AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation

Muhammad Saqib Javaid; Rimsha Chauhdary; Aashir Waleed; Farhan Ahmad; Muhammad Zubair; Osama Tariq

doi:10.1109/ICETECC65365.2025.11070285

AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation

Muhammad Saqib Javaid
, Rimsha Chauhdary
, Aashir Waleed^*
, Farhan Ahmad
, Muhammad Zubair
, Osama Tariq

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Traditional inventory management systems suffer from inefficiencies, manual intervention, and high error rates. This paper presents an AI-driven smart inventory system using QR code scanning, machine learning (Random Forest, Linear Regression), and predictive analytics for real-time stock tracking and sales forecasting. A Raspberry Pi 5 with webcams enables automated product scanning, achieving 99.2% accuracy with 0.9s latency. Out of the two models Random Forest model indicated higher R-squared of 0.89 compared to the Linear regression model with R-squared of 0.68. The system improves inventory efficiency by predicting demand trends and alerting users about low stock levels. Security measures include QR code authentication, role-based access control, and AES-256 encryption to protect inventory data. Compared to traditional systems and commercial solutions like SAP and Oracle, our approach is cost-effective, scalable, and suitable for small to medium-sized businesses. Future work includes API integration with e-commerce platforms and warehouse automation.

Original language	English
Title of host publication	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331543389
DOIs	https://doi.org/10.1109/ICETECC65365.2025.11070285
State	Published - 2025
Externally published	Yes
Event	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025 - Jamshoro, Pakistan Duration: 23 Apr 2025 → 25 Apr 2025

Publication series

Name	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

Conference

Conference	2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025
Country/Territory	Pakistan
City	Jamshoro
Period	23/04/25 → 25/04/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

AI
Inventory Management
Machine Learning
Predictive Analytics

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/ICETECC65365.2025.11070285

Cite this

Javaid, M. S., Chauhdary, R., Waleed, A., Ahmad, F., Zubair, M., & Tariq, O. (2025). AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation. In 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025 (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICETECC65365.2025.11070285

Javaid, Muhammad Saqib ; Chauhdary, Rimsha ; Waleed, Aashir et al. / AI-Powered Smart Inventory Management : Enhancing Efficiency Through Predictive Analytics and Automation. 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. Institute of Electrical and Electronics Engineers Inc., 2025. (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025).

@inproceedings{a24f712de8ec48669a6d4959af39d594,

title = "AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation",

abstract = "Traditional inventory management systems suffer from inefficiencies, manual intervention, and high error rates. This paper presents an AI-driven smart inventory system using QR code scanning, machine learning (Random Forest, Linear Regression), and predictive analytics for real-time stock tracking and sales forecasting. A Raspberry Pi 5 with webcams enables automated product scanning, achieving 99.2\% accuracy with 0.9s latency. Out of the two models Random Forest model indicated higher R-squared of 0.89 compared to the Linear regression model with R-squared of 0.68. The system improves inventory efficiency by predicting demand trends and alerting users about low stock levels. Security measures include QR code authentication, role-based access control, and AES-256 encryption to protect inventory data. Compared to traditional systems and commercial solutions like SAP and Oracle, our approach is cost-effective, scalable, and suitable for small to medium-sized businesses. Future work includes API integration with e-commerce platforms and warehouse automation.",

keywords = "AI, Inventory Management, Machine Learning, Predictive Analytics",

author = "Javaid, \{Muhammad Saqib\} and Rimsha Chauhdary and Aashir Waleed and Farhan Ahmad and Muhammad Zubair and Osama Tariq",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025 ; Conference date: 23-04-2025 Through 25-04-2025",

year = "2025",

doi = "10.1109/ICETECC65365.2025.11070285",

language = "English",

series = "2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025",

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Javaid, MS, Chauhdary, R, Waleed, A, Ahmad, F, Zubair, M & Tariq, O 2025, AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation. in 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025, Jamshoro, Pakistan, 23/04/25. https://doi.org/10.1109/ICETECC65365.2025.11070285

AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation. / Javaid, Muhammad Saqib; Chauhdary, Rimsha; Waleed, Aashir et al.
2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. Institute of Electrical and Electronics Engineers Inc., 2025. (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - AI-Powered Smart Inventory Management

T2 - 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

AU - Javaid, Muhammad Saqib

AU - Chauhdary, Rimsha

AU - Waleed, Aashir

AU - Ahmad, Farhan

AU - Zubair, Muhammad

AU - Tariq, Osama

PY - 2025

Y1 - 2025

N2 - Traditional inventory management systems suffer from inefficiencies, manual intervention, and high error rates. This paper presents an AI-driven smart inventory system using QR code scanning, machine learning (Random Forest, Linear Regression), and predictive analytics for real-time stock tracking and sales forecasting. A Raspberry Pi 5 with webcams enables automated product scanning, achieving 99.2% accuracy with 0.9s latency. Out of the two models Random Forest model indicated higher R-squared of 0.89 compared to the Linear regression model with R-squared of 0.68. The system improves inventory efficiency by predicting demand trends and alerting users about low stock levels. Security measures include QR code authentication, role-based access control, and AES-256 encryption to protect inventory data. Compared to traditional systems and commercial solutions like SAP and Oracle, our approach is cost-effective, scalable, and suitable for small to medium-sized businesses. Future work includes API integration with e-commerce platforms and warehouse automation.

AB - Traditional inventory management systems suffer from inefficiencies, manual intervention, and high error rates. This paper presents an AI-driven smart inventory system using QR code scanning, machine learning (Random Forest, Linear Regression), and predictive analytics for real-time stock tracking and sales forecasting. A Raspberry Pi 5 with webcams enables automated product scanning, achieving 99.2% accuracy with 0.9s latency. Out of the two models Random Forest model indicated higher R-squared of 0.89 compared to the Linear regression model with R-squared of 0.68. The system improves inventory efficiency by predicting demand trends and alerting users about low stock levels. Security measures include QR code authentication, role-based access control, and AES-256 encryption to protect inventory data. Compared to traditional systems and commercial solutions like SAP and Oracle, our approach is cost-effective, scalable, and suitable for small to medium-sized businesses. Future work includes API integration with e-commerce platforms and warehouse automation.

KW - AI

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UR - https://www.scopus.com/pages/publications/105012113974

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DO - 10.1109/ICETECC65365.2025.11070285

M3 - Conference contribution

AN - SCOPUS:105012113974

T3 - 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

BT - 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 23 April 2025 through 25 April 2025

ER -

Javaid MS, Chauhdary R, Waleed A, Ahmad F, Zubair M, Tariq O. AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation. In 2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025. Institute of Electrical and Electronics Engineers Inc. 2025. (2nd International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2025). doi: 10.1109/ICETECC65365.2025.11070285

AI-Powered Smart Inventory Management: Enhancing Efficiency Through Predictive Analytics and Automation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this