Lyrebird Optimization for Bidirectional Gated Recurrent Unit-Based Risk Prediction in Supply Chain Management

Abstract

The interconnectivity and globalization of today's world have made supply chains more complex than ever. Production, delivery, and profitability are increasingly vulnerable to unforeseen disruptions. This study introduces a novel approach to proactive risk prediction in supply chains using deep learning techniques. Inspired by the Lyrebird Optimization Algorithm (LOA), our proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU). BiGRUs excel at processing sequential data, such as supply chain metrics, due to their remarkable ability to capture temporal relationships within the data.

To enhance the learning process of BiGRUs from supply chain data, LOA—a bio-inspired algorithm—addresses hyper-parameter tuning. Initial data points collected include inventory levels, transportation records, supplier performance, and other external factors. We ensure the data is suitable for time-series analysis through cleaning techniques like imputation, normalization, and structuring. LOA fine-tunes the hyperparameters of the BiGRU, enabling it to better interpret the data and make accurate predictions about potential risks.

Proactively identifying these risks allows companies to strengthen their supply chain resilience by implementing appropriate mitigation strategies. By demonstrating the effectiveness of Lyrebird-optimized BiGRUs for proactive risk prediction, this study contributes to the field of supply chain risk management, ultimately fostering more resilient and adaptable supply networks.