Wissenssicherung und -transfer in der manuellen Montage/Knowledge retention and transfer in manual assembly

Table of contents

Bibliographic information

Cover of Volume: wt Werkstattstechnik online Volume 115 (2025), Issue 09
Open Access Full access

wt Werkstattstechnik online

Volume 115 (2025), Issue 09

Authors:
Publisher
VDI fachmedien, Düsseldorf
Copyright Year
2025
ISSN-Online
1436-4980
ISSN-Print
1436-4980

Chapter information

Open Access Full access

Volume 115 (2025), Issue 09

Wissenssicherung und -transfer in der manuellen Montage/Knowledge retention and transfer in manual assembly

Authors:
ISSN-Print
1436-4980
ISSN-Online
1436-4980
Preview:

Similar to other sectors, the manufacturing industry in Germany is faced by challenges. They include a shortage of skilled labor, rapid advancements in value creation systems, demographic shifts, and an increased willingness to change jobs. This means that expertise is lost more rapidly, making the effective transfer of skills a real necessity. Research is centering on innovative strategies to retain expertise and facilitate its efficient transmission.

Bibliography

  1. [1] Deutscher Industrie- und Handelskammertag e.V. (DIHK): Fachkräfteengpässe schon über Vorkrisenniveau. DIHK-Report Fachkräfte 2021. Stand: 2021. Internet: https://www.dihk.de/resource/blob/61638/9bde58258a88d4fce8cda7e2ef300b9c/dihk-report-fachkraeftesicherung-2021-data.pdf. Zugriff am 11.08.2025, S. 4–27 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  2. [2] Bundesministerium für Arbeit und Soziales (BMAS): Langfristprojektion des Langfristprojektion des Fachkräftebedarfs in Deutschland, 2021 – 2040. Szenario „Fortschrittliche Arbeitswelt“ (Annahmensetzung nach dem Koalitionsvertrag von 2021). Stand: 2023. Internet: https://www.bmas.de/DE/Service/Publikationen/Forschungsberichte/fb-617-langfristprojektion-des-fachkraeftebedarfs.html. Zugriff am 11.08.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  3. [3] Mariano, S.: Mitigating the Disruptive Consequences of Knowledge Loss in Organizational Settings: Knowledge Loss Clusters and Potential Organizational Interventions. European Conference on Knowledge Management 24 (2023) 1, pp. 872–880 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  4. [4] Hulstijn, J. H.: Theoretical and empirical issues in the study of implicit and explicit second-language learning: Introduction. Studies in Second Language Acquisition 27 (2005) 02, pp. 129–140 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  5. [5] Neuweg, G.: Tacit knowing and implicit learning. In: Fischer, M.; Boreham, N.; Nyhan, B. (eds.): European perspectives on learning at work. The acquisition of work process knowledge. Luxembourg: Office for Official Publications of the European Communities 2004, pp. 130–147 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  6. [6] Johnson, T. L.; Fletcher, S. R.; Baker, W. et al.: How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection. Applied ergonomics 74 (2019), pp. 1–9 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  7. [7] Titah, M.; Aitouche, S.; Mouss, M. D. et al.: Externalising and reusing of tacit knowledge in manufacturing task. International Journal of Knowledge Management Studies 8 (2017) 3/4, p. 351 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  8. [8] Feng, S. C.; Bernstein, W. Z.; Hedberg, T. et al.: Towards Knowledge Management for Smart Manufacturing. ASME Journal of computing and information science in engineering 17 (2017) 3, #JCISE-16–2051, doi.org/10.1115/1.4037178 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  9. [9] Kaur, H.; Rani, V.; Kumar, M.: Human activity recognition: A comprehensive review. Expert Systems 41 (2024) 11, doi.org/10.1111/exsy.13680 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  10. [10] Pirsiavash, H.; Vondrick, C.; Torralba, A.: Assessing the Quality of Actions. In: Fleet, D.; Pajdla, T.; Schiele, B. et al. (eds.): Computer Vision – ECCV 2014. Cham: Springer International Publishing 2014, pp. 556–571 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  11. [11] Patrona, F.; Chatzitofis, A.; Zarpalas, D. et al.: Motion analysis: Action detection, recognition and evaluation based on motion capture data. Pattern Recognition 76 (2018), pp. 612–622 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  12. [12] Yan, M.; Liu, X.; Li, Z. et al.: Evaluation of Human Action Based on Feature-Weighted Dynamic Time Warping. Applied Sciences 14 (2024) 23, #11130 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  13. [13] Zia, A.; Sharma, Y.; Bettadapura, V. et al.: Automated video-based assessment of surgical skills for training and evaluation in medical schools. International journal of computer assisted radiology and surgery 11 (2016) 9, pp. 1623–1636 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  14. [14] Zia, A.; Essa, I.: Automated surgical skill assessment in RMIS training. International journal of computer assisted radiology and surgery 13 (2018) 5, pp. 731–739 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55
  15. [15] Lei, Q.; Du, J.-X.; Zhang, H.-B. et al.: A Survey of Vision-Based Human Action Evaluation Methods. Sensors (Basel, Switzerland) 19 (2019) 19, #4129, doi.org/10.3390/s19194129 Open Google Scholar DOI: 10.37544/1436-4980-2025-09-55

Citation

Download RIS Download BibTex

Downloads

Download chapter (PDF)

Notice

This work is protected by copyright and may only be used in accordance with the provisions of the Terms of Use and License Agreement. Any use of the work beyond this is expressly prohibited and will be prosecuted.

Share via email

Media