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Macro and micro-logistic aspects in defining the parts-feeding policy in mixed-model assembly systems

Author

Listed:
  • Maurizio Faccio
  • Mauro Gamberi
  • Marco Bortolini
  • Francesco Pilati

Abstract

The handling activities within mixed-model assembly systems deal with two different logistic levels of the production environment. The micro-logistic level includes movements of parts across each assembly station due to pick to assembly activities. The macro-logistic level includes movements of parts within the supermarket and to deliver the stock-keeping units to the assembly stations. The most frequently adopted part-feeding policies, i.e., kanban system and kitting system, strongly influences both logistic levels with opposite effects. The former continuously refills the assembly stations; the latter prepares and delivers kits of components for each product. Moving from kanban to kitting system the time spent at the macro-logistic level increases. On the contrary, the time spent in part handling at the micro-logistic level decreases when moving from kanban to kitting system. Effective trade-offs are encouraged. This paper analyses the two introduced part-feeding policies, including hybrid possibilities, through an operative total handling time comparison model. The findings from five industrial cases belonging to different sectors and a global simulation analysis are discussed. Conclusions about the impact of some of the most important logistic variables of the production system to the whole performances drive the industrial practitioners in the part-feeding policy selection.

Suggested Citation

  • Maurizio Faccio & Mauro Gamberi & Marco Bortolini & Francesco Pilati, 2018. "Macro and micro-logistic aspects in defining the parts-feeding policy in mixed-model assembly systems," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 31(4), pages 433-462.
  • Handle: RePEc:ids:ijsoma:v:31:y:2018:i:4:p:433-462
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    Citations

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    Cited by:

    1. Emilio Moretti & Elena Tappia & Veronique Limère & Marco Melacini, 2021. "Exploring the application of machine learning to the assembly line feeding problem," Operations Management Research, Springer, vol. 14(3), pages 403-419, December.
    2. Emilio Moretti & Elena Tappia & Martina Mauri & Marco Melacini, 2022. "A performance model for mobile robot-based part feeding systems to supermarkets," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 580-613, September.
    3. Adenipekun, Ebenezer Olatunde & Limère, Veronique & Schmid, Nico André, 2022. "The impact of transportation optimisation on assembly line feeding," Omega, Elsevier, vol. 107(C).

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