Article
DEMAND FORECASTING USING ML
Demand forecasting is a critical component in supply chain management and business planning, aiming to accurately predict future customer demand for products or services. Traditional forecasting methods, such as moving averages and ARIMA models, often fall short when dealing with large-scale, non-linear, and multi-factor datasets. In recent years, machine learning (ML) has emerged as a powerful approach to enhance the accuracy and adaptability of demand forecasting models. This study explores the application of machine learning techniques—ranging from classical algorithms like linear regression and decision trees to advanced methods such as ensemble models (e.g., Random Forest, XGBoost) and deep learning architectures (e.g., LSTM networks)—to forecast demand using historical sales data, seasonal trends, promotional effects, and external variables such as weather and holidays. By engineering relevant features like time lags, rolling statistics, and categorical encodings, the models are trained and evaluated using appropriate time-series validation strategies. Performance is measured using metrics such as MAE, RMSE, and MAPE to ensure robust comparisons. The results demonstrate that ML models significantly outperform traditional forecasting methods in handling complex patterns and adapting to dynamic market behavior. The study also emphasizes the importance of feature selection, data preprocessing, and model retraining pipelines in achieving scalable and accurate demand forecasting systems. Overall, this work provides a comprehensive framework for deploying machine learning models in real-world forecasting applications, contributing to improved inventory planning, reduced waste, and better customer satisfaction.
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