Abstract :

Accurate Demand Forecasting Is A Fundamental Pillar Of Modern Supply Chain Management And Retail Optimization. Traditional Statistical Models, Such As ARIMA And Exponential Smoothing, Often Fail To Capture The Non-linear Complexities And Volatile Patterns Of Contemporary Global Markets. This Research Explores The Implementation Of Advanced Machine Learning (ML) Techniques To Enhance Product Demand Prediction Accuracy. By Leveraging Diverse Historical Datasets, Including Point-of-sale Transactions, Seasonal Trends, And External Economic Indicators, The Study Evaluates The Performance Of Supervised Learning Algorithms. Models Such As Random Forest, EXtreme Gradient Boosting (XGBoost), And Long Short-Term Memory (LSTM) Networks Are Rigorously Tested Against Baseline Statistical Methods. Particular Attention Is Given To The Integration Of Exogenous Variables, Such As Social Media Sentiment And Macroeconomic Shifts, Which Have Become Increasingly Relevant In The Post-2025 Digital Economy. The Methodology Involves Comprehensive Data Pre-processing, Including Feature Engineering For Seasonality And Lead-time Analysis. Empirical Results Demonstrate That Ensemble Methods And Deep Learning Architectures Significantly Reduce Mean Absolute Percentage Error (MAPE) Compared To Traditional Linear Models. Specifically, LSTM Models Exhibit Superior Performance In Handling Long-term Temporal Dependencies, While XGBoost Excels In High-dimensional Feature Spaces With Sparse Data. The Findings Suggest That ML-driven Forecasting Not Only Minimizes Inventory Carrying Costs But Also Reduces The Risk Of Stockouts And Waste. Furthermore, The Study Discusses The Scalability Of These Models Within Automated "Agentic AI" Ecosystems That Facilitate Real-time Decision-making. As Industries Move Toward Hyper-personalization, The Transition From Reactive To Proactive Demand Planning Becomes Essential. This Paper Concludes That A Hybrid Approach, Combining Domain Expertise With Automated ML Pipelines, Offers The Most Robust Framework For Future-proofing Supply Chains. The Research Provides A Scalable Architecture For Businesses To Achieve Operational Efficiency Through Datadriven Intelligence.

Published:

18-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1252-1263

Section:

Articles

License:

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

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