# AI Applications in Supply Chain Management: A Survey
## 4 Future Trends, Challenges, Threats  
The future of AI is promising across all domains of human endeavor and innovation [1], garnering significant academic attention [33,84]. Significant disruptions, such as COVID-19, have commenced a discussion on reversing outsourcing in supply chains and further developing local and in-house manufacturing, utilizing the Manufacturing-as-a-Service  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SCM Activities** & **AI Apps** \\ \hline Customer relationship management & Agent-based models, chatbots and virtual assistants \\ \hline Inventory management & Machine Learning, robots, drones, agent-based models and Large Language Models \\ \hline Transportation networks & Network theory, graph algorithms, genetic algorithms, ant colony optimization and reinforcement learning \\ \hline Procurement & Agent-based models, process automation and Generative AI \\ \hline Demand forecasting & Machine Learning, support vector machines, Neural Networks, decision trees, Deep Neural Networks, data mining, fuzzy models, sentiment analysis and Large Language Models \\ \hline Resilience & Artificial Neural Networks, deep reinforcement learning and Bayesian Networks \\ \hline Risk & Ensemble learning, Neural Networks, fuzzy logic programming, Machine Learning, Big Data, agent-based systems, Generative AI, Deep Convolutional Neural Networks and Large Language Models \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key SCM activities and their corresponding AI apps.  
model (MaaS) [4]. A shift towards human-centered AI in SCM is expected to manifest in the form of training and specializing human workforce while boosting AI familiarity among the general population [58]. There is a clear trend in utilizing AI applications to tackle both traditional and fresh problems [26].  
Nowadays, AI faces many challenges, namely, data privacy [68] and algorithmic bias [85], cybersecurity [68] and ethics [86], the absence of transparency and documentation, the lack of specialized professionals [33] and implementation challenges [1; 65]. Also, there is a lack of evidence to measure its Return on Investment (ROI) in supply management enterprises, thus making it difficult to appraise its value [58]. Managers' attitudes toward adopting AI for decision making remain in a deliberative stage, despite the COVID-19 crisis highlighting AI's potential to enhance business operations [59]. Additional challenges for AI in SCM are strong dependence on computer software, difficult implementation and inapplicability in cross-function and cross-border supply chain decision frameworks [24]. On top of that, companies must advance and elevate their degree of digital maturity in their organizational culture context before trying to implement AI solutions in their SCM [87]. Over and above technical challenges, there are organizational hurdles to overcome, namely, redefining processes, roles and responsibilities [85].  
The main threats of AI presence in human societies are the disruption of the current job landscape, use for nefarious purposes, weaponization and the increase in inequalities [1].  
Industry 4.0 buzz is not consistent with companies' readiness to adopt its technologies, since they lack the expertise, specifically tied to their existent business domain [23]. Managers should be cautious and not hold lofty expectations [88] in regards to the implications of AI concerning performance, since organizational features are also critical [22]. Besides AI in SCM, Big Data, the IoT, advanced Natural Language Processing, blockchain [89], robotics, autonomous vehicles and drones are all crucial fields to explore scientifically for refining even more the management of the supply chain [52; 90].