Milk-run kanban system for raw printed circuit board withdrawal to surface-mounted equipment

Swee Li Chee, Mei Yong Chong, Jeng Feng Chin


Purpose:  The paper aims to present a case study and later simulation analysis on a kanban system that incorporating milk-run operation to draw in raw material to the process.

Design/methodology/approach:  Data collection at the case study company for ten weeks followed by a process study called value stream mapping. The proposed kanban model is simulated to test its various performances including total output, average flow time, average work-in-process, SME utilization, and average waiting time. Response surface methodology is adopted to generate suitable representative regression models. 

Findings: For all performance measures, simulation results showed that the proposed system consistently outperforms the push system currently practiced. Second, the system indicates the advantages of leveling, particularly in the event of machine failure and blockage. Third, operator in the proposed kanban system has a lower utilization, even with the additional material handling task.  

Research limitations/implications: This study only begins to reveal the implication of leveling for production control on multi-machine scenario. The simulation of the system is solely based only the case study. The control parameters critical to the case study, were naturally used. The furtherance of the research should include generalizing the system and devising the respective methodology to facilitate wider applications.

Practical implications: Originality/value:  The kanban system is proposed in the light of conflicting interests in handling the surface mounting and the related upstream processes. Such aspect is common to electronics assembly industry.


printed circuit board, milk-run, kanban system, value stream mapping

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Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2024

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience