# A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems
## 1 Introduction  
All manufacturing systems, and thus also a discrete manufacturing system (where distinct items are manufactured [1]) should be more agile, flexible, and sustainable to cope with the dynamic changes in the manufacturing environment and market demands, while maintaining the quality of manufactured products [2, 3, 4]. Recently, this has been addressed by the concept of data-driven factories, which are characterized by the following features: agility, learning capability, and human-oriented manufacturing [5]. Often referred to as smart-manufacturing systems, these systems aim to convert the data collected throughout the entire manufacturing process into manufacturing intelligence. In turn, manufacturing intelligence enables knowledge-driven decisions that positively impact the operation of the entire manufacturing system [6, 7]. The data-driven factory represents a leap forward from the more traditional automation to a fully connected and flexible system [8, 9]. To develop the data-driven digital factory, digital models are a key enabling technology. A digital model is a simplified representation of a real system and process, with the characteristics which reflect the properties and explain the behavior of the real manufacturing system. It represents a partial description of the real system, because it is made for a specific purpose and therefore cannot include all the details of the real system. Figure 1 shows the connections between a real manufacturing system and its digital model. To synchronize both, data exchange should be performed in both directions. Digital models must keep up with the changes in the real system, and change accordingly over time, for which it is essential to structure the data flow from the real environment to the model and vice versa. When a digital model and a real system are bi-directionally connected by a feedback control loop, the model is usually referred to as a digital twin, which was first mentioned in 2003 [10]. However, the concept of a digital twin was envisioned well before the technology necessary for its implementation became available. Various definitions of a digital twin can be found in literature [11; 12], one of which was proposed by NASA in 2010. It defines the digital twin as an ultra-realistic, high-scaling simulation model that uses the best-available physical models, with data being collected throughout the whole lifecycle of the system [13; 14]. For the purposes of our research, we used the following definitions [15]. The term _digital model_ is used for a model where there is no automated data exchange between the physical world and the digital world, and in which all the data exchange is conducted manually. The second term, _digital shadow_, is used for an automated, one-way data flow from the physical world to the digital world, but not vice versa. The _digital twin_ is used when a physical object and its digital world are connected in both directions to perform an automated two-way data flow. Data flows are essential in the development of digital twins. By creating a link between the real system and its digital twin, a data-driven digital twin is created; a digital twin that evolves along with its physical counterpart based on the actual data acquired from the real system. In manufacturing, there are large amounts of structured, semi-structured and unstructured data generated during a product's lifecycle. The data is collected automatically in real time and represents the basis for the development of the digital twin [16; 17]. The problem with planning and the development of a digital twin is that there is a variety of different data sources, types, and structures. For example, data sources include data from sensors on machines, billboards, work plans, operating schedules, data from a Measurement Execution System (MES), Enterprise Resource Planning (ERP), etc. These data take various forms such as graphs, tabular records, collected data from sensors and they are on different scales in terms of both time and volume.