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Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break

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  • Steven Hoedt

    (Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 46, 9052 Gent-Zwijnaarde, Belgium
    Industrial Systems Engineering (ISyE)—Flanders Make@Ghent University, Graaf Karel de Goedelaan 5, 8500 Kortrijk, Belgium)

  • Arno Claeys

    (Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 46, 9052 Gent-Zwijnaarde, Belgium
    Industrial Systems Engineering (ISyE)—Flanders Make@Ghent University, Graaf Karel de Goedelaan 5, 8500 Kortrijk, Belgium)

  • El-Houssaine Aghezzaf

    (Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 46, 9052 Gent-Zwijnaarde, Belgium
    Industrial Systems Engineering (ISyE)—Flanders Make@Ghent University, Graaf Karel de Goedelaan 5, 8500 Kortrijk, Belgium)

  • Johannes Cottyn

    (Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 46, 9052 Gent-Zwijnaarde, Belgium
    Industrial Systems Engineering (ISyE)—Flanders Make@Ghent University, Graaf Karel de Goedelaan 5, 8500 Kortrijk, Belgium)

Abstract

Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don’t target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed.

Suggested Citation

  • Steven Hoedt & Arno Claeys & El-Houssaine Aghezzaf & Johannes Cottyn, 2020. "Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5543-:d:382352
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    References listed on IDEAS

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    1. Glock, C. H. & Jaber, M. Y., 2013. "A multi-stage production-inventory model with learning and forgetting effects, rework and scrap," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 59034, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Jaber, Mohamad Y. & Sikstrom, Sverker, 2004. "A numerical comparison of three potential learning and forgetting models," International Journal of Production Economics, Elsevier, vol. 92(3), pages 281-294, December.
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    Cited by:

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