International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Grocery Demand Insight and Stock Planning System |
|---|---|
| ABSTRACT | This paper presents an analytical study of grocery demand patterns and inventory optimization in a retail transaction environment. A Random Forest regression model is employed to forecast product demand and support stock planning decisions. Feature engineering techniques are used to derive indicators such as seasonal trends, discount impact, sales velocity, and stock risk levels. Experimental results demonstrate that machine learning–based demand prediction and stock classification effectively identify understock and overstock situations, providing actionable insights for data‑driven inventory management and operational efficiency in grocery retail systems. |
| AUTHOR | SAUPARNESH G, VIGNESH D, ANITHA M UG Student, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Coimbatore, India Assistant Professor, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College Coimbatore, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403109 |
| pdf/109_Grocery Demand Insight and Stock Planning System.pdf | |
| KEYWORDS | |
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