# The Role of AI in Warehouse Digital Twins: Literature Review +
###### Abstract
Stemming from simulation as their core, DTs are expected to provide a fully connected and continuously evolving virtual replica of their physical counterparts, enabled by IoT technology that facilitates data analytics and simulation [10]. Moreover, DTs possess smart capabilities that will allow them to not only monitor changes in the warehouse but also promote them to enhance performance. Furthermore, digitalization in warehousing is not novel, yet it has garnered more attention in recent years, driven by industry 4.0. In the 1990s, the early adoption of Enterprise Resource Planning (ERP) systems in logistics and supply chain management initiated efforts to improve data availability in material handling [11]. As the journey towards digitalization progressed, the implementation of Warehouse Management Systems (WMS), Internet of Things (IoT), automation, and big data have played significant roles in streamlining and standardizing intralogistics operations. Consequently, intralogistics and warehouses now possess a wealth of data that is often underutilized by decision-makers but could serve as a solid foundation for implementing innovative AI applications. However, large-scale automation has only been achievable for a few prominent companies, such as Amazon and Google [12]. A survey conducted in 2022 revealed that 79% of warehouses use some warehouse management systems (WMS), and 16% have adopted a \"goods-to-person\" warehousing solution [13]. Nevertheless, the data in warehouses remains largely untamed and disorganized. Many companies worldwide use warehouse management systems inefficiently, collecting data but often leaving it unattended, untreated, and unused. AI-based decision-making methods offer the potential to further enhance the features of DTs by enabling them to analyze and interpret the data collected by IoT devices autonomously. AI algorithms can identify patterns in data that can be exploited to predict future values of a variable, especially when the variables are complex or impossible to calculate. AI has found many applications in the logistics field, including inventory optimization and production planning [14; 15]. In this way, advanced data analytics can be leveraged to transform DTs from mere data repositories into a source of knowledge or wisdom, driving optimized warehouse management. AI and DT are heavily reliant on data, and in recent years, warehouses and supply chains have emerged as significant data sources and data-driven processes. The advent of AI-assisted DT presents an opportunity to address this problem. By structuring data and serving as a unified source of truth and information, DTs can offer global oversight of warehousing activities, allowing for informed decision-making through data-driven insights. Consequently, implementing DT technology can help businesses transform chaotic and unwieldy data in their warehouses into valuable information that can drive improved performance. Having established the complementarity between AI and DTs in warehouse management, this paper aims to provide an in-depth analysis of the state-of-the-art by assessing the maturity of AI applications within the DT paradigm. Through a comprehensive review of the existing literature, we seek to address the following research questions:  
* What AI techniques are mostly used for warehouse management under the DT paradigm? * How is AI employed to ensure and elevate WDT functions? * What are the challenges and barriers to adopting WDT and AI in warehouses? By addressing these questions, this paper seeks to contribute to a better understanding of the potential and limitations of AI and DTs in warehouse management and to provide insights into how these technologies can be effectively integrated to drive operational excellence and competitive advantage in the digital age. The rest of this paper is structured as follows. Section 2 describes the methodology used for the systematic literature review and highlights the contribution of this study compared to existing ones.