Madlene Leißau, Christoph Laroque • Winter Simulation Conference 2024, Orlando (USA)

The paradigm shift towards Industry 4.0 and the emerging trends of Industry 5.0 present ongoing challenges in production planning and control. In response to these dynamics, discrete event-driven simulation methods are gaining prominence as an operational decision-support-tool, particularly in the semiconductor industry. This paper introduces an automated low-code framework designed to synchronize model structures across simulation tool boundaries for extensive simulation studies, using the Semiconductor Manufacturing Testbed 2020 as a test reference, and aims to serve as a helpful tool for simulation experiments. Key aspects include model structure synchronization, Design of Experiments, and the distributed execution of large-scale simulation studies.

ResearchGate

Maurice Großmann, Madlene Leißau, Christoph Laroque • 27. ASIM Symposium Simulationstechnik, München

The development of simulation-based decision support systems requires large amounts of experimental data, which are generally not sufficiently available in compa-nies. Adequate design of experiments with large-scale simulation experiments can take into account many factors in the subsequent decision making process. At the same time, the number of necessary simulation runs increases. In order to support the process of experiment planning and distributed simulation execution, the au-thors have developed a low-code-based infrastructure in KNIME that distributes and evaluates the simulation runs for computation in a Kubernetes cluster. This should help to reduce the time required for experiments and make the systems more applicable.

ResearchGate

Günther Gaßner, Jenny Rüffer, Madlene Leißau, Tobias Voigt, Christoph Laroque • 27. ASIM Symposium Simulationstechnik, München

The wolrd faces significant sustainability challenges, from resource consumption to supply chain inefficiencies. A comprehensive simulation-based approach for creating Digital Twins (DTs) is developed to address there issues. This paper contributes to the gradual development of DTs and presents a novel architecture for a simulation tool. The proposed architecture enables the creation of a holistic virtual representation of the system by incorporating linear and circular production models, supporting various sceanrios, identifying optimization potential, and making data-driven decisions. Using simulation-based DTs indicates the potential to drive sustainable transformation in the beverage industry and beyond. As the approach progresses, it aims to provide a blueprint for leveraging digital technologies, fostering a more sustainable and resilient future.

ResearchGate

Sigrid Wenzel, Deike Gliem, Christoph Laroque • Industry 4.0 Science, Volume 40(5)

In the mechanical and plant engineering industry, characterized by small and medium-sized enterprises (SMEs), timely commissioning of customized products is a decisive competitive factor. Successful implementation requires precise project planning and control, which depend on constantly updated information concerning all processes throughout the project execution phase. Alongside digitalization and its associated challenges of establishing a digital shadow for production and logistics processes in line with specific requirements, sustainability is becoming increasingly important. As suppliers, SMEs are indirectly affected by the statutory reporting obligations for CO2 balancing. In the future, they will also need to determine the Product Carbon Footprint (PCF) of their customized products to meet sustainability reporting requirements and secure competitive advantages. This article discusses the specific challenges involved in developing a digital shadow for one-of-a-kind and small-batch manufacturers in mechanical and plant engineering as well as its utilization to assess the CO2 emissions of customized products and outlines a methodological research approach.

ResearchGate

Madlene Leißau, Christoph Laroque • SNE Simulation Notes Europe 34(3)

Manufacturing processes are increasingly driven by new product needs, innovations, and cost efficiency. Planning Staff and decision makers face the challenge of achieving fixed production programs and subsequently individual orders in a certain quantity and within a certain period at a guaranteed completion date. A systematic approach to scheduling and tracking resource requirements is necessary to ensure efficient flow of manufactured products. Forward- and backward-oriented planning strategies are most used by manufacturers to meet their demands for existing orders. The current application of such approaches is very time and resource intensive due to the complexity and dimension of the decision and planning problems to be considered; it is difficult to react to short-term changes within the production program. To address this gap, this paper provides a systematic literature review of backward decision and planning approaches in production scenarios and presents a potential overarching solution approach of a simulation- and machine learning-based decision support combination for operational production planning.

ResearchGate