Abstract :

Time Series Forecasting And Modelling Of Food Demand Supply Chain Based On Regressors Analysis Presents An Intelligent Predictive Framework For Analyzing And Forecasting Food Demand Within Supply Chain Management Systems Using Time Series Modelling And Regression-based Analytical Techniques. The Study Focuses On Identifying The Influence Of Various Regressors Such As Seasonal Variations, Population Growth, Consumer Purchasing Behavior, Climate Conditions, Transportation Costs, And Market Trends On Food Demand And Supply Fluctuations. By Applying Machine Learning Algorithms, Statistical Forecasting Models, And Regression Analysis To Historical Supply Chain Data, The Proposed System Aims To Improve Demand Prediction Accuracy, Reduce Food Wastage, Optimize Inventory Management, And Enhance Distribution Efficiency. The Framework Supports Real-time Decision-making For Suppliers, Retailers, And Policymakers By Providing Reliable Forecasts That Help Maintain Supply Chain Stability And Ensure Food Availability. Experimental Results Demonstrate That Integrating Regressor Analysis With Time Series Forecasting Significantly Improves Prediction Performance And Contributes To The Development Of Sustainable And Efficient Food Supply Chain Management Systems.

Published:

04-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

107-114

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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