The Value Stream Method (VSM) is widely used in manufacturing to analyze and redesign value streams. The aim is to improve processes, reduce waste and create a thorough product flow. Despite having many benefits, VSM also comes with disadvantages regarding modern dynamic production environments. It fails to meet the requirement of providing reliable information for a realistic Value Stream Design (VSD) followed by targeted improvement activities. As a result, the VSM is usually subject to uncertainty and relies on expert knowledge. Digitalization, on the other hand, is leading to an increasing availability of production data. The use of data has the potential to support the VSM with targeted data preparation. In this regard, the concept of Digital Twin (DT) offers the capability of providing the required database to systematically collect and condense this data. This paper provides a framework for the Digital Value Stream Twin (DVST). In addition, requirements for the implementation of a DVST in practice will be elaborated.

The value stream method, a key tool in industry to analyze and visualize value streams in production, aims to holistically optimize process steps, reduce waste, and achieve continuous material flow. However, this method primarily relies on data from a single on-site inspection, which is subjective and represents just a snapshot of the process. This limitation can lead to uncertainty and potentially incorrect decisions, especially in industries producing customer-specific products. The increasing digitization in production offers a solution to this limitation by supporting the method through data provision. The concept of the digital shadow emerges as a key tool that systematically captures, processes, and integrates necessary data into a model to enhance traditional value stream mapping. This addresses the method’s shortcomings, especially in heterogeneous IT landscapes and complex value streams. To effectively implement the digital shadow this study identifies concepts of digital shadows and their key components and evaluates them for their relevance in industrial environments using an expert study. Based on the results, a design model is defined. This model entails guidelines to support companies with the practical implementation of the digital shadow of a value stream. Lastly, the model is evaluated on a realistic value stream in a learning factory.

Um in der Dynamik heutiger Produktionssysteme angemessene Entscheidungen treffen zu können, sind aktuelle Informationen zum Status eines Wertstroms notwendig. Die Datenmengen in Unternehmen führen zu einer Dynamisierung der Produktionsabläufe und zwingen Wertstrommanager:innen zu kurzfristigen und zielgerichteten Anpassungen der Wertströme. Aufgrund des statischen Projektcharakters der konventionellen Wertstrommethode ist eine Weiterentwicklung zu einem dynamischen Managementansatz notwendig. Um diesen digital unterstützen zu können, werden die wichtigsten Aufgaben der Wertstrommanager:innen mithilfe eines Expert:innen-Interviews ermittelt und systematisiert.