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Decision support model for implementing assistive technologies in assembly activities: a case study

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  • Mirco Peron
  • Fabio Sgarbossa
  • Jan Ola Strandhagen

Abstract

The use of assistive technologies, such as digital instructions (DIs) and collaborative robots (cobots), can improve the productivity of assembly system. However, their implementation remains arbitrary. In this study, a decision support system based on tactical-level variables (i.e. throughput, operator and equipment cost, operation time and type) was proposed with the aim of suggesting when the introduction of assistive technologies becomes profitable. Four different assembly system configurations (i.e. manual assembly, manual assembly with the implementation of DIs, manual assembly with the implementation of cobots and manual assembly with the implementation of both DIs and cobots) were modelled by means of four different cost models and analysed in depth with a parametric analysis carried out by varying the tactical-level variables. The results suggested that, when the throughput is low, the introduction of cobots should be considered only in cases of high operation times, while the introduction of DIs and/or cobots is the best alternative when the throughput is high. Finally, the validity of the approach is proved by comparing the results suggested by the decision support system with those obtained from a case study.

Suggested Citation

  • Mirco Peron & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Decision support model for implementing assistive technologies in assembly activities: a case study," International Journal of Production Research, Taylor & Francis Journals, vol. 60(4), pages 1341-1367, February.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:4:p:1341-1367
    DOI: 10.1080/00207543.2020.1856441
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    Cited by:

    1. Lorena Espina-Romero & Jesús Guerrero-Alcedo, 2022. "Fields Touched by Digitalization: Analysis of Scientific Activity in Scopus," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    2. Arpad Gellert & Stefan-Alexandru Precup & Alexandru Matei & Bogdan-Constantin Pirvu & Constantin-Bala Zamfirescu, 2022. "Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions," Mathematics, MDPI, vol. 10(15), pages 1-21, August.
    3. Rita Martinho & Jéssica Lopes & Diogo Jorge & Luís Caldas de Oliveira & Carlos Henriques & Paulo Peças, 2022. "IoT Based Automatic Diagnosis for Continuous Improvement," Sustainability, MDPI, vol. 14(15), pages 1-28, August.
    4. David Mesa & Gianni Renda & Robert Gorkin III & Blair Kuys & Simon M. Cook, 2022. "Implementing a Design Thinking Approach to De-Risk the Digitalisation of Manufacturing SMEs," Sustainability, MDPI, vol. 14(21), pages 1-20, November.

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