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M.Sc. Constantin Hofmann

Research Associate
Production Systems
office hours: To be agreed
room: 110, Geb. 50.36
phone: +49 1523 9502583
Constantin HofmannUsp9∂kit edu

76131 Karlsruhe
Kaiserstraße 12


M.Sc. Constantin Hofmann

Area of Research:


General Tasks:

  • Coordination of lecture…
    • MBA Hector Production Engineering
      • integrated production planning in the age of Industry 4.0 (IPP)
      • IT in the Industry 4.0 factory
      • Ramp-up and series coordination
  • Workshop coach Lean and Industry 4.0
  • Responsible for the software development team


Projects:

  • Innovation Center SAP – Development of an AI-based multi-agent production control for matrix production
  • AiF ShopfloorPulse

 

Test benches:

 

Dissertation: Development of an AI-based multi-agent production control for matrix production

 

Curriculum Vitae:

05/07/1989 Born in Frankfurt am Main
10/2010-04/2014 Industrial Engineering (B.Sc.) at KIT focusing on information technology and production science
09/2012-06/2013 Génie industriel at INSA Lyon
04/2014-12/2016 Industrial Engineering (M.Sc.) at KIT
since 03/2017 Research Associate at Institute of Production Science (wbk) at Karlsruher Institute of Technology (KIT)

Publications

[ 1 ] Hofmann, C.; Lauber, S.; Haefner, B. & Lanza, G. (2018), "Development of an agile development method based on Kanban for distributed part-time teams and an introduction framework". Advanced Engineering Education & Training for Manufacturing Innovation, eds. Mourtzis, D. & Chryssolouris, G., pp. 45-50.
Abstract:
In the context of Industry 4.0, the development of maintainable and scalable applications becomes a core activity to master for industrial companies. To offer convincing trainings, learning factories have to coordinate the development of IT and hardware solutions with training concepts. The development of maintainable, modular and stable IT solutions in coherence with the hardware is the basis for good trainings. Especially learning factories face the challenge to work with part-time staff that is unfamiliar with large IT and hardware projects, web technology or PLC programming and development methods. In this paper we present a model how to introduce agile methods step-by-step to teams that have to cope with the challenge of a new project paired with a new technology and development methodology without overloading the team. An adapted teaching concept to introduce these development methods has been developed. For each of the maturity levels, tools and processes are presented as well as criteria to indicate when the team is ready to pass to the next level. The paper also presents the results of an application of the model to the development team at the Learning Factory Global Production at wbk.

[ 2 ] Lanza, G.; Hofmann, C.; Stricker, N.; Biehl, E. & Braun, Y. (2018), "Auf dem Weg zum Digitalen Shopfloor Management". Eine Studie zum Stand der Echtzeitentscheidungsfähigkeit und des Industrie 4.0-Reifegrads.
Abstract:
Shopfloor Management ist ein etabliertes Führungskonzept zur Stärkung der Koordination auf dem Shopfloor und zur Verankerung einer Kultur der kontinuierlichen Verbesserung. Shopfloor Management ist deutlich mehr als Besprechungen und Kennzahlen-Boards. Im Kern geht es um ein mensch-zugewandtes Führungsverständnis, das die Mitarbeiterentwicklung in den Vordergrund stellt. Der persönliche Kontakt, regelmäßige Abstimmungen und eine hohe Identifikation mit den Arbeitsergebnissen sind entscheidend für den Erfolg des Shopfloor Managements. Im Zuge der Digitalisierung unserer Arbeitswelt ändert sich auch die Gestaltung unserer Informationssysteme, weg von Expertensystemen hin zu menschzentrierten Assistenzsystemen, deren Benutzung sogar positive Emotionen wecken kann. Um die neuen technischen und gestalterischen Möglichkeiten dahingehend einzusetzen, dass der Kern des Shopfloor Managements gestärkt wird, ist ein tiefgreifendes Verständnis der Ziele, Erwartungen und der gelebten Praxis des Shopfloor Managements nötig. Vor diesem Hintergrund entstand durch das Institut für Produktionstechnik (wbk) am Karlsruher Institut für Technologie (KIT) im Rahmen des Gemeinschaftsprojekts ShopfloorPulse mit dem IPRI - International Performance Research Institute diese Studie zu Darstellung des Ist-Zustandes des Shopfloor Managements in Deutschland. Im Rahmen von zahlreichen Interviews mit Experten aus der Industrie wurden die derzeitige Nutzung von Shopfloor Management und die vorbereitenden Prozesse zur Bereitstellung und Aufarbeitung der Daten herausgearbeitet und in ein Framework eingeordnet. Aus den Ergebnissen der Studie ergibt sich ein ganzheitliches Bild über den derzeitigen Einsatz von Shopfloor Management als Grundlage für eine zielgerichtete Digitalisierung. Wir möchten uns an dieser Stelle für die Förderungen des Bundesministeriums für Wirtschaft und Energie (BMWi), die Unterstützung des Projektträgers Arbeitsgemeinschaft industrieller Forschungsvereinigungen “Otto von Guericke” (AiF) und bei der Fördervereinigung Bundesvereinigung Logistik (BVL) e.V. bedanken. Unser Dank gilt insbesondere den vielen aktiven Teilnehmern aus der Industrie, die einen entscheidenden Beitrag zum Gelingen dieser Studie beigetragen haben.

[ 3 ] 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.
Abstract:
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.

[ 4 ] Hofmann, C.; Stähr, T.; Cohen, S.; Stricker, N.; Haefner, B. & Lanza, G. (2019), "Augmented Go & See: An approach for improved bottleneck identification in production lines". Procedia Manufacturing , eds. Christoph Herrmann, S. T., pp. 148-154.
Abstract:
Bottlenecks in production lines are often shifting and thus hard to identify. They lead to decreased output, longer throughput times and higher work in progress. Go & See is a well-established Lean practice where managers go to the shop floor to see the problems first hand. Mixed reality is a promising technology to improve transparency in complex production environments. Until recently, mixed reality applications have been very demanding in terms of computing power requiring high performance hardware. This paper presents an approach for real-time KPI visualization using mixed reality for bottleneck identification in production lines relying on the bring-your-own device principle. The developed application uses image recognition to identify work stations and visualizes cycle times and work in progress in augmented reality. With this additional information, it is possible to discern different root causes for bottlenecks, for example systematically higher or varying cycle times due to breakdowns. This solution can be classified according to the acatech industry 4.0 maturity model as a level 3 - transparency - application. It could be shown that the identification of bottlenecks and underlying reasons has been improved compared to standard Go & See.