# AI Applications in Supply Chain Management: A Survey
## 3 Artificial Intelligence  
AI is defined as intelligence through which machines can display and emulate all human cognitive functions, such as problem solving and decision making, to the benefit of organizational optimization and automation. The presence of AI is evident in the fields of agricultural management, education, healthcare, fashion, e-commerce, gaming and the military [1]. The roots of AI go back to the 1950s, and its key techniques include Machine Learning (ML), Deep Learning (DL), Neural Networks (NNs), Natural Language Processing (NLP), Computer Vision (CV), Knowledge Representation and Reasoning (KR&R), Recommender Systems (RSs) and optimization (OP) [3; 52]. Many of these techniques incorporate simulation as a fundamental approach to modeling, testing and optimizing AI-driven decisions, particularly in complex and dynamic supply chain environments. A branch of AI is Generative AI (GEN AI), which can produce a variety of different forms of content, namely, text, graphics, audio, video or other data forms, and leverages Machine Learning models [53]. A prime example of Generative AI is ChatGPT (Version 4.0) which offers an adequate number of benefits but also various threats and challenges to SCM [54; 55]. The IoT has the leading role of generating data for AI to analyze. Integrating the IoT and AI within the SCM context provides notable advancements in supply chain  
Figure 1: Supply chain management framework: inputs, processes and outputs.  
transparency, agility and overall functionality [56]. Artificial Intelligence of Things, which refers to utilizing the IoT to execute smart tasks with the aid of AI integration, is one of these upcoming developments that can convert a complex supply chain into a unified process [57]. Figure 2 depicts various AI manifestations as they have been AI-generated by the Microsoft Designer program (Version 19041.0).  
Nowadays, it is more than evident that AI leads the way in revolutionizing every aspect of SCM and is employed to make educated predictions on demand and transportation routes, suggest innovative solutions and optimize operations costs [58]. Moreover, the need to make ends meet in a highly competitive environment sets AI as the main force to succeed. Even when faced with a global pandemic crisis, such as COVID-19, AI is a crucial factor that helps management achieve its goals [59]. Furthermore, although there is a hesitation in managers to utilize AI [59], neurosymbolic AI is explicitly suitable to give explanations for every decision based on AI models [60].  
Managers' perceptions and intentions in utilizing AI in decision making are still in a contemplating phase, even though the COVID-19 crisis has strengthened business operations through AI [59]. AI-driven SCM optimization delivers a range of benefits that markedly enhance inventory management, improve demand projection, optimize logistics, increase efficiency and productivity and upgrade decision-making policies [61]. The adoption of AI in supply chain decision making is also triggered by the Environmental, Social and Governance framework. These triggers are product waste reduction and greenhouse gas emission reduction, product security and quality, and agile and lean practices, respectively [62].