Cyber-Physical Systems for Knowledge and Expertise Sharing in Manufacturing Contexts: Towards a Model Enabling Design


Cyber-physical systems (CPS) are currently attracting a great deal of attention as a part of the discourse surrounding the fourth industrial revolution. Thus far, the chief focus has been upon complex architectures for supply chain-wide data exchange between intelligent machines. Here, however, we take a very different tack by examining the support CPS may offer for the exchange and acquisition of knowledge-intensive human practices in the context of manual set-up processes on modern production machines. The paper offers contributions to both CSCW and the ongoing development of CPS in three ways. Firstly, it presents a detailed empirical study of knowledge and expertise sharing practices in a production environment. Secondly, the results of this study are used as the basis of an over-arching model that was developed with the express purpose of facilitating design. Finally, CPS-based technical possibilities are matched to the practice-relevant knowledge and expertise sharing requirements captured within the model.

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  1. 1.

    Clearly, despite the grand vision, CPS will not be able to solve all of the problems with KES. Some of the more organisationally-founded issues will remain beyond its remit. Most importantly, CPS technology is relatively new and has a long way to go before achieving its full potential. In our view, this does not preclude the value of what it may have to offer from a research perspective (see de Carvalho et al. 2018).

  2. 2.

    In manufacturing contexts CPS have been termed cyber-physical production systems (CPPS) (Monostori et al. 2016). CPPS are just CPS to support manufacturing processes. For simplicity’s sake we will use CPS throughout the paper, even when talking about manufacturing contexts.

  3. 3.

    There has been extensive debate regarding ‘tacit knowledge’ in CSCW – see Schmidt (2012) for a thorough discussion of the matter. We will not enter into this discussion here because we are simply concerned with acknowledging research that has been performed in related areas. In de Carvalho et al. (2018), we argue, in line Schmidt’s position on KES, that much of what has been deemed tacit is not tacit at all, just embedded in embodied action and visible in other ways apart from propositions.

  4. 4.

    Shadowing is a special type of observation, where a researcher shadows the participant, following them wherever they go (Czarniawska 2007). This technique is of particular relevance for the collection of situated data regarding issues surrounding the main focus of the study. In our case, whilst the observations focused on the set-up process, the shadowing focused on other processes that might have an impact on it. By doing this, we sought to achieve a more nuanced understanding of the social and organisational environment where the process we set out to support was unfolding.

  5. 5., accessed on 20. October 2018

  6. 6.

    In the case of the videos, codes were associated with time frames, just as they were associated with sentences or paragraphs in the transcribed data.

  7. 7.

    Didactic practices describe the ability of a knowledgeable person to effectively communicate knowledge relating to a specific job by demonstrating it to a knowledge seeker in the course of a knowledge transfer process (Schmidt 2012).

  8. 8.

    The Unified Architecture (UA) from the OPC foundation:, accessed on 2nd November 2018.


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The findings in this paper come from the research project ‘Cyberrüsten 4.0: Cyberphysische Unterstützung des Menschen beim Rüstvorgang am Beispiel eines Biegeprozesses zur Klein- serienfertigung auf Basis eines Wissenstransferansatzes’, funded by a grant of the European Union and EFRE.NRW (No. EFRE-0800263).

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Hoffmann, S., de Carvalho, A.F.P., Abele, D. et al. Cyber-Physical Systems for Knowledge and Expertise Sharing in Manufacturing Contexts: Towards a Model Enabling Design. Comput Supported Coop Work 28, 469–509 (2019).

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Key words

  • knowledge sharing
  • expertise sharing
  • manufacturing context
  • cyber-physical systems
  • augmented reality
  • sensor technology