Abstract:

Predictive supply chain risk management involves the application of advanced analytical techniques and technologies, such as artificial intelligence (AI) and machine learning (ML). These approaches offer the ability to analyse data in real time, identify patterns and make predictions that help companies to prepare for potential disruptions and take appropriate action before they lead to major problems. In a world with more and more complexity and volatility of global markets, the management of supply chain risks is a key strategic issue for companies. To ensure a robust and stable supply chain, potential risks must not only be identified and understood, they also have to be predicted. In this context, predictive risk management in the supply chain is becoming the focus of current research and practical applications. This paper aims to comprehensively examine the current state of research and practice in the field of predictive supply chain risk management in order to provide insights into the latest developments, challenges and opportunities.