A stochastic shift model for economically designed charts constrained by the process stage configuration

An economic model including the labor resource and the process stage configuration is proposed to design g charts allowing for all the design parameters to be varied in an adaptive way. A random shift size is considered during the economic design selection. The results obtained for a benchmark of 64 process stage scenarios show that the activities configuration and some process operating parameters influence the selection of the best control chart strategy: to model the random shift size, its exact distribution can be approximately fitted by a discrete distribution obtained from a relatively small sample of historical data. However, an accurate estimation of the inspection costs associated to the SPC activities is far from being achieved. An illustrative example shows the implementation of the proposed economic model in a real industrial case. (C) 2011 Elsevier B.V. All rights reserved.

Monitoring the process mean and variance using a synthetic control chart with two-stage testing

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

X̄ charts with variable sample size

The usual practice in using a control chart to monitor a process is to take samples of size n from the process every h hours. This article considers the properties of the X̄ chart when the size of each sample depends on what is observed in the preceding sample. The idea is that the sample should be large if the sample point of the preceding sample is close to but not actually outside the control limits and small if the sample point is close to the target. The properties of the variable sample size (VSS) X̄ chart are obtained using Markov chains. The VSS X̄ chart is substantially quicker than the traditional X̄ chart in detecting moderate shifts in the process.

VSSI X̄ charts with sampling at fixed times

A standard X̄ chart for controlling the process mean takes samples of size n0 at specified, equally-spaced, fixed-time points. This article proposes a modification of the standard X chart that allows one to take additional samples, bigger than n0, between these fixed times. The additional samples are taken from the process when there is evidence that the process mean moved from target. Following the notation proposed by Reynolds (1996a) and Costa (1997) we shortly call the proposed X chart as VSSIFT X chart where VSSIFT means variable sample size and sampling intervals with fixed times. The X chart with the VSSIFT feature is easier to be administered than a standard VSSI X chart that is not constrained to sample at the specified fixed times. The performances of the charts in detecting process mean shifts are comparable. Copyright © 1998 by Marcel Dekker, Inc.

X̄ Charts with Variable Parameters

Varying the parameters of the X̄ chart has been explored extensively in recent years. In this paper, we extend the study of the X̄ chart with variable parameters to include variable action limits. The action limits establish whether the control should be relaxed or not. When the X̄ falls near the target, the control is relaxed so that there will be more time before the next sample and/or the next sample will be smaller than usual. When the X̄ falls far from the target but not in the action region, the control is tightened so that there is less time before the next sample and/or the next sample will be larger than usual. The goal is to draw the action limits wider than usual when the control is relaxed and narrower than usual when the control is tightened. This new feature then makes the X̄ chart more powerful than the CUSUM scheme in detecting shifts in the process mean.

A synthetic control chart for monitoring the process mean and variance

Purpose - The aim of this paper is to present a synthetic chart based on the non-central chi-square statistic that is operationally simpler and more effective than the joint X̄ and R chart in detecting assignable cause(s). This chart will assist in identifying which (mean or variance) changed due to the occurrence of the assignable causes. Design/methodology/approach - The approach used is based on the non-central chi-square statistic and the steady-state average run length (ARL) of the developed chart is evaluated using a Markov chain model. Findings - The proposed chart always detects process disturbances faster than the joint X̄ and R charts. The developed chart can monitor the process instead of looking at two charts separately. Originality/value - The most important advantage of using the proposed chart is that practitioners can monitor the process by looking at only one chart instead of looking at two charts separately. © Emerald Group Publishing Limted.

Preliminary results concerning the VSS X- chart with unknow in-control parameters

The VSS X- chart is known to perform better than the traditional X- control chart in detecting small to moderate mean shifts in the process. Many researchers have used this chart in order to detect a process mean shift under the assumption of known parameters. However, in practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS X- control chart when the process parameters are estimated and we compare them in the case where the process parameters are assumed known. We draw the conclusion that these performances are quite different when the shift and the number of samples used during the phase I are small. ©2010 IEEE.

Attribute charts for monitoring the mean vector of bivariate processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Joint X̄ and R charts with variable parameters

Recent studies have shown that the X̄chart with variable parameters (Vp X̄ chart) detects process shifts faster than the traditional X̄ chart. This article extends these studies for processes that are monitored by both, X̄ and R charts. Basically, the X̄ and R values establish if the control should or should not be relaxed. When the X̄ and R values fall in the central region the control is relaxed because one will wait more to take the next sample and/or the next sample will be smaller than usual. When the X̄ or R values fall in the warning region the control is tightened because one will wait less to take the next sample and the next sample will be larger than usual. The action limits are also made variable. This paper proposes to draw the action limits (for both charts) wider than usual, when the control is relaxed and narrower than usual when the control is tightened. The Vp feature improves the joint X̄ and R control chart performance in terms of the speed with which the process mean and/or variance shifts are detected. © 1998 IIE.

Quality Control in the Healthcare Industry: An Analysis of Surgery Turnover Times

For virtually all hospitals, utilization rates are a critical managerial indicator of efficiency and are determined in part by turnover time. Turnover time is defined as the time elapsed between surgeries, during which the operating room is cleaned and preparedfor the next surgery. Lengthier turnover times result in lower utilization rates, thereby hindering hospitals’ ability to maximize the numbers of patients that can be attended to. In this thesis, we analyze operating room data from a two year period provided byEvangelical Community Hospital in Lewisburg, Pennsylvania, to understand the variability of the turnover process. From the recorded data provided, we derive our best estimation of turnover time. Recognizing the importance of being able to properly modelturnover times in order to improve the accuracy of scheduling, we seek to fit distributions to the set of turnover times. We find that log-normal and log-logistic distributions are well-suited to turnover times, although further research must validate this finding. Wepropose that the choice of distribution depends on the hospital and, as a result, a hospital must choose whether to use the log-normal or the log-logistic distribution. Next, we use statistical tests to identify variables that may potentially influence turnover time. We find that there does not appear to be a correlation between surgerytime and turnover time across doctors. However, there are statistically significant differences between the mean turnover times across doctors. The final component of our research entails analyzing and explaining the benefits of introducing control charts as a quality control mechanism for monitoring turnover times in hospitals. Although widely instituted in other industries, control charts are notwidely adopted in healthcare environments, despite their potential benefits. A major component of our work is the development of control charts to monitor the stability of turnover times. These charts can be easily instituted in hospitals to reduce the variabilityof turnover times. Overall, our analysis uses operations research techniques to analyze turnover times and identify manners for improvement in lowering the mean turnover time and thevariability in turnover times. We provide valuable insight into a component of the surgery process that has received little attention, but can significantly affect utilization rates in hospitals. Most critically, an ability to more accurately predict turnover timesand a better understanding of the sources of variability can result in improved scheduling and heightened hospital staff and patient satisfaction. We hope that our findings can apply to many other hospital settings.

Control estadístico de calidad en bibliotecología : Aplicación de gráficos de control en los procesos técnicos

A partir de la aceptación de la biblioteca como parte del ciclo de creación, organización y diseminación del conocimiento cambió el concepto de la misma de una entidad cerrada hacia un sistema dinámico en constante interacción con su entorno. Así se la reconoció como una institución social más que como una colección de documentos. Desde entonces se percibió a la biblioteca como una entidad en la que se podía aplicar los principios de gestión. Desde entonces se utilizaron distintas herramientas de gestión para la toma de decisiones en el ámbito de las bibliotecas. Entre estas herramientas son de gran importancia en el control estadístico de procesos los gráficos de control, utilizados para medir la estabilidad de un proceso a través del tiempo. Han tenido amplia aplicación en el control estadístico de la calidad, comenzando en el ámbito industrial. Hoy su aplicación se ha extendido a una gran variedad de disciplinas incluyen empresas de servicios y unidades administrativas. Aquí se presentan a los gráficos de control como una importante herramienta de gestión aplicada a los procesos técnicos permitiendo su evaluación y el monitoreo de su desempeño a partir de la utilización de indicadores y otros datos de carácter diagnóstico

Control estadístico de calidad en bibliotecología : Aplicación de gráficos de control en los procesos técnicos

A partir de la aceptación de la biblioteca como parte del ciclo de creación, organización y diseminación del conocimiento cambió el concepto de la misma de una entidad cerrada hacia un sistema dinámico en constante interacción con su entorno. Así se la reconoció como una institución social más que como una colección de documentos. Desde entonces se percibió a la biblioteca como una entidad en la que se podía aplicar los principios de gestión. Desde entonces se utilizaron distintas herramientas de gestión para la toma de decisiones en el ámbito de las bibliotecas. Entre estas herramientas son de gran importancia en el control estadístico de procesos los gráficos de control, utilizados para medir la estabilidad de un proceso a través del tiempo. Han tenido amplia aplicación en el control estadístico de la calidad, comenzando en el ámbito industrial. Hoy su aplicación se ha extendido a una gran variedad de disciplinas incluyen empresas de servicios y unidades administrativas. Aquí se presentan a los gráficos de control como una importante herramienta de gestión aplicada a los procesos técnicos permitiendo su evaluación y el monitoreo de su desempeño a partir de la utilización de indicadores y otros datos de carácter diagnóstico

Control estadístico de calidad en bibliotecología : Aplicación de gráficos de control en los procesos técnicos

A partir de la aceptación de la biblioteca como parte del ciclo de creación, organización y diseminación del conocimiento cambió el concepto de la misma de una entidad cerrada hacia un sistema dinámico en constante interacción con su entorno. Así se la reconoció como una institución social más que como una colección de documentos. Desde entonces se percibió a la biblioteca como una entidad en la que se podía aplicar los principios de gestión. Desde entonces se utilizaron distintas herramientas de gestión para la toma de decisiones en el ámbito de las bibliotecas. Entre estas herramientas son de gran importancia en el control estadístico de procesos los gráficos de control, utilizados para medir la estabilidad de un proceso a través del tiempo. Han tenido amplia aplicación en el control estadístico de la calidad, comenzando en el ámbito industrial. Hoy su aplicación se ha extendido a una gran variedad de disciplinas incluyen empresas de servicios y unidades administrativas. Aquí se presentan a los gráficos de control como una importante herramienta de gestión aplicada a los procesos técnicos permitiendo su evaluación y el monitoreo de su desempeño a partir de la utilización de indicadores y otros datos de carácter diagnóstico

ARL1 of the Attribute c Control Chart with Estimated Parameter

International audience

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.