AI driven Automation for Inventory Management System

In conclusion, this research has successfully demonstrated the development and implementation of a comprehensive AI-driven automation framework designed to transform inventory management practices within the grocery retail sector. By addressing the critical challenges of stock optimization, waste reduction, and demand forecasting, the system provides a robust, data-driven solution to the persistent inefficiencies of traditional methods. The resulting web-based application, built on the Flask framework, stands as a testament to the practical application of machine learning in a commercial context. The key achievement of this work lies in its holistic and user-centric design. The system seamlessly integrates secure user authentication with an intuitive data exploration module that demystifies the machine learning pipeline. By providing transparent visualizations of data preprocessing and algorithmic performance comparisons, it empowers users to build trust and understanding in the predictive models.

              [1].Madamidola, Daramola Oladunni, Akintola Kolawole “ A Review of Existing Inventory Management Systems “Volume 12 Issue 9 ǁ September 2024 ǁ PP. 40-50, International Journal of Research in Engineering and Science (IJRES)               [2].Prashant Tripathi , Prakhyat Bhartia, Sonam Gupta, Rajveer Singh “ Bridging the Digital Gap: A Web - Based Inventory Management System “ FUDMA Journal of Sciences , volume 4 , issue 2 , p. 86 – 91 , Proceedings of the International Conference on Innovative Computing & Communication (ICICC 2024)               [3]. Karan Wanchoo Gurgaon, India “Retail demand forecasting: A comparative study of machine learning models”, 2019 IEEE 5th International Conference for Convergence in Technology (I2CT)               [4].M. Orra and G. Serpen, “An Exploratory Study for Neural Network Forecasting of Retail Sales Trends Using Industry and National Economic Indicators”, in Proceedings for the 4th Workshop on Computational Intelligence in Economics and Finance, Salt Lake City, USA, 2005, pp. 875–878.               [5].Xie dairu, Zhang Shilong “ Machine Learning Model for Sales Forecasting by Using XGBoost”, Published in: 2021 IEEE InternationalConference on Consumer Electronics and Computer Engineering (ICCECE)               [6].Yuta Kaneko, Katsutoshi Yada “ A Deep Learning Approach for the Prediction of Retail Store Sales” 10.1109/ICDMW.2016.0082 , Published in: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)               [7].H. Witten, and E. Frank, “Data Mining: Practical machine learning tools and techniques,” Morgan Kaufmann, 2nd Ed., 2005.               [8].Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, issue 1, pp. 1–127, 2009.               [9].Keerthi, Latha , Suhana K, ,Vidyarani U , “Data-Driven Sales Forecasting in Supermarkets with AI Techniques” Volume: 09 Issue: 09 | Sept – 2024 International Journal of Scientific Research in Engineering and Management (IJSREM)             [10].Chavali Saathvika Durga Abhinaya, Bellamkonda Lahari, Devarapalli Anjali, “Supermarket Sales Prediction Using Machine Learning” November 2023 EPRA International Journal of Research & Development (IJRD)             [11].Sh. Liu, A. Duffy, R. Whitfield and I. Boyle, “Integration of decision support systems to improve Decision Support Performance, Knowledge and Information Systems” Journal, vol. 22, Issue 3, pp. 261-286, March 2010.