The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Charles F. Bethea - One of the best experts on this subject based on the ideXlab platform.
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The use of patient mix‐adjusted control charts to compare in‐hospital costs of care
Health Care Management Science, 1999Co-Authors: Eric L. Eisenstein, Charles F. BetheaAbstract:We introduce a technique for patient mix‐adjusting $$\bar x$$ charts and compared differences between unadjusted and patient mix‐adjusted results. Our data came from coronary artery bypass graft (CABG) surgery patients at Baptist Medical Center, Oklahoma City, Oklahoma. We first developed an unadjusted $$\bar x$$ control chart to compare monthly changes in CABG surgery costs and then used a published model to patient mix‐adjust our $$\bar x$$ control chart information. Before adjustment, the average log costs for three of ten months were outside the 90% control limit lines, and there was a trend toward increasing costs. After adjustment, two months had average costs outside the 90% lower control limit lines, and the trend toward increasing costs had been explained by differences in patient acuity.
Yuan Cheng - One of the best experts on this subject based on the ideXlab platform.
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Phase II synthetic exponential charts and effect of parameter estimation
Quality Technology and Quantitative Management, 2017Co-Authors: Yuan ChengAbstract:Exponential charts for monitoring time-between-events data are widely studied recently as it has shown a wide range in different applications. To improve the sensitivity of the simple exponential chart, this paper develops a synthcontrol chart to monitor the two-sided shifts in the exponential mean. This synthetic chart consists of an exponential sub-chart and a conforming run length (CRL) sub-chart and it is designed from two new perspectives. Previous synthetic charts are constructed based on the optimization program for a specified shift size, which proves to be difficult to predict in practice. In this paper, the synthetic chart is constructed based on a new design method which takes the mean (ARL) and the standard deviation (SDRL) of the run length into consideration. The other improvement is that the exponential sub-chart is designed to ensure that the CRL sub-chart only needs a lower control limit and that the proposed synthetic chart is ARL-unbiased. The effect of parameter estimation on the propo...
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Optimal design of a synthetic chart for monitoring process dispersion with unknown in-control variance
Computers & Industrial Engineering, 2015Co-Authors: Bing Xing Wang, Yuan ChengAbstract:We propose synthetic charts for monitoring process dispersion.The proposed scheme is for the two-sided shifts in process dispersion.The proposed scheme can effectively detect the changes in process dispersion.Tables are provided for the statistical properties of the proposed scheme. For a stable manufacturing process, quality problems are often caused by changes in process dispersion. Although there have been plenty of research on the monitoring of process dispersion, the existing studies of synthetic charts for monitoring process dispersion only focus on the upward shift monitoring. However, the decrease shift monitoring is also necessary and important. In this paper, a synthetic S 2 chart is proposed to simultaneously monitor both upward and downward shifts and it consists of a Shewhart-type two-sided S 2 sub-chart and a conforming run length sub-chart. In the known in-control variance case, the conforming run length sub-chart only needs a lower control limit and the proposed synthetic S 2 chart is shown to be average run length (ARL) unbiased. The effect of parameter estimation on the proposed synthetic S 2 chart is also investigated as it is an important issue especially in the real manufacturing processes. Considering that the in-control variance is usually unknown and needs to be estimated by Phase I samples in practice, a new synthetic S 2 chart in which conforming run length sub-chart also only needs a lower control limit is developed when the in-control variance is estimated. Furthermore, optimal designs for both known and unknown parameter cases are studied. The advantage of the proposed chart in performance is shown in the results of the comparison with the ARL-unbiased S 2 chart. Also, an example illustrates the construction and application procedure of this proposed chart.
Eric L. Eisenstein - One of the best experts on this subject based on the ideXlab platform.
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The use of patient mix‐adjusted control charts to compare in‐hospital costs of care
Health Care Management Science, 1999Co-Authors: Eric L. Eisenstein, Charles F. BetheaAbstract:We introduce a technique for patient mix‐adjusting $$\bar x$$ charts and compared differences between unadjusted and patient mix‐adjusted results. Our data came from coronary artery bypass graft (CABG) surgery patients at Baptist Medical Center, Oklahoma City, Oklahoma. We first developed an unadjusted $$\bar x$$ control chart to compare monthly changes in CABG surgery costs and then used a published model to patient mix‐adjust our $$\bar x$$ control chart information. Before adjustment, the average log costs for three of ten months were outside the 90% control limit lines, and there was a trend toward increasing costs. After adjustment, two months had average costs outside the 90% lower control limit lines, and the trend toward increasing costs had been explained by differences in patient acuity.
Stephen M. Pollock - One of the best experts on this subject based on the ideXlab platform.
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Process Adjustment for Assemblies
International Series in Operations Research & Management Science, 2003Co-Authors: David W. Glenn, Stephen M. PollockAbstract:After the design and construction of manufacturing tools, adjustment is often necessary to reduce mean deviation from target of product dimensions. We formulate a stochastic dynamic program to find the optimal adjustment policy when the adjustment outcome is uncertain. The optimal policy minimizes the sum of adjustment cost during pre-production and quality cost during production. The formulation is developed for a single process and for two processes producing components for assembly. In the single process case, sufficient conditions are given for the optimal policy to have an upper and lower control limit form. In the two process case, we compare separate and combined policies and give an example where appreciable savings can be realized from a combined policy.
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PROCESS ADJUSTMENT FOR ASSEMBLIES Should Components, or Assemblies, Be Made to Specifications?
2003Co-Authors: David W. Glenn, Stephen M. PollockAbstract:After the design and construction of manufacturing tools, adjustment is often necessary to reduce mean deviation from target of product dimensions. We for mulate a stochastic dynamic program to find the optimal adjustment policy when the adjustment outcome is uncertain. The optimal policy minimizes the sum of adjustment cost during pre-production and quality cost during production. The formulation is developed for a single process and for two processes producing components for assembly. In the single process case, sufficient conditions are given for the optimal policy to have an upper and lower control limit form. In the two process case, we compare separate and combined policies and give an example where appreciable savings can be realized from a combined policy.
Erwin M. Saniga - One of the best experts on this subject based on the ideXlab platform.
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Statistical Design of Attribute Charts for Monitoring and Continuous Improvement When Count Levels Are Low
Frontiers in Statistical Quality Control 7, 2020Co-Authors: Erwin M. Saniga, Darwin J. Davis, James M. LucasAbstract:Consider the situation where an attribute chart is being used to monitor a process for special causes of variability and attempts at continuous improvement are being made. In certain cases where the count level is low no lower control limit will exist for standard Shewhart control charts.
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detecting improvement using shewhart attribute control charts when the lower control limit is zero
Iie Transactions, 2006Co-Authors: James M. Lucas, Darwin J. Davis, Erwin M. SanigaAbstract:In this paper, we present a method to monitor count data so as to be able to detect improvement when the counts are low enough to cause the lower limit to be zero. The method, which is proposed as an add-on to the conventional Shewhart control chart, consists in counting the number of samples in which zero defectives or zero defects per unit occur and signaling an increase in quality if k-in-a-row or 2-in-t samples have zero counts of defectives or zero defects per unit. This method enjoys some similarities to the very popular Shewhart control chart in that it is easy to design, understand and use. It is flexible, robust, and, like the Shewhart chart, yields detection frequencies that are optimal for very large shifts and good for other shifts. Some comparisons with traditional CUSUM charts are provided. Figures enabling Shewhart control chart users to easily design low-side add-on control charts are given for c and np charts.
