Innovative hybrid kitting systems for automotive manufacturing: performance analysis and optimisation
Mário Jorge Simões,
Telmo Pinto and
Cristóvão Silva
International Journal of Production Research, 2025, vol. 63, issue 19, 7013-7038
Abstract:
The automotive industry holds significant potential for robotic kitting systems. This potential depends on item characteristics, process speed, and available space. With mass customisation on the rise, this work explores how Industry 4.0 technologies can enhance kitting processes. It delves into the advantages and challenges of kitting systems, proposing innovative hybrid strategies to overcome these challenges. The study identifies critical research questions, quantitatively models inherent operations, and defines operational research tools for designing automated and collaborative kitting operations. Asynchronous and Sequential Hybrid Kitting Systems layouts are presented alongside an analysis of Mixed Integer Programming models proposed for optimal component allocation to minimise the cycle time. Real-world data from an automotive manufacturer is used to assess critical parameter impacts. Results show increased picking errors lead to more collaborative area allocation and longer cycle times. Simultaneous operator picking significantly reduces cycle times. Various component allocation scenarios highlight that optimal assignments yield lower cycle times, favouring the Sequential system. This work empowers industry decision-makers to choose a kit preparation system that enhances kit preparation quality through Asynchronous Hybrid Kitting or to adopt a faster assembly line-like approach for kitting preparation through Sequential Hybrid Kitting System.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2025.2491783 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:63:y:2025:i:19:p:7013-7038
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2025.2491783
Access Statistics for this article
International Journal of Production Research is currently edited by Professor A. Dolgui
More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().