Utilizing Attention Based Machine Learning Models for Improved Demand Forecasting in Supply Chains

The goal of the suggested approach is to offer a reliable way to increase supply chains' accuracy in demand forecasting, which will enhance inventory control and operational efficiency. The use of gradient boosting machines (GBM) with attention weights for supply chain demand forecasting is investigated in this study. The attention mechanism improves its capacity to concentrate on the most pertinent data points, such as seasonal trends, promotions, and historical sales patterns. GBM, an ensemble learning technique, is selected for its excellent performance and interpretability. In addition, the incorporation of attention mechanisms with GBM illustrates how the state-of-the-art ML techniques can be adapted to specific domain problems to create innovation in predictive analytics. The study makes use of Kaggle's “Forecasts for Product Demand” dataset, which contains historical sales information for a variety of products from several shops. In order to prioritize important aspects in forecasting future demand while taking non-linear relationships and temporal dependencies into consideration, the model incorporates attention weights. In this framework, attention-based GBM focuses on high-impact features like seasonality and promotions, enhancing demand prediction. Performance measurements like Mean Absolute Error (MAE) of 0.18 and a Root Mean Squared Error (RMSE) of 0.2 with a registered R-squared value of 0.9 in the experiment implemented in Python. The Attention-based GBM outperforms Random Forest and SVM, achieving superior accuracy and minimal prediction error. This study also opens the door for future applications in other commercial sectors by demonstrating the promise of machine learning approaches in tackling practical issues in supply chain demand forecasting.