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Tom Stähr, M.Sc.

Research Associate
department: Production Systems
office hours: To be agreed
room: 003, Geb. 50.36,
phone: +49 1523 9502622
Tom Staehr does-not-exist.kit edu

Campus Süd

Tom Stähr, M.Sc.

Area of Research:

  • Scalable Automation in the assembly
  • Changeability
  • Fuel cell
  • Augmented/Virtual Reality


General Tasks:

  • Core team Learning Factory Global Production (Industry 4.0)
  • Learning factory trainer
    • Module Scalable Automation
    • Module Lean and Industry 4.0
  • Responsible of lecture integrated production planning in the context of Industry 4.0 (IPP)
    • Data collection and analysis
    • Concept planning
    • Preperation and controlling of implementation



  • EU Inline - An innovative design of a flexible, scalable, high quality production line for PEMFC manufacturing


Test benches:


Dissertation: Methodology for the planning and selection of configurations of scalable assembly systems – an approach for scalable automation


Curriculum Vitae:

since 09/2015 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)
04/2012 - 05/2015 Study of Business Engineering at the KIT (M.Sc.)
01/2013 - 06/2013 Semester abroad at EM Lyon Business School
10/2008 - 03/2012 Study of Business Engineering at the KIT (B.Sc.)
11/12/1987 born in Hamburg



[ 1 ] Lanza, G.; Stähr, T. & Sapin, S. (2016), "Planung einer Montagelinie mit skalierbarem Automatisierungsgrad", Zeitung für wirtschaftlichen Fabrikbetrieb, no. 10, pp. 614-617.
Der optimale Automatisierungsgrad einer Montagelinie wird beeinflusst durch Lohnniveau, Stückzahlbedarfe und Variantenmix. Kurze Produktlebenszyklen und schwankende Absätze führen zu einer hohen Volatilität von Stückzahlbedarfen und Variantenmix. Die langfristige Einstellung einer performanten Montage erfordert daher eine Skalierbarkeit des Automatisierungsgrades. Im Folgenden wird eine Methodik vorgestellt, mit der bereits in der Planung einer Montagelinie die Skalierbarkeit des Automatisierungsgrades vorausgedacht wird.

[ 2 ] Lanza, G.; Schulze, V.; Bejnoud, F.; Stähr, T.; Wruck, A. & Ren, L. (2016), "Chancen und Herausforderungen für Total – Cost – of – Ownership Betrachtungen von Werkzeugmaschinen", Zeitung für wirtschaftlichen Fabrikbetrieb, no. 12, pp. 1-4.
Für die Betrachtung der Total Cost of Ownership (TCO) von Werkzeugmaschinen gibt es seit vielen Jahren Vorarbeiten. Trotzdem werden TCO in der Praxis bisher kaum berücksichtigt. Auf der Suche nach den Ursachen wurde eine Umfrage unter Herstellern und Betreibern von Werkzeugmaschinen zu Verbreitung, Chancen und Hemmnissen von TCO-Betrachtungen vorgenommen. Ausgehend von den identifizierten Bedürfnissen der Branche wird das weitere Vorgehen bei der Entwicklung eines einheitlichen Standards für TCO-Berechnungen im Maschinen- und Anlagenbau vorgestellt.

[ 3 ] Stähr, T. & Lanza, G. (2017), "Ausfallanalyse von Werkzeugmaschinen", WT-Online, pp. 507-510.
Realitätsnahe Lebensdauerprognosen sind für eine ganzheitliche, betriebswirtschaftliche Kostenbetrachtung sehr wichtig. Wirtschaft und Forschung bemühen sich seit Langem, die Total Cost of Ownership (TCO) von Werkzeugmaschinen zu berücksichtigen. Eine Umfrage unter Herstellern und Betreibern von Werkzeugmaschinen analysiert Verbreitung, erwartete Potentiale sowie Hemmnisse von TCO-Betrachtungen. Anhand der Anforderungen der Branche wurde ein Modell mit Fokus auf der belastungsabhängigen Beschreibung des Ausfallver - haltens von Maschinen und Anlagen entwickelt, das in bestehende Standards eingebettet werden kann.

[ 4 ] Stähr, T.; Ungermann, F. & Lanza, G. (2017), "Scalable assembly for fuel cell production". 7. WGP-Jahreskongress Aachen, 5.-6. Oktober 2017, eds. Schmitt, R. & Schuh, G., pp. 303-311.
The reduced time-to-market and multiple innovations lead to a rising number of emerging technologies and new products. Production systems for emerging technologies are subject to high stress from highly volatile influencing factors such as volume and variants. In order to react to these factors and to achieve cost-efficient production, companies need to establish scalable production systems. This paper introduces a methodology which supports the production planner with an iterative planning method for a scalable production system focussing on the scalability of the level of automation. The methodology consists of four steps. Its basis constitutes in a scenario analysis of the influencing factors for the production system. In the next step, alternative configurations of the production system are generated. From the different configurations, possible scaling paths are derived in accordance with the scenarios. The final step focusses on identifying the optimal scaling paths according to production cost and risk. The methodology will be demonstrated with the use case of fuel cell production within the European research project INLINE.

[ 5 ] Stähr, T.; Englisch, L. & Lanza, G. (2018), "Creation of configurations for an assembly system with a scalable level of automation". Procedia CIRP 76, eds. Wang, S. & Hu, J., pp. 7-12. 10.1016/j.procir.2018.01.024
Due to shortened product lifecycles and an increasing number of variants, the need for scalable assembly systems is rising. This trend is even stronger in the production of emerging technologies. An important step in the planning of a scalable assembly system is the creation of system configurations. State of the art is a scaling of the system from a manual, over semi-automated to an automated system during the start of production. This process is very rigid and does not offer the flexibility which is necessary to react to highly volatile influencing factors. The authors have identified the urgent need for a thorough scenario analysis to adequately consider the risk in predicting volatile influencing factors. In this paper, a two-part methodology is proposed considering multiple scaling mechanisms allowing for a swift and cost-effective adaptation to external factors. The first part is concerned with the scenario analysis. In this part, the planner has to identify the volatile receptors that influence their production. For each of the identified receptors, market studies and workshops with internal experts are conducted to develop a detailed scenario analysis, modelled in a modified BPMN logic. In the second part, the planner needs to develop production system configurations according to the results of the scenario analysis. The appropriate scaling mechanisms are chosen based on the volatile receptors. The application of these mechanisms on station level results in various station concepts satisfying the entire range of expected values within the volatile receptors.

[ 6 ] 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.
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.