Simulation analysis of a JIT production line

Meral, Sedef; Erkip, Nesim

doi:10.1016/0925-5273(91)90161-l

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…The three models described above (Huang et al 1983, Philipoom et al 1987, Chaturvedi and Golhar 1992, all use simulation. In addition, simulation models of kanban systems have been developed by Schroer et al (1985) , Lee (1987) , Gravel and Price (1988) , Lee and Seah (1988) , Villeda et al (1988) , Sarkar and Fitzsimmons (1989) , Wilson et al (1989) , Berkley and Kiran (1991) , Meral and Erkip (1991) , Swinehart and Blackstone (1991) , Muralidhar et al (1992) , Berkley (1993) , Gupta et al (1995) , Gupta and Al-Turki (1996, 1998a, 1998b. While simulation models can be used to model the dynamics involved in JIT systems and generate appropriate measures of performance, simulation does not lend itself to qualitative analysis; i.e.…”

Section: Analytic Versus Simulation Models Of Kanban Systemsmentioning

confidence: 99%

Stochastic coloured Petri net (SCPN) models of traditional and flexible kanban systems

Moore¹,

Gupta²

1999

International Journal of Production Research

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Modelling and analysis of kanban-controlled (just-in-time) production systems under realistic assumptions presents a number of challenges, including the ability to conduct both qualitative and quantitative analysis, and the ability to model control policies. These challenges are due, in part, to station interdependence, blocking and starvation due to limited bu er spaces, and the necessity of modelling both material and kanban¯ows. Petri nets (PNs) have recently emerged as a promising approach for modelling manufacturing systems. PNs are a graphical and mathematical technique useful for modelling concurrent, asynchronous, distributed, parallel, non-deterministic and stochastic systems. PN models can be analysed to determine both their qualitative and quantitative properties. In this paper, we develop stochastic, coloured PN (SCPN) models of a JIT system utilizing two di erent kanban control policies: a traditional kanban system (TKS) policy and a¯exible kanban system (FKS) policy. The resulting models can be used to represent JIT systems of arbitrary size, producing single or multiple types of products, with ® xed order points 1. The models are shown to be live and bounded, and can be simulated to produce quantitative results. Sample simulation results are presented to illustrate the models' capabilities.

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Section: Analytic Versus Simulation Models Of Kanban Systemsmentioning

confidence: 99%

Stochastic coloured Petri net (SCPN) models of traditional and flexible kanban systems

Moore¹,

Gupta²

1999

International Journal of Production Research

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“…However, once implemented, JIT is fraught with numerous types of uncertainties such as processing time and demand variations, breakdowns and other types of planned or unplanned interruptions. Amongst the researchers who have addressed some of these issues are Albino et al (1992), Berkley (1990), Chaturvedi and Golhar (1992), Ebrahimpour and Fathi (1985), Egbelu (1987), Wang (1991a), (1991b), Huang et al (1983), Jordan (1988), Kimura and Terada (1981), Lee (1987), Lee and Seah (1988), Meral and Erkip (1991), Monden (1984), Muralidhar et al (1992), Occena and Yokota (1991), Philipoom et al (1987), Ramnarayanan and Gillenwater (1991), Rees et al (1987), Sarker and Fitzsimmons (1989), Sarker and Harris (1988), Savsar (1997), Schroer et al (1985), Swinehart and Blackstone (1991), Villeda et al (1988) and Wilson et al (1989).…”

Section: Ijopm 1910 1066mentioning

confidence: 99%

Flexible kanban system

Gupta

Al-Turki²,

Perry³

1999

International Journal of Operations &Amp; Production Management

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Just-in-time (JIT) systems were originally designed for deterministic production environments such as constant processing times and smooth and stable demand. However, once implemented, JIT is fraught with numerous types of uncertainties, including variations in processing time and demand, planned interruptions such as preventive maintenance and unplanned interruptions such as equipment failure. These uncertainties lead to lowered production throughput, decreased machine utilization, increased order completion time and greater backlogs and overtime requirements. In this paper, we introduce a newly developed system, which we refer to as the flexible kanban system (FKS), to cope with uncertainties and planned/unplanned interruptions. We demonstrate the superiority of the new system by considering four case examples covering various uncertainties, conducting numerous studies and comparing the overall performances of the FKS with that of the traditional JIT system. In all the cases considered, the performance of the FKS was, indeed, superior to that of the traditional JIT system.

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“…JIT is used in Toyota plants in Japan and US, Mercedes Benz in Computers & Industrial Engineering 49 (2005) based on scheduling, material handling and simulation techniques. Savsar and Jawini (1995); Changchit and Kung (1988); Kung and Changchit (1989); Meral and Erkip (1991); Baykoc and Erol (1998); Sarker (1989) have developed computer simulation models for analysis and assessment of JIT production systems. There are other studies, which highlight the importance of JIT simulation modeling (Gross, 1993;Manivannan & Pegden, 1990;Simulation Optimizes JIT System Design, 1997;Nandkeolyar, Ahmad, & Pai, 1998).…”

Section: Introductionmentioning

confidence: 99%