Advanced International Journal for Research

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Volume 7 Issue 2
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AI Driven Supply Chain Optimization for Deep Demand Forecasting in Grocery Retail

Author(s) Mr. Vakkala Venusai, Mr. Gurram Sujankumar, Mr. Vemula Kumarswamy, Mr. Kota Lakshmi Narahari Reddy, Dr. Geetha D Devanagavi
Country India
Abstract Demand forecasting plays an important role for making the retail supply chains more sustainable and efficient, importantly in the grocery sector. Groceries are the things that does not last for the long time and the buying habits of people also changes a lot, so predicting or forecasting what they will buy is tricky. Recent studies show that traditional methods used for forecasting often does not work well when handling with the unpredictable and the complex sales trends [1]. It uses the advanced machine learning, deep learning techniques and the model is tested using actual Walmart sales data that includes the sales history as well as information about promotions, holidays, and economic conditions, which helped in making the results more accurate. Different forecasting methods are tested including the basic time series approaches and a machine learning model called XGBoost [2]. To make the model much better, a deep learning model was created which uses the LSTM networks along with Self Attention mechanisms, inspired by the recent research on the attention-based predictions [3]. The results show that these attention enhanced LSTM is much better for making the accurate predictions than the usual statistical methods. This model helps in reduction of the stock wastage due to expiry and maintaining the required stock to avoid the loss of sales. It allows for making the smarter decisions based on the data. This leads to the most efficient operations and less waste in the retail sector [5].
Keywords Demand Forecasting, Supply Chain Optimization, LSTM, Self Attention, XGBoost, Retail Analytics.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-03-05
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