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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68516
Volume:
10 |
10 |
Oct 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Artificial Intelligence & Machine Learning
Zeal Polytechnic

Artificial Intelligence & Machine Learning
Zeal Polytechnic

Artificial Intelligence & Machine Learning
Zeal Polytechnic
Artificial Intelligence & Machine Learning
Zeal Polytechnic
Small retail shops often manage billing, stock records, and reordering manually or through disconnected tools. As a result, recorded stock can diverge from shelf conditions and replenishment decisions can be delayed. This paper proposes SmartShopAI, an integrated retail management framework combining billing, inventory control, configurable low-stock alerts, short-term sales forecasting, a controlled natural-language query interface, and image-based shelf monitoring. Each confirmed sale is treated as an atomic transaction that updates stock and checks reorder thresholds, while the analytical modules read completed sales records and produce advisory outputs. The paper contributes a layered architecture, a data model with an audit trail, safety constraints for AI-assisted querying and vision alerts, and an evaluation protocol with defined metrics. It is a design and evaluation-protocol paper: it reports no measured accuracy, user-study results, or field outcomes. Its findings concern design requirements and the evidence needed before the system can be relied on in daily retail work.
KEYWORDS
This paper presents SmartShopAI, a layered framework for small-shop retail that integrates billing, inventory control, alerts, and three AI advisory modules. Its core contribution is its architectural design, which features a transactional core with explicit integrity rules, isolated visual-versus-database stock tracking, and AI safety constraints. No performance metrics are claimed; the expected benefits—improved stock accuracy, automated reorder alerts, and reduced routine effort—are hypotheses to be tested via the empirical protocol in Section VIII. Next steps include implementing the core, collecting a retail dataset, and evaluating all modules against Table III metrics.
[1] T. W. Gruen, D. S. Corsten, and S. Bharadwaj, "Retail out-of-stocks: A worldwide examination of extent, causes and consumer responses," Grocery Manufacturers of America, Washington, DC, USA, 2002. [2] D. Corsten and T. Gruen, "Desperately seeking shelf availability: An examination of the extent, the causes, and the efforts to address retail out-of-stocks," Int. J. Retail Distrib. Manag., vol. 31, no. 12, 2003, doi: 10.1108/09590550310507731. [3] B. Lim and S. Zohren, "Time-series forecasting with deep learning: A survey," Philos. Trans. R. Soc. A, vol. 379, no. 2194, Art. no. 20200209, 2021, doi: 10.1098/rsta.2020.0209. [4] L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324. [5] T. Yu et al., "Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task," in Proc. EMNLP, 2018, pp. 3911–3921, doi: 10.18653/v1/D18-1425. [6] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proc. IEEE CVPR, 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91. [7] G. Bradski, "The OpenCV library," Dr. Dobb's J. Softw. Tools, 2000. [8] F. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
Publication Date
10/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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