Research & Development Dimension extension/knowledge and decision-making In order to understand which activities lead to which artifacts, a look must be taken at decision-making as such. If, for example, product development is to take account of decision-making, activities relevant to the decision-making process must first be identified. At the same time, the artifacts used in the activities must be taken into consideration as regards the information they contain. The analysis can even go so far as to include knowledge contributed to the activities by artifacts and the people involved. This, however, means that modeling of the data flow is subject to a new requirement. The aim is to model dynamics and variance while at the same time gaining insights across multiple projects. The value added by analyzing the knowledge applied and iden- tifying points of decision can be seen when the development environment is designed. Tools, processes and artifacts can be tailored to the specific points of decision in order, for example, to make the impact on product design easier to see. At the same time, a critical look can be taken at whether a decision was based on facts or on a “gut feeling“ or intuition. In addition, the ability to identify the results of a specific decision makes it possible to evaluate the relevance of the individual decisions. Finally, decision support systems should start at exactly this point. This allows integration in suitable IT systems and the individualized provision of information according to the role involved. Summary and outlook In summary, it can be seen that data flow analysis can not only be used to optimize product development processes and development environments. Using it to coordinate product development based on the flow of data makes detailed and reliable communication of the planned development activities possible. Even the last level of detail, the implementation of tasks by individual people or by machines, results in a closed feedback loop, which enables comprehensive optimization of the development environment. In the future, the data flow will be at the core of the coordinating instance. As the level of automation in product development increases, the coordination of human-machine collaboration on the basis of data flow becomes an essential prerequisite for the efficient implementation of product development processes. This makes it possible to record the highest level of detail auto- matically using learning algorithms, thus enabling continuous maintenance of the control loop. 38 ProductDataJournal 2019-2 References: [1] Abramovici, Michael; Herzog, Otthein (Hg.) (2016): Engineering im Umfeld von Industrie 4.0. Einschätzungen und Handlungsbedarf. Munich: Herbert Utz Verlag GmbH; acatech (Acatech study). [2] acatech (2011): Cyber-Physical Systems. Innovationsmotor für Mobilität, Gesundheit, Energie und Produktion. Berlin, Heidelberg: Springer (acatech Position, 11). Available online at http://dx.doi.org/10.1007/978-3-642- 27567-8. [3] VDI 2221 Blatt 1, 03/2018: Entwicklung technischer Produkte und Systeme. [4] Lünnemann, Pascal; Stark, Rainer; Wang, Wei Min; Manteca, Paola Ibanez (2017): Engineering activities — considering value creation from a holistic perspective. In: Ricardo Jardim-Gonçalves (Hg.): “Engineering, technology & innovation management beyond 2020: new challenges, new approaches”. 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC) : conference proceedings. Piscataway, NJ: IEEE, pp. 315–323. [5] Lünnemann, Pascal; Wang, Wei Min; Stark, Rainer: Methodische Analyse der Entwicklungsaktivitäten. In: Peter Köhler (Hg.): 15. Gemeinsames Kollo- quium Konstruktionstechnik 2017. DuEPublico: Duisburg-Essen Publications Online, University of Duisburg-Essen, Germany: DuEPublico: Duisburg- Essen Publications Online, University of Duisburg-Essen, Germany, pp. 89–98. [6] Müller, Patrick; Lindow, Kai; Gregorzik, Stefan; Stark, Rainer (Hg.) (2019b): Smart Industrial Products. Smarte Produkte und ihr Einfluss auf Geschäfts- modelle, Zusammenarbeit, Portfolios und Infrastrukturen. PDM | PLM Competence Center des Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechik – IPK – Berlin. Berlin. [7] Ōno, Taiichi; Hof, Wilfried; Stotko, Eberhard C.; Rother, Mike (2013): Das Toyota-Produktionssystem. Das Standardwerk zur Lean Production. 3., expanded and updated version. Frankfurt am Main: Campus-Verl. (Produk- tion). [8] Porter, Michael E.; Heppelmann, James E. (2015): Wie smarte Produkte Unternehmen verändern. In: Harvard Business manager (12), pp. 52–74. [9] DIN EN ISO 9001, 2008-12: Qualitätsmanagementsysteme. [10] Riedelsheimer, T.; Lünnemann, P.; Lindow, K.; Stark, R. (2017): Betrachtung des Entwicklungsumfeldes durch die methodische Datenflussanalyse. In: ProduktDaten Journal (2), S. 52–56. [11] Taylor, Frederick Winslow (2004): Die Grundsätze wissenschaftlicher Betriebsführung. (The principles of scientific Management). [Nachdr. der Ausg.] München, Oldebourg, 1913. Düsseldorf: VDM-Verl. Müller (Classic edition). Contact Dr.-Ing. Kai Lindow Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik Head of the Information and Process Control Department kai.lindow@ipk.fraunhofer.de Phone +49 30 39006214