The effect of autocorrelated demand in JIT production systems

Takahashi, Katsuhiko; Nakamura, Naohiro

doi:10.1080/002075498193264

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…These conclusions are also shared by Takahashi and Nakamura [51], Altiok and Melamed [52] and Pereira et al [53], who found that both positive and negative Corr(A) and Corr(S) have a negative impact on the performance of serial production lines.…”

Section: Auto-correlated Processessupporting

confidence: 58%

Serial production line performance under random variation: Dealing with the ‘Law of Variability’

Romero-Silva

Marsillac

Shaaban

et al. 2019

Journal of Manufacturing Systems

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Section: Auto-correlated Processessupporting

confidence: 58%

Serial production line performance under random variation: Dealing with the ‘Law of Variability’

Romero-Silva

Marsillac

Shaaban

et al. 2019

Journal of Manufacturing Systems

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“…They introduce autocorrelation by means of TES (Transform-Expand-Sample) processes and their results alert on the potential losses on accuracy that might be derived from an inadequate use of independence assumption. Takahashi and Nakamura (1998) studied the case of Just-In-Time systems achieving similar conclusions. Balcioglu et al (2007) provide an approximate solution for the mean waiting times when autocorrelation is present in time to failure time series.…”

Section: Introductionmentioning

confidence: 69%

Autocorrelation effects in manufacturing systems performance: A simulation analysis

Pereira

Vilas

Monteil

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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Autocorrelation has been pointed out as one of the most challenging issues in manufacturing systems modeling. Numerical experimentation has shown that it may either enhance or harm performance. Furthermore, there is not yet a general agreement in what a realistic autocorrelation model is or whether it is actually relevant for practical applications. This paper provides a simulation analysis of the effects on performance caused by manufacturing process parameters following autoregressive (AR) processes. AR time series are employed for modeling variations in parameters that happen at a time scale different from the corresponding to process cycle execution. Three basic configurations are analyzed: serial line, assembly process and a disassembly process. A case study from the natural slate tiles industry is presented showing the differences obtained in simulation results between a model in which independent and identically distributed (i.i.d.) assumptions are adopted and one in which autocorrelation effects are considered.

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“…Livny et al (1993) demonstrate that autocorrelation in service or inter-arrival times may severely affect the queue lengths and consequently waiting time distribution of a system. Takahashi and Nakamura (1998) analyze a production system controlled by Kanban cards with concurrent and sequential ordering processes. They demonstrate that correlated demand arrivals can have significant impact on the WIP inventories and on the expected waiting time of the products.…”

Section: Impact Of Correlation On the Performance Of Queuing And Manumentioning

confidence: 99%

Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes

Dizbin

Tan

2018

Flex Serv Manuf J

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Empirical studies show that the inter-event times of a production system are correlated. However, most of the analytical studies for the analysis and control of production systems ignore correlation. In this study, we show that real-time data collected from a manufacturing system can be used to build a Markovian arrival processes (MAP) model that captures correlation in inter-event times. The obtained MAP model can then be used to control production in an effective way. We first present a comprehensive review on MAP modeling and MAP fitting methods applicable to manufacturing systems. Then we present results on the effectiveness of these fitting methods and discuss how the collected inter-event data can be used to represent the flow dynamics of a production system accurately. In order to study the impact of capturing the flow dynamics accurately on the performance of a production control system, we analyze a manufacturing system that is controlled by using a basestock policy. We study the impact of correlation in inter-event times on the optimal base-stock level of the system numerically by employing the structural properties of the MAP. We show that ignoring correlated arrival or service process can lead to overestimation of the optimal base-stock level for negatively correlated processes, and underestimation for the positively correlated processes. We conclude that MAPs can be used to develop data-driven models and control manufacturing systems more effectively by using shop-floor inter-event data.

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The effect of autocorrelated demand in JIT production systems

Cited by 10 publications

References 12 publications

Serial production line performance under random variation: Dealing with the ‘Law of Variability’

Serial production line performance under random variation: Dealing with the ‘Law of Variability’

Autocorrelation effects in manufacturing systems performance: A simulation analysis

Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes

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