The Red Cross Hospital is a medium-sized general hospital with 385 beds, located in Beverwijk, The Netherlands. It also has a freestanding National Burn Care Centre. The Red Cross Hospital was the first hospital in The Netherlands with a quality system based on ISO 9000. At the end of 2001 the hospital started implementing Six Sigma. The process began with Executive Training for management and Green Belt (GB) training for 16 middle managers and other staff. Seven GB projects were started in the areas of accounts receivable, patient logistics, invoicing, medication, temporary workers, and length of stay in hospital. In February 2003 the final review of the first group was done and savings appeared to be three times higher than estimated beforehand. At present (May 2004) the fourth group of Green Belts has been trained. In this paper we briefly explain that Six Sigma was the next logical step in the quality approach of the hospital. We also discuss how it was implemented and we describe some case studies in the nursing departments.

Several control charts for individual observations are compared. Traditional ones are the well-known Shewhart individuals control charts based on moving ranges. Alternative ones are non-parametric control charts based on empirical quantiles, on kernel estimators, and on extreme-value theory. Their in-control and out-of-control performance are studied by simulation combined with computation. It turns out that the alternative control charts are not only quite robust against deviations from normality but also perform reasonably well under normality of the observations. The performance of the Empirical Quantile control chart is excellent for all distributions considered, if the Phase I sample is sufficiently large.

Today's manufacturing environment has changed since the time when control chart methods were originally introduced. Sequentially observed data are much more common. Serial correlation can seriously affect the performance of the traditional control charts. In this article we derive explicit easy-to-use expressions of the variance of an EWMA statistic when the process observations are autoregressive of order 1 or 2. These variances can be used to modify the control limits of the corresponding EWMA control charts. The resulting control charts have the advantage that the data are plotted on the original scale making the charts easier to interpret for practitioners than charts based on residuals.

No abstract

General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. AbstractSerial correlation can seriously affect the performance of traditional control charts. Many authors have studied the effect of autocorrelation on EWMA control charts and have shown how to modify the control limits to account for autocorrelation. In this paper we compare three different estimation methods for the variance of the EWMA statistic that is adapted to autocorrelated data. This comparison is based on the asymptotic relative efficiency of the estimators.