Challenges like short product life cycles, new disruptive technologies and volatile markets place stress on today’s production systems [1,2]. To adapt quickly to changing market demands and customer requirements, there is an increasing need for flexibility and transformability of production on the one hand. On the other hand, productivity and delivery performance targets have to be met. Both conflicting target areas are only insufficiently addressed by the static nature of classical production systems [3]. This leads to increasing inefficiencies. Value stream management (VSMM) [4] has the goal to systematically transform a current state value stream to a desired target state. Its major tool for creating transparency and planning change is the value stream method (VSM) [5]. The VSM aims to identify the causes of waste throughout the value stream and eliminate them by implementing a target state [6]. The target state is designed with the aid of design principles and guidelines. [7] For instance, the design guidelines support the calculation of the customer cycle, the lead time, the design of the process connections and the required number of employees [7,8]. Finally, the developed target state is gradually realized by means of individual projects as part of an implementation plan [6].

However, the VSM is reaching its limits regarding the current challenges mentioned. Due to singular data recording on site, the VSM only provides a snapshot of value streams and thus does not take into account the dynamic development of production systems [9,10]. For this reason, there is a need for further development of the VSM to meet the current dynamics of production systems. According to one study, around 66% of lean experts believe that further development of the VSM using Industry 4.0 technologies is beneficial [2]. By merging the two approaches, the restrictions can be eliminated.

One promising Industry 4.0 technology related to the optimization of production processes is the Digital Twin (DT) [11,12]. DTs received specific attention in the context of digitalization and increased implementation of cyber-physical production systems (CPPS) where all entities can collect, generate or process data/information [13]. A DT is considered as an efficient way to accomplish the interconnection between physical and virtual spaces [14]. Within this understanding, the terms Digital Model (DM), Digital Shadow (DS) and DT are often used synonymously. However, the definitions differ in the level of data integration between the physical and digital object [11].

A DM is the digital representation of an existing physical object with no automated data exchange between the physical and digital object. It includes a comprehensive description of the physical object based on mathematical models or any other models of a physical object, which do not need an automatic data integration. The DS is an extension of the DM. For this, an automated one-way data flow between physical and digital object must exist. A change in the physical object determines a change in the digital object but not vice versa. If further, both data flow directions are fully automated, one might refer to it as a DT. In this case, the digital object represents the controlling instance of the physical object. A change in the physical object directly leads to a change in the digital object and vice versa [11]. Technical realization of the DT focuses on implementation for individual production processes, both in research and practice [11,14]. According to [15,16], an integration of the DT to represent an entire production system in digital space is promising as it links the existing production system with real-time data to a digital reflection. This allows us to eliminate the static character of a once created value stream map as dynamic changes of production systems are considered [15,16]. To the authors knowledge, the transfer and adaption of the DT concept to the complex interrelationships within a value stream to support the continuous improvement of it within VSMM has not been taken place yet. Therefore, this paper proposes a framework for the implementation of a DT at the value stream level, the so-called Digital Value Stream Twin (DVST). The DVST is defined as a comprehensive digital representation of a value stream. It includes the properties, conditions, and behaviors of a value stream through mathematical models and data. The virtual, dynamic model helps optimize the performance of a value stream. In VSMM, the DVST is used to model and present a holistic view of interrelationships and dependencies of individual production processes. The optimization of a value stream as a unit is the priority rather than the optimization of an individual production process.

3. Objectives and Definition of the Digital Value Stream Twin

• maintaining the current value stream design (reactive improvement)
• continuous improvement towards the target state (proactive improvement)