# AI Applications in Supply Chain Management: A Survey
## 1 Introduction  
In the midst of the fourth Industrial Revolution, where transformative technologies such as Artificial Intelligence (AI), advanced robotics and Internet of Things (IoT) are reshaping production and business overall, the lines between humans and machines are becoming less distinct. And what is more, Industry 5.0 brings humans and technology even closer [1,2]. In general, AI involves the use of computers to perform tasks that are typically run by humans, such as learning, understanding, recognizing, reasoning, and adapting [3]. The economic contribution of AI technologies will reach around USD 13 trillion by 2030 [4], while the supply chain management market is expected to surpass USD 62.2 billion by the same year [5]. Considering the increasing role of AI in supply chain management (SCM), this study seeks to investigate its primary applications, benefits and challenges. To structure this investigation, we pose the following research question: How are AI techniques being applied in key SCM processes, and what are the emerging trends and challenges associated with their adoption? Supply chain management (SCM) is the monitoring and optimization of the production and distribution of products and services of a company [6]. Supply chains are cause for a lot of concern for and are the focus of many enterprises, governments and consumers and gain broader media coverage [7]. In today's major competitive environment, a cloud supply chain based on Industry 4.0 technologies and digital platforms transforms into the \"supply chain-as-a-service\" paradigm [8,9]. The benefits of employing AI in SCMare resilience improvement [10; 11; 12] and risk management [13; 14; 15], end-to-end visibility and transparency [2; 16; 17], information and process resilience [18], the real-time tracking of goods in every part of the supply chain [19], sustainability [20], and food safety and public health [21]. It is important to note that all these features are possible through the integration of the digital twin concept into SCM processes, leveraging real-time data and AI-based algorithms to interpret them [9]. Past academic work concerning AI in SCM emphasized the need to lessen the current hype [22]; brought to the surface the lack of know-how in organizations [23] and the lack of understanding in ordinary decision makers [24]; proposed an AI taxonomy relevant to SCM [4]; described the most frequent AI techniques and SCM subfields [25; 26]; utilized a bibliometric review to trace the evolution of AI research in SCM [2]; critically reviewed the drivers, practices, benefits, barriers and consequences of AI adoption [27; 28; 29]; proposed an AI integration framework [30]; and reviewed the effect of AI on operational efficiency, strategic innovation and sustainability [31]. Moreover, the following studies offered a practical framework of AI applications in SCM [32], reviewed the role of AI in the supply chain analytics [33; 34], examined the organizational and behavioral factors that promote AI adoption in supply chain [35], introduced a research framework for AI in supply chain resilience [36] and explored the relationship between AI applications and supply chain concentration [37]. Finally, these studies investigated the potential of AI in supply chain visibility [38], evaluated the benefits and challenges of AI in the agricultural supply chain [39], examined the relationship between AI and supply chain finance [40; 41] and proposed specific officer roles in overseeing AI utilization in SCM [42]. The scope of this research study covers a thorough analysis of AI applications in SCM, with a focus on the key processes of the chain. Although our paper encompasses a broad and representative range of studies, it is not intended to be an exhaustive review of all existing research, since our objective is to present relevant studies that emphasize the integration of AI techniques in SCM, focusing on their practical applications and emerging trends. Chapter Two and Three cover relevant information on supply chain and AI, respectively.