• 산업경영시스템학회지
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• 제47권1호
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• pp.20-27
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• 2024

Determining the number of operators who set up the machines in a human-machine system is crucial for maximizing the benefits of automated production machines. A man-machine chart is an effective tool for identifying bottlenecks, improving process efficiency, and determining the optimal number of machines per operator. However, traditional man-machine charts are lacking in accounting for idle times, such as interruptions caused by other material handling equipment. We present an adjusted man-machine chart that determines the number of machines per operator, incorporating idleness as a penalty term. The adjusted man-machine chart efficiently deploys and schedules operators for the hole machining process to enhance productivity, where operators have various idle times, such as break times and waiting times by forklifts or trailers. Further, we conduct a simulation validation of traditional and proposed charts under various operational environments of operators' fixed and flexible break times. The simulation results indicate that the adjusted man-machine chart is better suited for real-world work environments and significantly improves productivity.

• 대한산업공학회지
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• 제38권4호
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• pp.237-243
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• 2012

This paper proposes quantitative investment strategies for KOSPI200 index futures using VKOSPI and control chart. Stochastic control chart is employed to decide when to take a position as well as what position out of long and short should be taken by monitoring whether VKOSPI or difference of VKOSPI touches the control limit lines. The strategies include 4 approaches, which are traditional control chart and 2-Area control chart coupled with VKOSPI and its difference, respectively. Computational experiments using real KOSPI200 futures index for recent 3 years are conducted to show the excellence of the proposed investment strategies under control chart framework.

• 산업경영시스템학회지
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• 제24권67호
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• pp.27-36
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• 2001

A standard assumption when using a control chart to monitor a process is that the observations from the process output are statistically independent. However, for many processes the observations are autocorrelated and this autocorrelation can have a significant effect on the performance of the control chart. In this paper, we consider combined control chart of monitoring the mean of a process in which the observations can be modeled as a first-order autoregressive process. The Shewhart control chart of residuals-EWMA control chart of the observations is considered and the method of combination is recommended. The performance of the proposed control chart is compared with the performance of other control charts using a simulation.

• 산업경영시스템학회지
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• 제23권56호
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• pp.83-92
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• 2000

This paper presents a study on control schemes for gradual increases (drifts) in a process variance. A new control chart, the Drifting Variance Control Chart (DVCC) is designed using Likelihood Ratio Test (LRT), and the ARL performance of the chart is evaluated for different subgroup sizes. The performance of this chart is then compared to some of the popular control schemes for the process dispersion, like the Shewhart S$^2$chart, the CUSUM chart and the EWMA chart. Results are presented and discussed. Also included is a sensitivity analysis that investigates how the DVCC performs when applied to a stepped change in process variance.

• 산업경영시스템학회지
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• 제35권4호
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• pp.118-125
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• 2012

When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called 'adjacent point operation', becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

• 품질경영학회지
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• 제38권3호
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• pp.313-321
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• 2010

The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from each other. Recently, the CV control chart is developed for monitoring processes in such situations. However, the CV control chart has low performance in detecting small shift. Due to the development of equipment and technique, currently, small shift of process occurs more frequently than large shift. In this paper, we proposes the CV-CUSUM control chart using CUSUM scheme which is cumulative sum of the deviations between each data point and a target value to detect a small shift in the process. We also found that the FIR(fast initial response) CUSUM control chart is especially valuable at start-up or after a CV-CUSUM control chart has signaled out-of-control.

• 품질경영학회지
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• 제35권1호
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• pp.24-34
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• 2007

This paper investigates the average run length (ARL) of a selectively moving average (S-MA) control chart. The S-U chart is designed to detect shifts in the process mean. The basic idea of the S-MA chart is to accumulate previous samples selectively in order to increase the sensitivity. The ARL of the S-MA chart was shown to be monotone decreasing with respect to the decision length in a previous research [3]. This paper derives the steady-state ARL in a closed-form and shows that the monotone property is resulted from head-start assumption. The steady-state ARL is shown to be a sum of head-start ARL and an additional term. The statistical design procedure for the S-MA chart is revised according to this result. Sensitivity study shorts that the steady-state ARL performance is still better than the CUSUM chart or the Exponentially Weighted Moving Average (EWMA) chart.

• 산업경영시스템학회지
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• 제15권26호
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• pp.137-150
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• 1992

As a part of quality control activities for developing competitive products, the significant method of process quality assurance for solving initial production quality problems is just quality control process chart(QC process chart). However, the QC process chart which is used for domestic enterprises at present had obscured in basement and not itemized by industry and formally used. So. in this study, the improved QC process charts which classified by industry we suggested so that each enterprise may utilize them according to the each enterprise characteristics.

• 산업경영시스템학회지
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• 제24권67호
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• pp.11-18
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• 2001

The efficiency and robustness of the scale estimator based on the Gini's mean difference are well known in Nam et al.(2000). In this paper we propose use of robust control limits based on the Gini's mean difference for the control of the process deviation. To compare the performances of the proposed control chart with the existing R-chart or S-chart, some Monte Carlo simulations are performed. The simulation results show that the use of the Gini's mean difference in construction of the control limits has good performance.

• 산업경영시스템학회지
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• 제16권28호
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• pp.93-101
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• 1993

In this paper, two adaptive exponentially weighted moving avenge control chart schemes which available for real-time are proposed. The weighting coefficient is estimated using a recursive kalman filter algorithm. Simulated average run lengths indicate the proposed schemes are sensitive to process shifts And their performance is comparable to CUSUM control chart and customary EWMA control chart.