Advanced Supply Chain Management Using Adaptive Serial Cascaded Autoencoder with LSTM and Multi-Layered Perceptron Framework

Supply chain management is essential for businesses to handle uncertainties, maintain efficiency, and stay competitive. Financial risks can arise from various internal and external sources, impacting different supply chain stages. Companies that effectively manage these risks gain a deeper understanding of their procurement activities and implement strategies to mitigate financial threats. This paper explores financial risk assessment in supply chain management using advanced deep learning techniques on big data. The Adaptive Serial Cascaded Autoencoder (ASCA), combined with Long Short-Term Memory (LSTM) and Multi-Layered Perceptron (MLP), is used to evaluate financial risks. A data transformation process is used to clean and prepare financial data for analysis. Additionally, Sandpiper Galactic Swarm Optimization (SGSO) is employed to optimize the deep learning model’s performance. The SGSO-ASCALSMLP-based financial risk prediction model demonstrated superior accuracy compared to traditional methods. It outperformed GRU (gated recurrent unit)-ASCALSMLP by 3.03%, MLP-ASCALSMLP by 7.22%, AE-LSTM-ASCALSMLP by 10.7%, and AE-LSTM-MLP-ASCALSMLP by 10.9% based on F1-score performance. The SGSO-ASCALSMLP model is highly efficient in predicting financial risks, outperforming conventional prediction techniques and heuristic algorithms, making it a promising approach for enhancing financial risk management in supply chain networks.