Fast Initial Response Control Charts For Accounting Activities

Although Six Sigma was developed to improve processes in a manufacturing environment, its use has expanded to many other areas including accounting and finance.  We propose that control charts, originally used as tools for monitoring short-run manufacturing processes, can be effectively used in the Control Stage of Six Sigma projects designed to improve accounting processes with sparse data.  We describe four of these control charts: (1) pre-control charts; (2) Shewhart control charts with dramatically reduced average run lengths (ARLs); (3) Cumulative Sum (CUSUM) control charts with fast initial response (FIR) enhancements; and (4) Exponentially Weighted Moving Averages (EWMA) control charts with FIR enhancements.  We provide examples of FIR enhancements to CUSUM and EWMA control charts that can result in quicker detection of small shifts in the mean of accounting data

Effect of non-compliance with the normality hypothesis on the mean control charts

Los gráficos de control son ampliamente usados para monitorizar la calidad de procesos industriales. Tradicionalmente se asume que la variable aleatoria que representa la característica de calidad se distribuye de forma normal y los límites de control se definen de forma que la probabilidad de obtener una falsa alarma es 0.0027. Sin embargo, en la práctica la característica de calidad podría seguir otra distribución y este hecho podría afectar a la eficiencia del gráfico de control. En el presente trabajo se realiza un estudio de simulación Monte Carlo con el objetivo de evaluar empíricamente el impacto del incumplimiento del supuesto de normalidad en el gráfico de control para la media. Se consideran distintas distribuciones probabilísticas para analizar diferentes grados de incumplimiento. Adicionalmente, se han considerado situaciones en los que el proceso está bajo control y fuera de control. Los resultados sugieren que los gráficos de control son una herramienta efectiva cuando la distribución de la característica de calidad tiene una leve asimetría. Sin embargo, para obtener una efectividad similar a la obtenida bajo normalidad es necesario aumentar levemente el número de muestras o el tamaño de las mismas. En el caso de que la característica de calidad siga una distribución con un grado de asimetría mayor es necesario aumentar los tamaños muestrales para obtener resultados aceptables. Por último, no es recomendable utilizar los gráficos de control en situaciones extremas de falta de normalidad.Control charts are widely used to monitor the quality of industrial processes. It is quite common to assume that the random variable associated to the quality characteristic has a Normal distribution, and the control limits are defined so that the probability of obtaining a false alarm is 0.0027. However, the quality characteristic could follow a different distribution in practice, and this fact could have an impact on the efficiency of the control chart.In this paper, a Monte Carlo simulation study is carried out to evaluate empirically the impact of the lack of the normality assumption on the control chart for the mean. Different probabilistic distributions are considered. In addition, under control and out of control processes are considered.The results derived from the simulation study suggest that control charts are an effective tool when the distribution of the quality characteristic is slightly asymmetric. However, a large number of samples or larger sample sizes are required to obtain similar results to the case of symmetric distributions. In the case of asymmetric distributions, it is necessary to increase the sample sizes to obtain acceptable results. Finally, control charts are not recommended under evident cases of non-normality.Universidad Pablo de Olavid

The Mixed CUSUM-EWMA (MCE) control chart as a new alternative in the monitoring of a manufacturing process

Goal: The objective is to conclude, based on a comparative study, if there is a significant difference in sensitivity between the application of MCE and the individual application of the CUSUM or EWMA chart, i.e., greater sensitivity particularly for cases of lesser magnitude of change. Design/Methodology/Approach: These are an applied research and statistical techniques such as statistical control charts are used for monitoring variability. Results: The results show that the MCE chart signals a process out of statistical control, while individual EWMA and CUSUM charts does not detect any situation out of statistical control for the data analyzed. Limitations: This article is dedicated to measurable variables and individual analysis of quality characteristics, without investing in attribute variables. The MCE chart was applied to items that are essential to the productive process development being analysed. Practical Implications: The practical implications of this study can contribute to: the correct choice of more sensitive control charts to detect mainly small changes in the location (mean) of processes; provide clear and accurate information about the fundamental procedures for the implementation of statistical quality control; and encourage the use of this quality improvement tool. Originality/Value: The MCE control chart is a great differential for the improvement of the quality process of the studied company because it goes beyond what CUSUM and EWMA control charts can identify in terms of variability

On the performance of Shewhart-type synthetic and runs-rules charts combined with an X chart

Please read abstract in the article.Part of this work was supported by the SARCHI Chair at the University of Pretoria. Sandile Shongwe’s research was supported by National Research Foundation (NRF) and Department of Science and Technology’s Innovation Doctoral Scholarship (SFH14081591713) and Marien Graham’s research was supported in part by the NRF’s (Thuthuka program: TTK14061168807; grant number: 94102).http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-16382017-06-30hb2016Statistic

A modified side-sensitive synthetic chart to monitor the process mean

Please read abstract in the article.Part of this work was supported by the SARCHI Chair at the University of Pretoria. Sandile Shongwe‟s research was supported in part by the National Research Foundation and Department of Science and Technology‟s Innovation Doctoral scholarship (SFH14081591713) and Marien Graham‟s research was supported in part by the National Research Foundation (Thuthuka programme: TTK20100724000013247, Grant number: 76219).2018-01-31hb2016Statistic