Digital Twins represent a powerful tool for transforming production and logistics towards Industry 4.0. They mirror physical assets in the digital world, enriching them with additional capabilities and features such as decision-making or lifecycle management. Due to the diverse possibilities associated with the Digital Twin, their design and implementation are also wide-ranging. This paper aims to contribute to the formalization and standardization of the description of Digital Twins. It presents a method for evaluating them through their lifecycle, from design to operation. The paper is based on an overview of their potential functionalities and properties with ranked stages of development. This method allows for an application-specific evaluation of Digital Twins and describes how they can be improved to suit the application better. The maturity model development follows the procedure for developing maturity models for IT management. Relevant capabilities and features were identified with a systematic literature review following the PRISMA guidelines. The results of this review were ranked and categorized and constitute the core of the maturity model, which was validated on five use-cases from different domains in production and logistics. The maturity model assesses Digita Twins in seven categories (context, data, computing capabilities, model, integration, control, human-machine interface) with 31 ranked characteristics. It evaluates existing solutions for potential improvements for a given application or the transfer to a new use-case. The resulting method and a supplementary web service present a generalized model for the evaluation of Digital Twins. Based on a description of a potential application, this is the first step towards a systematic evaluation, improving the structured development of such applications

Due to the increasing complexity of contemporary production scheduling problems, it is generally not possible to calculate nearly optimal production schedules in an acceptable amount of time. Hence, normally, dispatching rules are used to determine the job sequences. However, the selection of suitable dispatching rules is not a trivial task and depends on the relevant key performance indicators. Moreover, the suitability of dispatching rules changes over time because of the stochastic and dynamic nature of manufacturing systems. This paper proposes an adaptive simulation-based optimization approach to select individual dispatching rules for production control. The paper's contribution is two-fold. First, it shows that the proposed approach improves the performance compared to benchmark approaches in a manufacturing scenario from semiconductor industry. Second, in order to be able to react quickly to dynamic changes, it proposes strategies for maintaining information from previously calculated solutions after a change, such as a machine breakdown, occurred.

In many current supply chains, transport processes are not yet being monitored concerning how they influence product quality. Sensor technologies combined with telematics and digital services allow for collecting environmental data to supervise these processes in near real-time. This article outlines an approach for integrating sensor-based quality data into supply chain event management (SCEM). The article describes relationships between environmental conditions and quality defects of automotive products and their mutual relations to sensor data. A discrete-event simulation shows that the use of sensor data in an event-driven control of material flows can keep inventory levels more stable. In conclusion, sensor data can improve quality monitoring in transport processes within automotive supply chains.