Dr.-Ing. Shun Yang

  • Campus Süd

Dr.-Ing. Shun Yang

Areas of Research:

  • Global Production
  • Site-specific production using Industry 4.0
  • Smart Automation
  • Digital Business Model for Production


General Responsibilities:




Test benches:



Regionalized implementation strategy of smart automation within assembly systems in China


Curriculum Vitae:

Since 04/2021 Post-Doc at KIT
since 04/2017 Executive Director of KIT China Branch
since 01/2016 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)
09/2013 - 12/2015 Study of Production Operation Management at the KIT, China (M.Sc.)
since 04/2011 Project Engineer at GAMI
04/2010 - 03/2011 Research exchange at the IWF of TU-Braunschweig
09/2007 - 03/2010 Study of Mechanical Engineering at the XAUAT, China (M.Sc.)
09/2003 - 07/2007 Study of Mechanical Engineering at the XAUAT, China (B.Sc.)
01/05/1985 Born in Xi´an


[ 1 ] Yang, S.; Arndt, T. & Lanza, G. (2016), "A flexible simulation support for production planning and control in small and medium enterprises". Procedia CIRP 56, eds. Elservier, pp. 389-394.
For efficient, effective and economical production operation management in a manufacturing unit of an organization, it is essential to integrate the production planning and control system into an enterprise resource planning. Today’s planning systems suffer from a low range in planning data which results in unrealistic delivery times. One of the root causes is that production is influenced by uncertainties such as machine breakdowns, quality issues and the scheduling principle. Hence, it is necessary to model and simulate production planning and controls (PPC) with information dynamics in order to analyze the risks that are caused by multiple uncertainties. In this context, a new approach to simulate PPC systems is exposed in this paper, which aims at visualizing the production process and comparing key performance indicators (KPIs) as well as optimizing PPC parameters under different uncertainties in order to deal with potential risk consuming time and effort. Firstly, a production system simulation is created to quickly obtain different KPIs (e.g. on time delivery rate, quality, cost, machine utilization, WIP) under different uncertainties, which can be flexibly set by users. Secondly, an optimization experiment is conducted to optimize the parameters of PPC with regard to the different KPIs. An industrial case study is used to demonstrate the applicability and the validity of the proposed approach.

[ 2 ] Lanza, G.; Kopf, R.; Zaiß, M.; Stricker, N.; Eschner, N.; Yang, S.; Jacob, A.; Schönle, A.; Webersinke, L. & Wirsing, L. (2017), "Laser-Strahlschmelzen - Technologie mit Zukunftspotential". Commissioner: Bundesministerium für Bildung und Forschung. Ein Handlungsleitfaden, Karlsruhe, eds. Wbk Institut für Produktionstechnik, ISBN/ISSN: 978-3-00-056913-5.
Additive Fertigungsverfahren haben sich in den vergangenen Jahren im Prototypenbau etabliert: Der deutlich höhere Freiheitsgrad im Bauteildesign sowie der Wegfall von Werkzeugkosten wecken das Interesse der Industrie, diese Verfahren auch für die Serienproduktion zu verwenden. Für die Fertigung mit Metallen verspricht vor allem das Laser-Strahlschmelzen (engl. Laser Beam Melting, LBM) großes Potenzial. Aus dem jetzigen Stand der Technik eignet sich dieses allerdings nur bedingt für die Serienanwendung. Um LBM an die Anforderungen und Bedürfnisse möglicher Anwender anzupassen, muss es entsprechend weiterentwickelt werden. Am wbk wurde die Studie „Laser-Strahlschmelzen – Technologie mit Zukunftspotenzial“ entwickelt, welche geeignete Maßnahmen ableitet, die das Verfahren für eine zielgerichtete Industrialisierung vorbereitet und priorisiert. Die Studie zeigt zudem zukünftige Entwicklungstrends des LBM-Verfahrens auf und kategorisiert potenzielle wirtschaftliche Einsatzgebiete der Anwender. Basierend auf 28 Interviews sowie Workshops mit anerkannten Experten aus der Industrie und Forschung ließen sich letztlich Handlungsempfehlungen für Unternehmen, Forschung, Verbände und Politik ableiten. Die notwendigen Entwicklungstätigkeiten wurden in einer Roadmap festgehalten. Deutlich wird, dass die geringe Prozessstabilität und Produktivität der LBM-Anlagen sowie der gesamten Prozesskette als größte Hürde gelten. Auch bei den verwendeten Materialien und von den entstehenden Bauteilen erwartet die Industrie große Entwicklungen. Das LBM-Verfahren eignet sich zum heutigen Zeitpunkt vor allem für Bauteile in kleinen Stückzahlen. Experten schreiben dem LBM-Verfahren ein großes Potenzial für die Serienfertigung zu. Allerdings müssen hierfür weitere Entwicklungsmaßnahmen vorgenommen werden. Die Studie definiert aus den Anforderungen der Anwender Klassen, die als besonders relevant betrachtet werden: • Variantenreiche, individualisierte Serienproduktion • Qualitätsorientierte Produktion großer Bauteile • Kostengünstige Produktion in hoher Stückzahl Diese drei Klassen stellen Extremausprägungen dar, zwischen denen sich die Anwender mehrheitlich einordnen lassen und welche die Grundlage für die Handlungsempfehlungen bilden.

[ 3 ] Yang, S.; Hamann, K.; Haefner, B.; Wu, C. & Lanza, G. (2018), "A Method for Improving Production Management Training by Integrating an Industry 4.0 Innovation Center in China". Procedia Manufacturing Volume 23, eds. Elsevier, pp. 213-218. 10.1016/j.promfg.2018.04.019
Production Management is an important issue for organizations that spend considerable amounts of investment annually on personnel training. Especially in the era of Industry 4.0 and Intelligent Manufacturing, considering the exponential growth of new knowledge and information, personnel need to update and supplement the necessary knowledge. Nevertheless, there is a lack of adequate methodology for executing trainings in the field of production management. This paper aims to develop a method for executing production management training which combines online learning and offline training as well as practical parts by using an Industry 4.0 Innovation Center equipped with model devices. A procedure is to first starting with an E-Learning module containing basic knowledge, accessible on the Learning Platform Moodle. Secondly, an on-line survey is created to collect expectations and requirements. Then the training schedule is carried out for execution of professional training. The training part in the innovation center will contribute to build up the basis for adaptions of the training knowledge to practical need of a company. Lastly the test and evaluation is conducted via virtual team room (Vitero). A case study based on training service provider is used to validate the feasibility of the approach. The derived results are presented and conclusions are discussed.

[ 4 ] Yang, S.; Boev, N.; Haefner, B. & Lanza, G. (2018), "Method for Developing an Implementation Strategy of Cyber-Physical Production Systems for Small and Medium-sized Enterprises in China". Procedia CIRP, eds. ELSEVIER, pp. 48-52. 10.1016/j.procir.2018.01.027
Enabled by the development of internet technologies, cyber-physical production systems (CPPS) are expected to open up entirely new possibilities to improve the efficiency of existing assembly systems of industrial companies. Nevertheless, realizing the potential of CPPS still remains a difficult task for small and medium-sized enterprises (SME), given the high variety of improvement possibilities offered by CPPS enabling technologies and the limited resources for their deployment. Hence, it is necessary to develop an implementation strategy of CPPS. Meanwhile, the consideration of location factors could support industrial companies to identify the appropriate CPPS implementation strategy since the location factors highly effect assembly system environment. In this context, a new approach to analyse the influence of location factors on the implementation of CPPS is exposed in this paper, which aims at investigating and identifying of relationships in between. Firstly, an application map of CPPS is generated. Secondly, the manufacturing industry status analyzed and subsequently a catalog of currently important location factors for the assembly systems are identified. Then a qualitative model of a relational analysis is established by an agglomerative hierarchical clustering algorithm. An industrial case study is used to demonstrate the applicability and the validity of the proposed approach.

[ 5 ] Yang, S.; Schrage, J.; Haefner, B. & Lanza, G. (2019), "Development of a regionalized implementation strategy for smart automation within assembly systems in China". 80, eds. Procedia CIRP, pp. 723-728. 10.1016/j.procir.2019.01.039
Companies struggle to overcome the difficulties stemming from the dynamic environment of global production due to the specific conditions in different regions. Particularly, insufficient know-how about a regionalized implementation strategy of smart automation (SmAu) technologies is one significant difficulty for enterprises. Thus, developing a key performance indicator (KPI) oriented, regionalized implementation strategy for smart automation technologies is increasingly important. In this context, a new approach is exposed to systematically investigate and identify the interdependencies among location factors, smart automation technologies, and KPIs. Firstly, the environment consisting of location-related factors, KPIs and smart automation technologies is defined in detail. Further, a Catalog quantifies the influence of different regions in China. Secondly, important aspects to model the qualitative and quantitative interdependencies in a multimethod simulation are introduced. Subsequently, an approach to analyze suitable implementation strategies is presented. A case study based on a production line for digitalized production technology is used to validate the proposed approach.

[ 6 ] Gönnheimer, P.; Kimmig, A.; Mandel, C.; Stürmlinger, T.; Yang, S.; Schade, F.; Ehrmann, C.; Klee, B.; Behrendt, M.; Schlechtendahl, J.; Fischer, M.; Trautmann, K.; Fleischer, J.; Lanza, G.; Ovtcharova, J.; Becker, J. & Albers, A. (2019), "Methodical approach for the development of a platform for the configuration and operation of turnkey production systems". Procedia CIRP, eds. Putnik, G., pp. 880-885. 10.1016/j.procir.2019.04.260
Shorter product lifecycles lead not only to faster time-to-market for products but also to the need for just as fast available associated production systems. These shorter product lifecycles, as well as the increasing individualization of products, also result in further decreasing production lot sizes. Young companies in China in particular are characterized by a very high speed of innovation but may not have the necessary manufacturing knowledge or capacities to bring their developed products to the market with a scalable production. For this reason, there is a great need to quickly set up and commission turnkey production systems or to reconfigure existing production systems for new production tasks in the shortest possible time. This paper describes the design and architecture of a cloud platform with the aim to support a manufacturer independent design process for turnkey production systems. This process ranges from the product to be manufactured to the operation of the production system. Firstly, the structure and methodology used to link the various objectives are discussed. The system for recording and structuring product and production system data to create reusable modules from components and machines is described. Subsequently, the use of standardized modules is developed to support reconfiguration of the production system during operation. In addition, the digital business models tailored to the production system are proposed to the platform user for commissioning and operation of the plant. A case study is conducted to validate the proposed methodology.

[ 7 ] Yang, S.; Liu, H.; Zhang, Y.; Arndt, T.; Hofmann, C.; Häfner, B. & Lanza, G. (2020), "A Data-Driven Approach for Quality Analytics of Screwing Processes in a Global Learning Factory". Learning Factories across the value chain - from innovation to service, pp. 454-459. 10.1016/j.promfg.2020.04.052
A network can be very beneficial for root cause analysis due to different data from various factories. Nevertheless, it is difficult to obtain reliable and consistent data. In this context, this paper aims to develop a method for data-driven oriented quality analytics of screwing processes considering a global production network.