Carmen Krahe, M.Sc.
Area of Research:
Machine Learning for 3D models
Production Planning and Control
Production control of flexible production systems
- Coordination of lecture Integrated production planning in the age of I4.0 (IPP)
- Detailed planning: Production planning and control
- Coordination of Tutorials for IPP
- AIAx – Artificial Intelligence goes CAx
- MoSyS - Human-oriented design of complex System of Systems
|since 11/2018||Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)|
|10/2011 - 10/2018||Study of Industrial Engineering at Karlsruhe Institute of Technology (KIT)|
|05/04/1993||Born in Karlsruhe|
|[ 1 ]|| Hofmann, C.; Brakemeier, N.; Krahe, C.; Stricker, N. & Lanza, G. (2018), "The Impact of Routing and Operation Flexibility on the Performance of Matrix Production Compared to a Production line". Advances in Production Research, eds. Schmitt, R. & Schuh, G., pp. 155-165.
An increasing number of product variants and a decrease in demand certainty challenge manufacturing companies. Lean, flow-oriented production lines are best-practice to assure efficient production in a predictable environment. However, with the increase in complexity and uncertainty, more flexible production systems such as matrix production currently receive much attention. Having neither a common takt time nor a rigid linkage, they offer new degrees of freedom regarding process order and machine choice. This paper contributes to answering the question under which conditions a matrix production is favourable compared to a production line. To answer this question, the effects of MTTF and MTTR as driving factor to choose a matrix production over a production line are analysed. Regarding the material flow in the matrix, the benefits of routing flexibility and operation flexibility concerning throughput time, tardiness and output of the matrix production are evaluated. The results show that a rule based approach has its limits especially regarding the exploitation of operation flexibility. For low levels of routing flexibility, the rule based approach tends to generate sup-optimal solutions due to a lack of coordination between the agents.
|[ 2 ]|| Lanza, G.; Klenk, F. & Krahe, C. (2019), "Track & Trace als Basis für die Kreislaufwirtschaft in der Automobilindustrie", Werkstoffe in der Fertigung, pp. 22-23.
Die Kreislaufwirtschaft zielt darauf ab, den aktuellen „take, make & dispose“-Ansatz der linearen Wirtschaft, der Auslöser massiver Ressourcenverschwendung ist, durch die mehrfache, wertschöpfende Zufuhr von Produkten in den Produktionsprozess zu verändern. Hierdurch kann der Ressourcen- und Energieverbrauch reduziert sowie die Wirtschaftlichkeit der Unternehmen erhöht werden. Eine Voraussetzung für die Implementierung der Kreislaufwirtschaft ist die lückenlose Nachverfolgbarkeit von Produkten, Komponenten und Materialien. So wird ermöglicht, dass Produkte, die am Ende ihres Lebenszyklus wieder in den Besitz eines Unternehmens gelangen, leichter identifiziert, aufgearbeitet und weiterverwendet werden können. Der vorliegende Artikel zielt darauf ab, eine Übersicht aktueller Track & Trace-Technologien zu geben und die Möglichkeiten aufzuzeigen, wie mittels einer zentralen Austauschplattform ein Mehrwert aus Daten generiert werden kann.
|[ 3 ]|| Greinacher, S.; Overbeck, L.; Kuhnle, A.; Krahe, C. & Lanza, G. (2020), "Multi-objective optimization of lean and resource efficient manufacturing systems", Production Engineering, vol. 14, pp. 165-176. 10.1007/s11740-019-00945-9
In the manufacturing industry, target-oriented and efficient use of resources is gaining importance, alongside economic optimization. The economic and organizational optimization of manufacturing systems according to the lean principles is only partly compatible with the goals of resource-efficient manufacturing. Therefore, an approach is sought to improve individual analyses of manufacturing systems. This paper proposes an approach for the multi-objective optimization of lean and resource-efficient manufacturing systems. To predict the dynamic effects of several configurations of manufacturing systems, material, energy, and information flows of a discrete event simulation are coupled with an assessment model, based on objectives of lean and resource-efficient manufacturing. Using design of experiments, Gaussian process meta-models are computed for the behavior of the simulation model. These meta-models allow the approximation of the system behavior to be computed in a short period of time and enable extensive multi-objective optimization and more adequate decision-making support systems. The proposed approach is tested in the metalworking industry.
|[ 4 ]|| Kandler, M.; Lanza, G. & Krahe, C. (2020), "Development of a Human-centered Industry 4.0 Philosophy". New Developments in Sheet Metal Forming, pp. 123-135.
Industry 4.0 is increasingly finding its way into production. New technologies and instruments are increasingly changing production processes and, in particular, production work. New instruments to support employees make it possible to increase labour productivity, which is a key factor contributing to economic growth. While at the beginning the benefits were unclear and the investment strategy was missing, today the lack of acceptance and unsuitable organizational structures in the companies are the biggest obstacles. This is reminiscent of the first failure of Lean Management. Only after several years with the anchoring of Lean principles as a philosophy and a sustainable improvement culture, Lean Management was successful. What is needed is an approach that involves employees in incremental process improvements using Industry 4.0 in order to support the acceptance. To develop this approach, the respective success factors of Lean Management and Industry 4.0 are first studied and then combined to form an integrated process optimization approach. The result is a human-centred optimization approach that promotes an increased acceptance of Industry 4.0 and supports employees in coping with future flexible work content.