Abstract 

Today, the widening and complexity of supply chains and the fact that risks cause serious disruptions have caused academicians and practitioners to show interest in studies within the scope of supply chain risk management. In this framework, supply chain risk management is becoming increasingly important and processes are being managed with many new techniques. Recent research shows that artificial intelligence techniques are increasingly used in supply chain risk management. As a result of the use of artificial intelligence in supply chain risk management, it has been observed that changes occur especially in functioning and dynamics. Options such as driverless vehicles that emerged with artificial intelligence, robots used in storage and shelves, and the easy use of large data in the system ensure that errors in supply chains are minimized. Within the scope of supply chain risk management, it is possible for businesses to identify and evaluate risks and determine appropriate risk reduction strategies with the help of artificial intelligence techniques. In this study, it is aimed to examine in detail the artificial intelligence techniques used in supply chain risk management. In this context, expert systems, artificial neural networks, fuzzy logic, genetic algorithm and machine learning techniques are examined in detail. Then, these techniques were compared with each other and evaluations were made about which artificial intelligence technique would be more effective in which problems. In the conclusion part, artificial intelligence techniques that should be applied according to the problem types and some suggestions for future studies are presented.

Keywords

Supply Chains, Supply Chain Risk Management, Artificial Intelligence Techniques