Marit Schoonhoven

Design and analysis of control charts for standard deviation with estimated parameters

This paper concerns the design and analysis of the standard deviation control chart with estimated limits. We consider an extensive range of statistics to estimate the in-control standard deviation (Phase I) and design the control chart for real-time process monitoring (Phase II) by determining the factors for the control limits. The Phase II performance of the design schemes is assessed when the Phase I data are uncontaminated and normally distributed as well as when the Phase I data are contaminated. We propose a robust estimation method based on the mean absolute deviation from the median supplemented with a simple screening method. It turns out that this approach is efficient under normality and performs substantially better than the traditional estimators and several robust proposals when contaminations are present.

A Robust Standard Deviation Control Chart

This article studies the robustness of Phase I estimators for the standard deviation control chart. A Phase I estimator should be efficient in the absence of contaminations and resistant to disturbances. Most of the robust estimators proposed in the literature are robust against either diffuse disturbances, that is, outliers spread over the subgroups, or localized disturbances, which affect an entire subgroup. In this article, we compare various robust standard deviation estimators and propose an algorithm that is robust against both types of disturbances. The algorithm is intuitive and is the best estimator in terms of overall performance. We also study the effect of using robust estimators from Phase I on Phase II control chart performance. Additional results for this article are available online as Supplementary Material.

Design schemes for the X control chart

This paper studies several issues regarding the design of the X control chart under normality. Different estimators of the standard deviation are considered and the effect of the estimator on the performance of the control chart is investigated. Furthermore, the choice of the factor used to get accurate control limits for moderate sample sizes is addressed. The paper gives an overview on the performance of the charts by studying different characteristics of the run length distribution, both in the in-control and in the out-of-control situation.

The x-bar control chart under non-normality

This paper studies design schemes for the control chart under non-normality. Different estimators of the standard deviation are considered and the effect of the estimator on the performance of the control chart under non-normality is investigated. Two situations are distinguished. In the first situation, the effect of non-normality on the control chart is investigated by using the control limits based on normality. In the second situation we incorporate the knowledge of non-normality to correct the limits of the control chart. The schemes are evaluated by studying the characteristics of the in-control and the out-of-control run length distributions. The results indicate that when the control limits based on normality are applied the best estimator is the pooled sample standard deviation both under normality and under non-normality. When the control limits are corrected for non-normality, the estimator based on Ginis mean sample differences is the best choice.

Improving processes in financial service organizations: where to begin?

Purpose – The purpose of this paper is to create actionable knowledge, thereby supporting and stimulating practitioners to improve processes in the financial services sector.Design/methodology/approach – This paper is based on a case base of improvement projects in financial service organizations. The data consist of 181 improvement projects of processes in 14 financial service organizations executed between 2004 and 2010. Following the case‐based reasoning approach, based on retrospective analysis of the documentation of these improvement projects, this paper aims to structure this knowledge in a way that supports practitioners in defining improvement projects in their own organizations.Findings – Identification of eight generic project definition templates, along with their critical to quality flowdowns and operational definitions. An overview of the distribution of improvement projects of each generic template over different departments and the average benefit per project for each department. The gener...

A Robust X¯ Control Chart

This article studies alternative standard deviation estimators that serve as a basis to determine the control chart limits used for real-time process monitoring (phase II). Several existing (robust) estimation methods are considered. In addition, we propose a new estimation method based on a phase I analysis, that is, the use of a control chart to identify disturbances in a data set retrospectively. The method constructs a phase I control chart derived from the trimmed mean of the sample interquartile ranges, which is used to identify out-of-control data. An efficient estimator, namely the mean of the sample standard deviations, is used to obtain the final standard deviation estimate from the remaining data. The estimation methods are evaluated in terms of their mean squared errors and their effects on the performance of the phase II control chart. It is shown that the newly proposed estimation method is efficient under normality and performs substantially better than standard methods when disturbances are present in phase I. Copyright

Guaranteed In-Control Performance for the Shewhart X and X Control Charts

ABSTRACT Recently, two methods have been published in this journal to determine adjusted control limits for the Shewhart control chart in order to guarantee a pre-specified in-control performance. One is based on the bootstrap approach (Saleh et al. (2015)), and the other is an analytical approach (Goedhart, Schoonhoven, and Does (2017)). Although both methods lead to the desired control chart performance, they are still difficult to implement by the practitioner. The bootstrap is rather computationally intensive, while the analytical approach consists of multiple integrals and derivatives. In this letter to the editor we simplify the analytical expressions provided in Goedhart, Schoonhoven, and Does (2017) by using the theory on tolerance intervals for individual observations as given in Krishnamoorthy and Mathew (2009).

A robust estimator for location in Phase I based on an EWMA chart

In practice, a control chart for process monitoring (Phase II) is based on parameters estimated from data collected on the process characteristic under study (Phase I). The Phase I data could contain unacceptable data, which in turn could affect the monitoring. In this study, we consider various estimation methods that are potentially relevant within the parameter estimation process. The quality of the Phase I study is evaluated in terms of the precision of the resulting estimates as well as the effectiveness of the exploratory data analysis, where ‘effectiveness’ is measured by the proportion of observations that are correctly identified as unacceptable. Moreover, we study the impact of the Phase I estimation method on the performance of the EWMA control chart in Phase II.

Quality quandaries: Streamlining the path to optimal care for cardiovascular patients

The Quality Quandaries column highlights a lean Six Sigma improvement project in a Netherlands hospitals cardiology department.

Shewhart control charts for dispersion adjusted for parameter estimation

ABSTRACT Several recent studies have shown that the number of Phase I samples required for a Phase II control chart with estimated parameters to perform properly may be prohibitively high. Looking for a more practical alternative, adjusting the control limits has been considered in the literature. We consider this problem for the classic Shewhart charts for process dispersion under normality and present an analytical method to determine the adjusted control limits. Furthermore, we examine the performance of the resulting chart at signaling increases in the process dispersion. The proposed adjustment ensures that a minimum in-control performance of the control chart is guaranteed with a specified probability. This performance is indicated in terms of the false alarm rate or, equivalently, the in-control average run length. We also discuss the tradeoff between the in-control and out-of-control performance. Since our adjustment is based on exact analytical derivations, the recently suggested bootstrap method is no longer necessary. A real-life example is provided in order to illustrate the proposed methodology.