting, and customer satisfaction, highlighting AI’s transformative potential in retail.
Keywords
Artificial intelligence, autonomous systems, inventory management, machine learning, personalized shopping, retail, smart shopping cart
DOI Link
Publication Date
2025-01-01
Publication Title
IEEE Access
Volume
13
First Page
55576
Last Page
55585
Deposit Date
2026-10-22
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Zulfiqar, M., Khalid, A., Siddig, A., Nawaz, M., & Saay, S. (2025) 'AI-Driven Smart Shopping Carts With Real-Time Tracking and Inventory Forecasting for Enhanced Retail Efficiency', IEEE Access, 13, pp. 55576-55585. Available at: 10.1109/ACCESS.2025.3553854