The economically designed CUSUM chart for monitoring short production runs

Nenes, George; Tagaras, George

doi:10.1080/00207540500422197

Cited by 31 publications

(36 citation statements)

References 17 publications

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“…Under the condition of a perfect setup, assuming = 0 immediately leads to Equation (3). (14) and (15) The EWMA statistic Y i , i = 1,. . .…”

Section: Discussionmentioning

confidence: 99%

“…Nenes and Tagaras 14 have recently proposed the 'truncated ARL' as a proper statistical performance measure for a CUSUM chart with known population parameters 0 and 0 used to monitor a short production run. In this paper, the truncated ARL and the probability of having at least one signal during an out-of-control production run have been computed and compared for Shewhart and EWMA t-charts.…”

Section: Introductionmentioning

confidence: 99%

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Shewhart and EWMA t control charts for short production runs

Celano

Castagliola

Trovato³

et al. 2011

Quality & Reliability Eng

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Short-run productions are common in manufacturing environments like job shops, which are characterized by a high degree of flexibility and production variety. Owing to the limited number of possible inspections during a short run, often the Phase I control chart cannot be performed and correct estimates for the population mean and standard deviation are not available. Thus, the hypothesis of known in-control population parameters cannot be assumed and the usual control chart statistics to monitor the sample mean are not applicable. t-charts have been recently proposed in the literature to protect against errors in population standard deviation estimation due to the limitation of available sampling measures. In this paper the t-charts are tested for implementation in short production runs to monitor the process mean and their statistical properties are evaluated. Statistical performance measures properly designed to test the chart sensitivity during short runs have been considered to compare the performance of Shewhart and EWMA t-charts. Two initial setup conditions for the short run fixing the population mean exactly equal to the process target or, alternatively, introducing an initial setup error influencing the statistic distribution have been modelled. The numerical study considers several out-of-control process operating conditions including one-step shifts for the population mean and/or standard deviation. The obtained results show that the t-charts can be successfully implemented to monitor a short run. Finally, an illustrative example is presented to show the use of the investigated t charts.

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“…Under the condition of a perfect setup, assuming = 0 immediately leads to Equation (3). (14) and (15) The EWMA statistic Y i , i = 1,. . .…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shewhart and EWMA t control charts for short production runs

Celano

Castagliola

Trovato³

et al. 2011

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Similar to Nenes and Tagaras (2005), the probabilities p ij of moving from C t-1 = i to C t = j may be computed from:…”

Section: Stochastic Model Of the Process And The Control Chartmentioning

confidence: 99%

“…These parameter choices serve two purposes; on the one hand they cover a Each of the 96 examples is optimized for both Shewhart and CUSUM charts using a costminimization criterion. Specifically, optimization of the CUSUM chart is based on the model of Nenes and Tagaras (2006), while optimization of the Shewhart chart is performed using the cost model of Tagaras (1996). Note that no statistical optimization of any kind, for any of the two charts is being attempted at this point.…”

Section: Comparisons Based On the Economically Optimized Schemesmentioning

confidence: 99%

“…Specifically, Nenes and Tagaras (2005) propose statistical measures of performance for control charts that are appropriate for short runs while Nenes and Tagaras (2006) develop a model for the economic optimization of CUSUM schemes in finite-horizon processes. The current paper starts with these models as a basis but extends these earlier works in two directions with respective objectives as follows:…”

Section: Introductionmentioning

confidence: 99%

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