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

Adaptive EWMA Control Charts with a Time Varying Smoothing Parameter

It is known that time-weighted charts like EWMA or CUSUM are designed to be optimal to detect a specific shift. If they are designed to detect, for instance, a very small shift, they can be inefficient to detect moderate or large shifts. In the literature, several alternatives have been proposed to circumvent this limitation, like the use of control charts with variable parameters or adaptive control charts. This paper has as main goal to propose some adaptive EWMA control charts (AEWMA) based on the assessment of a potential misadjustment, which is translated into a time-varying smoothing parameter. The resulting control charts can be seen as a smooth combination between Shewhart and EWMA control charts that can be efficient for a wide range of shifts. Markov chain procedures are established to analyze and design the proposed charts. Comparisons with other adaptive and traditional control charts show the advantages of the proposals.Acknowledgements: financial support from the Spanish Ministry of Education and Science, research project ECO2012-38442

An Examination of the Robustness to Non Normality of the EWMA Control Charts for the Dispersion

The EWMA control chart is used to detect small shifts in a process. It has been shown that, for certain values of the smoothing parameter, the EWMA chart for the mean is robust to non normality. In this article, we examine the case of non normality in the EWMA charts for the dispersion. It is shown that we can have an EWMA chart for dispersion robust to non normality when non normality is not extreme.Average run length, Control charts, Exponntially weighted moving average control chart, Median run length, Non normality, Statistical process control

Monitoring variance by EWMA charts with time varying smoothing parameter

Memory charts like EWMA-S² or CUSUM-S² can be designed to be optimal to detect a specific shift in the process variance. However, this feature could be a serious inconvenience since, for instance, if the charts are designed to detect small shift, then, they can be inefficient to detect moderate or large shifts. In the literature, several alternatives have been proposed to overcome this limitation, like the use of control charts with variable parameters or adaptive control charts. This paper proposes new adaptive EWMA control charts for the dispersion (AEWMA-S²) based on a timevarying smoothing parameter that takes into account the potential misadjustment in the process variance. The obtained control charts can be interpreted as a combination of EWMA control charts designed to be efficient for different shift values. Markov chain procedures are established to analyse and design the proposed charts. Comparisons with other adaptive and traditional control charts show the advantages of the proposals

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Joint economic design of EWMA control charts for mean and variance

Cataloged from PDF version of article.Control charts with exponentially weighted moving average (EWMA) statistics (mean and variance) are used to jointly monitor the mean and variance of a process. An EWMA cost minimization model is presented to design the joint control scheme based on pure economic or both economic and statistical performance criteria. The pure economic model is extended to the economic-statistical design by adding constraints associated with in-control and out-of-control average run lengths. The quality related production costs are calculated using Taguchi's quadratic loss function. The optimal values of smoothing constants, sampling interval, sample size, and control chart limits are determined by using a numerical search method. The average run length of the control scheme is computed by using the Markov chain approach. Computational study indicates that optimal sample sizes decrease as the magnitudes of shifts in mean and/or variance increase, and higher values of quality loss coefficient lead to shorter sampling intervals. The sensitivity analysis results regarding the effects of various inputs on the chart parameters provide useful guidelines for designing an EWMA-based process control scheme when there exists an assignable cause generating concurrent changes in process mean and variance. (C) 2006 Elsevier B.V. All rights reserved

The development of egg hatching and storage machines equipped with cooling and heating systems and iot

The development of egg hatching and storage machines equipped with cooling and heating systems and IoT was for helping chicken breeders to address the issue of chicken production shortages. To produce large numbers of poultry production, eggs hatching is one of the major step that needs to pay attention to. There are several reasons why egg hatching process fails, such as lack of care by hen, eaten by rooster, and unsuitable hatching environment and temperature. In addition, if the eggs are not incubated within 1 week, the eggs will be damaged having producing a hatching machine and egg storage can help the chicken breeders to produce a better amount of chicken production. Internet of Things (IoT) elements such as the Arduino and Blynk are also used to make this egg hatching and storage machine attractive and to meet the needs and requirements of users. The objectives of this study were to design, develop and evaluate the functionality of egg hatching and storage machines in combination with cooling and heating systems along with IoT. Methodology is a technique and method that incorporates methods and approaches used to achieve the objectives and objectives of the study. The model used is the ADDIE model which consists of 5 phases namely Analysis, Design, Development, Implementation, and Evaluation. This product has received expert confirmation in terms of design and functionality. The results show that the egg hatching and storage machine is well developed and can attract users when using this hatching and storage machine