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Analysis Study Research Paper | Computer Science and Information Technology | India | Volume 14 Issue 3, March 2025 | Rating: 4.8 / 10
Forecasting for Grocery Store Perishable Food Products Using Big Data Analytics
Deeksha Kachhwaha, Vani Agrawal
Abstract: This research tackles the challenge of perishable food product management in grocery stores through Big Data Analytics. It employs the Moving Average algorithm and Linear Regression for sales trend prediction and inventory optimization, implemented using Python. The Moving Average algorithm smoothens sales data fluctuations, aiding trend identification, while Linear Regression predicts future sales patterns based on historical data. A sample dataset with daily sales is used to demonstrate the techniques, visually presenting actual sales data alongside Moving Average and Linear Regression forecasts. The study aims to enhance forecasting accuracy, minimize waste, and improve inventory management efficiency in grocery stores. By harnessing Big Data Analytics, it offers insights for optimizing perishable goods supply chain operations, presenting a practical, data - driven approach for the retail sector. The forecasting models' flexibility and adaptability to diverse datasets hold promise for revolutionizing perishable food product management in retail.
Keywords: Perishable food management, Grocery retail, Sales forecasting, Moving Average algorithm, Supply chain operation
Edition: Volume 14 Issue 3, March 2025,
Pages: 111 - 117