# Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics
## 1 Introduction  
The cornerstone of an organization is a working logistics supply chain. Organizations whose core industries are based on internal and external logistics development play a crucial role in ensuring that the organization's core business will continue without halting [1]. However, it is essential to outline the central purpose of logistics quite briefly, transporting everything from point X to Y. Based on the particular mission at hand, the methods of doing so may differ considerably. These activities are becoming more challenging for many companies as global economies impose higher consumer customization expectations. The items produced and the manufacturing lines themselves entail more complex technologies. It is becoming increasingly challenging to identify alternatives to the core businesses' technical problems [2, 3]. The method of obtaining information about the supply chain's internal workings is becoming more complicated as distribution supply chains grow more dynamic. Any company in which the supply chain has a critical function can quickly become a concern since lacklustre accountability and exposure of the supply chain can throw clouds on outdated processes that may otherwise be rectified [4, 5]. Ensuring maximum control ofthe supply chain not only grants access to a variety of evidence that can act as a foundation for business choices. It also offers greater capacity to adapt rapidly to developments and make fast operating decisions in real-time based on changes in demand or production [6; 7]. Although the supply chain's visibility is necessary, achieving and retaining full visibility through a supply chain network spanning the globe is a challenge bordering on the impossible without using recent digital technology developments [8; 9]. The automotive business and culture as a whole are on the edge of what is sometimes referred to as \"Industry 4.0\" or the fourth industrial revolution [7; 10]. Rapid advancement and integration to emerging technology innovations and mindsets are the core aspects of Industry 4.0. Many new technologies increasingly find further adoption and different uses within our current industrial framework [11; 12]. The approaches and ideas in the context of \"Industry 4.0\" are situated at the interface of the disciplines of electrical engineering, business administration, computer science, business and information systems engineering, mechanical engineering, and the participating segments. The illustrated parts of Industry 4.0 result in general fields of activity, which are of particular interest for business and information systems engineering. The increasing digitalization of all manufacturing and manufacturing-supporting tools results in registering a growing amount of actor and sensor data, supporting functions of control and analysis [13]. Digital processes evolve due to the likewise increased networking of technical components. In conjunction with the increase in the digitalization of produced goods and services, they lead to completely digitalized environments [10; 14]. Those are, in turn, driving forces for new technologies such as simulation, digital protection or virtual resp. Many of the innovations that make up the fourth industrial revolution are gradually utilizing innovative development, big data analytics algorithms, and sophisticated human-machine interfaces, to name only a few [15]. Four main categories can be roughly grouped into these different technologies [16]: (1) Cyber-physical constructs. (2) Platforms for the Internet of Things (IoT). (3) Computing cognitively. (4). Cloud computing. A fascinating field of technical research within the industrial framework of Industry 4.0 is that of Digital Twins [17]. A Digital Twins is a form of cyber-physical device that uses numerous IoT sensors and produces a high-fidelity visual image of a physical asset. The abundance of data obtained by the Digital Twins is then aggregated and analyzed using machine learning algorithms to promote strategic and organizational decision-making [18].