# AI Applications in Supply Chain Management: A Survey
## 3 Artificial Intelligence
### Resilience and Risk  
Supply chain resilience is improved with the help of AI in turmoil times. Moreover, firm performance is related to AI and supply chain resilience [74]. Especially, AI improves transparency by employing continuous monitoring, handling last-mile delivery and tailor-made demands, and lessens the negative impacts of global crises, like COVID-19 [10; 11]. A quality measure of resilience is also important, not just a quantity one. While it is crucial to understand the disruption and recovery actions a firm must take to roll back to normal proceedings, it is even more intuitive to boost the quality of network health by investing in prevention policies [75]. There is empirical evidence that the level of visibility maintained and shared with all supply chain partners defines the impact of disruption events. Thus, AI fosters resilience in the supply chain [76].  
Artificial Neural Networks are used to recognize and lessen risks in everyday operations in supply chains. These risk cases are disruptions in supply, variations in demands and shifts in market conditions [77]. Once again, analysis of historical data, patterns and trends can recognize and create risk management policies that alleviate disruptions' effects and fortify resilience [64]. The integration of deep reinforcement learning and predictive analysis further enhance decision-making processes in real time and anticipate disruptions [78]. The most used AI technique in supply chain resilience is Bayesian Networks, as a thorough approach to evaluating risk, analyzing uncertainty and deciding amid structural dynamics [79]. There is concrete evidence demonstrating that the data processing functionalities of AI impact supply chain performance by enhancing supply chain resilience overall [80]. The numerous successive crises and supply chain instabilities attest to the need for further utilization of AI to bolster resilience.  
Additionally, resilience necessitates a forward-thinking approach to risk management, namely, the identification of potential threats, the assessment of likelihood and impact and the implementation of prevention and/or mitigation policies [81]. Risks in SCM can be categorized into supply and demand risk, process and control risk, and environmental and information risk [14] and are quantified as the multiplication of their likelihood and effects. Predictive risk management is based on AI methods and instruments, like ensemble learning and Neural Networks to anticipate risks and take actions to mitigate them [65]. Specific AI techniques, including fuzzy logic programming, Machine Learning, Big Data and agent-based systems, could be used to promote resilience in supply chain management [82]. A proactive approach to risk reduction in SCM can be achieved with Generative AI. The continual analysis of specific supplier performance indicators, data obtained from markets and IoT devices, and AI algorithms can identify potential emerging threats and facilitate documented decision making [71]. Deep Convolutional Neural Networks (DCNNs) demonstrate remarkable efficiency stemming from calculating intricate and nonlinear correlations among variables. The utilization of DCNNs bolsters predictability and robustness in the global supply chain [83]. Large Language Models have demonstrated their worth as essential assets for improving risk assessment and mitigation by analyzing various data sources and delivering actionable intelligence [68].  
Table 1 summarizes key SCM activities with their corresponding AI applications.