## 1 Introduction
These challenges are particularly evident in warehouse management, where the constant flux in inbounds logistics, internal warehouse operations, and outbound logistics of bespoke components and materials demand adaptable solutions. To manage this complexity, digital twin (DT) technology, a virtual replica of a physical asset or system, has already emerged as a promising tool for warehouses [11], and research analysis and implementation are addressing ETO challenges [12; 13]. By integrating real-time data from IoT devices with BIM and AI, recent research has highlighted the potential of DTs in various industrial contexts, including manufacturing, construction, and logistics. Studies have demonstrated the benefits of DTs in streamlining production processes [14; 15], improving supply chain visibility [12; 16], and enhancing predictive maintenance [17; 18]. However, the specific challenges of ETO manufacturing, such as the high variability in product manufacturing and demand fluctuations, necessitate tailored DT solutions and open a space for deeper investigation. This is also interesting in the context of providing new software to improve the very mature Warehouse Management System (WMS). Indeed, DT development can evolve the WMS through the integration of BIM information and IoT data in manufacturing, highlighting the potential for improved information management and collaboration in asset management as well in operations support [19; 20]; in the same way, the integration of AI capabilities to analyze complex warehouse data and generate actionable insights remains a nascent area of research which can exploit the data set fusion guaranteed by DT deployment [10; 11]. Additionally, the role of DTs in enhancing safety protocols within ETO warehouses has received limited attention. Despite the research conducted, DT application in ETO environments remains relatively unexplored and this paper aims to contribute to this research field by presenting ways of understanding and designing the DT implementation conducted within the IRISproject [21]; this research was funded by the European Union's Horizon 2020 research and innovation program within the framework of the Change2 Twin (C2T) [22] project's cascade fundings (grant agreement No 951956). The DT for a warehouse in the ETO model is defined for the warehouse of Focchi S.p.A., an ETO manufacturer of high-technological prefabricated building envelopes. The project aims to develop a DT that leverages the IoT, BIM, and AI to optimize warehouse operations and enhance worker safety. By analyzing Focchi's specific production processes and user requirements, this research seeks to uncover the opportunities and challenges associated with DT adoption in ETO manufacturing. The findings will inform the development of tailored DT solutions that can improve efficiency, safety, and ultimately business intelligence in warehouse management. This research evaluates how DTs can offer unprecedented visibility into warehouse operations, facilitating data-driven decision-making, process optimization, and risk mitigation to improve safety measures. This research allows for the support of the design implementation of a digital twin with technologies such as IoT and AI, demonstrating the potential of the solution implementation in comparison with conventional on-the-market products for warehouse management for engineer-to-manufacturing models. This research allows the evaluation of the effectiveness of DT technology in optimizing warehouse management within ETO environments, highlighting the benefits of a DT by integrating IoT, AI, and BIM for real-time visualization, predictive production planning, and data-driven decision-making.