Conceptual Framework for Analysing Knowledge Dynamics in Engineering Science

Märten, Anke; Jenek, Julius Friedrich Wilhelm; Wang, Wei Min; Fleck, Claudia; Meyer, Henning; Stark, Rainer; Ammon, Sabine

doi:10.21278/idc.2018.0486

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end, it is relevant to consider not only data, but also information and knowledge. 28 Data is enriched to information and knowledge 29 and can be provided in the form of a data stream, parameters, and digital models on a formal or an informal level. 30 Several notations from different domains allow the modeling and analysis of data streams or processes.…”

Section: State Of the Artmentioning

confidence: 99%

Enabling automated engineering’s project progress measurement by using data flow models and digital twins

Ebel

Riedelsheimer

Stark

2021

International Journal of Engineering Business Management

Self Cite

View full text Add to dashboard Cite

A significant challenge of managing successful engineering projects is to know their status at any time. This paper describes a concept of automated project progress measurement based on data flow models, digital twins, and machine learning (ML) algorithms. The approach integrates information from previous projects by considering historical data using ML algorithms and current unfinished artifacts to determine the degree of completion. The information required to measure the progress of engineering activities is extracted from engineering artifacts and subsequently analyzed and interpreted according to the project’s progress. Data flow models of the engineering process help understand the context of the analyzed artifacts. The use of digital twins makes it possible to connect plan data with actual data during the completion of the engineering project.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Enabling automated engineering’s project progress measurement by using data flow models and digital twins

Ebel

Riedelsheimer

Stark

2021

International Journal of Engineering Business Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…The questions within the interview guide were organized along the key concepts of this framework: knowledge acquisition, knowledge transformation, knowledge generation, knowledge transfer, knowledge coordination and knowledge integration (cf. Märten et al, 2018). The interview guide was pre-tested and refined with researchers of the IIT department.…”

Section: Figure 1 Task For the Student Project -Development Of A Pss Concept Including An Add-on Modulementioning

confidence: 99%

Approaching Knowledge Dynamics Across the Product Development Process with Methods of Social Research

Wang¹,

Mörike²,

Hergesell³

et al. 2019

Proc. Int. Conf. Eng. Des.

Self Cite

View full text Add to dashboard Cite

Knowledge is a crucial factor in state-of-the-art product development. It is often provided by stakeholders from divers disciplinary and individual backgrounds and has to be integrated to create competitive products. Still, it is not fully understood, how knowledge is generated, transformed, transferred and integrated in complex product development processes. To investigate the dynamic interrelations between involved stakeholders, applied knowledge types and related artefacts, researchers at the TU Berlin conducted and evaluated a student experiment to study basic phenomena of development projects. In relation to research methods and instruments applied in this experiment, various improvement opportunities were identified. In this paper, the experimental setting and its results are critically analysed from a social science perspective in order to generate improved research design. Based on the results of this analysis, a first set of methods and instruments from social sciences are identified that can be applied in further experiments. The goal is to develop a methodological toolbox that can be used to approach research on knowledge dynamics in product development.

show abstract

Conceptual Framework for Analysing Knowledge Dynamics in Engineering Science

Cited by 2 publications

References 17 publications

Enabling automated engineering’s project progress measurement by using data flow models and digital twins

Enabling automated engineering’s project progress measurement by using data flow models and digital twins

Approaching Knowledge Dynamics Across the Product Development Process with Methods of Social Research

Contact Info

Product

Resources

About