Deep learning hybrid models for effective supply chain risk management: mitigating uncertainty while enhancing demand prediction

In today's rapidly evolving business landscape, effective supply chain management (SCM) is crucial for achieving success. Accurately predicting product demand is a significant challenge for companies, impacting customer satisfaction, inventory optimization, cost reduction, and operational efficiency. This study focuses on demand forecasting within intelligent supply chains (SCs) and supply chain risk management (SCRM), aiming to enhance overall SC efficiency and mitigate risks, highlighting the use of deep learning hybrid and singles models to address SCRM challenges, specifically in mitigating uncertainty and improving demand prediction accuracy. Our research paper investigates predictive modeling techniques for demand forecasting within the automotive sector. Specifically, we assess the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and a hybrid RNN-ANN model with Gradient Boosting (GB). Through meticulous analysis and evaluation, we demonstrate the superior predictive accuracy of the hybrid model compared to individual models. The results indicate consistent outperformance of the hybrid model, as evidenced by lower Mean Absolute Error (MAE) and Mean Squared Error (MSE) values across electric and thermal product categories. This research aims to provide valuable insights and practical tools for businesses to refine their demand prediction processes. By addressing demand uncertainty, organizations can streamline their SCs, minimize costs, and establish a responsive and adaptable framework for sustainable growth.