Deep Learning-Based Solutions for Business Enterprise Demand Prediction and Analytics
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Sales forecasting is crucial to business strategy, which affects inventory, finance and customer satisfaction. The conventional demand forecasting approaches cannot cope with complicated and dynamic data and with new changes. However, the present paper explores how deep learning (DL) can enhance the accuracy of sales forecasting in various industries. It is analyzed using a dataset of retail sales that had sales data, product categories and consumer profiles. Preprocessing and feature engineering of the data were conducted before being used in the creation of CNN and BiLSTM architecture-based forecasting models. Conventional methods like ARIMA, LR, were used to assess and compare the models. As indicated by the findings, deep learning methods worked much better than conventional models; that is, CNN was slightly better than the BiLSTM in error reduction. The two models generated a high R2 value of 0.94, and both models had a good predictive ability, though the CNN model had lower prediction error (RMSE 239.10, MAE 157.10) than BiLSTM (RMSE 242.52, MAE 154.58). Finally, the findings demonstrate that deep learning techniques are the best approach to demand planning and inventory management in the real-world retail sector, as they yield more reliable and valid sales predictions.
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