Potential Models of Group Learning in Production

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A4 Artikkeli konferenssijulkaisussa
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Date

2020-10-12

Department

Department of Energy and Mechanical Engineering

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Language

en

Pages

12

Series

SPS2020 - Proceedings of the Swedish Production Symposium, pp. 205-216, Advances in Transdisciplinary Engineering ; Volume 13

Abstract

Working in groups is beneficial for many complex production jobs as groups can have the cognitive and physical capacity that lacks from individuals. The group learning process is complicated when, in addition to individual learning by doing, the number of workers and knowledge transfer have their effects. Production managers need tools for analyzing and predicting group performance and learning over future production periods. Mathematical learning curve models are one of those tools that managers use, with a few are available for groups. This paper reviews potential group learning curve models for production environments. The models are fitted to data from an assembly experiment consisting of different group sizes and repetitions. The results show that more parameters improve the fit. A qualitative evaluation has been performed to answer how well the models reflect group learning and support decision making in production and how their prediction of data could be improved. The results suggest that the S-shaped model performed the best making it a potential one for describing learning in groups in production environments. The paper also suggests future directions along with this line of research.

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Keywords

Group learning, learning curve model, group size, production, experimental data

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DOI

10.3233/ATDE200158

Citation

Peltokorpi, J & Jaber, M Y 2020, Potential Models of Group Learning in Production. in K Safsten & F Elgh (eds), SPS2020 - Proceedings of the Swedish Production Symposium. Advances in Transdisciplinary Engineering, vol. 13, IOS Press, pp. 205-216, Swedish Production Symposium, Jönköping, Sweden, 07/10/2020. https://doi.org/10.3233/ATDE200158

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https://urn.fi/URN:NBN:fi:aalto-202101251605

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[article-cris] Insinööritieteiden korkeakoulu / ENG