Investigation of Digital Value Stream Twins in Learning Factory Environments

In today's dynamic and technology-driven business landscape, the pursuit of operational excellence has become increasingly reliant on innovative approaches. This paper investigates the application of the Digital Twin (DT) concept in terms of value streams within learning factory environments to enhance operational optimization and transparency. Value Stream Management (VSM) is an approach which aims to optimize the value flow through an organization. It focuses on mapping, analyzing, and improving value streams, encompassing all activities required to deliver a product to customers. Derived from the VSM framework, a Digital Value Stream Twin (DVST) as representation of the learning factory is introduced, capturing real-time data and enabling a dynamic visualization, monitoring and control of the value stream performance. By integrating data analytics, the DVSTT evolves over time, adapting to changes in operational conditions and supporting the identification of optimization opportunities. The research methodology involves a case study conducted within a FESTO learning factory, which conceptually demonstrates the benefits of utilizing DVST to enhance decision-making, identify bottlenecks, and streamline processes. Findings reveal that integrating the DVST into a learning factory environment empowers organizations to experiment with process improvements before implementing changes in actual operations. Finally, an outlook on development potentials of the introduced DVST is given.