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The purpose of this paper is to discuss the causes for the discrepancy between the utilization of statistical process control (SPC) in services and manufacturing. Furthermore, an approach for integrating customer demands and technical aspects of a service is presented. Services are very often characterized by a large number of characteristics, with relatively few observations. This research addresses a methodology based on Z and W charts, proposing it for the control of service features. An example associated with service provision is presented to illustrate the computation of Z and W as well as its interpretation.

The shortcomings of traditional control charts are stated and compared with the control charts for Z and W. An example illustrates how to utilize these charts, their ability to monitor several characteristics simultaneously, along with a continuous monitoring of process capability.

The proposed approach allowed the representation of several process characteristics in the same charts, even when those characteristics are not collected with the same periodicity. The Z and W charts are dimensionless and can be applied whenever it is possible to estimate process parameters, being an interesting approach to be utilized in Phase 2 of SPC. The difficulty for identifying the existence of non-random patterns emerges as the major shortcoming for these charts.

The proposed approach is a contribute to overcoming the discrepancy that persists between the utilization of SPC in services and manufacturing. Nevertheless, service production and consumption are frequently simultaneous, which constitutes an issue hard to deal with that is not fully addressed in this piece of research. Furthermore, the Z charts also present some disadvantages, notably an increased difficulty for analyzing the existence of non-random patterns, which worsens as increases the number of products/quality characteristics to be checked.

The proposed charts are very flexible and provide a rational utilization of resources. In fact, the representation of several processes is possible, along with the traditional analysis of patterns, thus providing an effective approach for controlling services processes.

Several quantitative approaches that have been utilized in manufacturing for a long time are still scarce in services. However, services play a major role in modern economies, being clear that improvements in service provision might have a direct impact on society.

The approach was based on the utilization of Z/W with samples, but it can be extended to individual observations or even to the control of discrete variables. Additionally, a methodology for process capability analysis in real-time is also proposed.

The purpose of this paper is to develop an exponentially weighted moving average (EWMA) control chart based on the Wilcoxon rank-sum (WRS) statistic using repetitive sampling to improve the sensitivity of the EWMA control chart to process mean shifts regardless of the prior knowledge of the underlying process distribution.

The proposed chart is developed without any distributional assumption of the underlying quality process for monitoring the location parameter. The authors developed formulae as well as algorithms to facilitate the design and implementation of the proposed chart. The performance of the proposed chart is investigated in terms of the average run-length, standard deviation of the run-length (RL), average sample size and percentiles of the RL distribution. Numerical examples are given as illustration of the design and implementation of the proposed chart.

The proposed control chart presents very attractive RL properties and outperforms the existing nonparametric EWMA control chart based on the WRS in the detection of the mean process shifts in many situations. However, the performance of the proposed chart relatively deteriorates for small phase I sample sizes.

This study develops a new control chart for monitoring the process mean using a two-sample test regardless of the nature of the underlying process distribution. The proposed control chart does not require any assumption on the type (or nature) of the process distribution. It requires a small number of subgroups in order to reach stability in the phase II performance.

Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.

The BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.

The BN model outperforms adaptive np control charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.

The BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.

The BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

Megan’s Year: An Irish Traveler’s Story provides the reader with an in-depth look inside the life of a young girl growing up in Ireland. The author provides a realistic and a touching peek into lives of travelers through Megan’s eyes, showing both the joys as well as the personal and familial challenges of a mobile lifestyle. Through the insights of Megan intermediate students will be able to develop an understanding and appreciation of cultural diversity. Students will be able to make connections to others in their global community by getting to know Megan and seeing the different ways in which she views the world. In this short unit, students will participate in the use of readers’ theater, Venn diagrams, the Directed Reading-Thinking Activity (DR-TA) and the Know-Want to Know-Learned- Questions (KWLQ) strategy. As a culminating activity, the students will prepare a story comparing their lives to the life Megan experiences as an Irish Traveller.

The study aims to design a control chart based on an exponentially weighted moving average (EWMA) chart of Pearson's residuals of a model of negative binomial regression in order to detect possible anomalies in mortality data.

In order to evaluate the performance of the proposed chart, the authors have considered official historical records of death of children of Ecuador. A negative binomial regression model was fitted to the data, and a chart of the Pearson residuals was designed. The parameters of the chart were obtained by simulation, as well as the performances of the charts related to changes in the mean of death.

When the chart was plotted, outliers were detected in the deaths of children in the years 1990–1995, 2001–2006, 2013–2015, which could show that there are underreporting or an excessive growth in mortality. In the analysis of performances, the value of λ = 0.05 presented the fastest detection of changes in the mean death.

The proposed charts present better performances in relation to EWMA charts for deviance residuals, with a remarkable advantage of the Pearson residuals, which are much easier to interpret and calculate. Finally, the authors would like to point out that although this paper only applies control charts to Ecuadorian infant mortality, the methodology can be used to calculate mortality in any geographical area or to detect outbreaks of infectious diseases.

The purpose of this paper is to develop an extensive economic production quantity (EPQ) model on the basis of previous research. Considering an imperfect three‐state production process, this paper makes contributions to an integrated model combining conceptions of quality loss and design of control chart based on EPQ model. The objective is to minimize the total production cost with the determination of EPQ and design parameters of control chart subjected to quality loss and other process costs.

In this paper, imperfect process is defined as a three‐state process, and the quality cost corresponding to each state contributes to the eventual total expected cost formulation. Control chart is used to monitor the shift from the target value within whole process and its control limits are set to be related to the quality cost.

The proposed integrated model conforms more closely to the real situation of production process considering the process shift as a random variable.

Numerical computation and sensitivity analysis through a case study are presented to demonstrate the applications of the model.

Few research efforts investigate an integrated model considering EPQ, control chart and quality loss simultaneously. In particular, compared with the former researches, the process shift, due to which the quality cost incurs, is considered as a random variable in this paper.

“Twin metric” control preserves the simple, intuitive graphical features of Shewhart control charts, while incorporating the much improved performance of CUSUM. Two metrics are plotted on the twin metric control chart at each sample interval; the Shewhart value and a simplified CUSUM value. The action limits for the two metrics are numerically identical. The name twin metric emphasizes this identity. Twin metric responsiveness, measured in terms of the average run length (ARL) curve, is several times better than Shewhart control, with or without runs rules to supplement the Shewhart chart. Twin metric enables substantially better response to real process shifts and substantially fewer false alarms compared to Shewhart charts. Discusses the conceptual framework, the arithmetic formulas, and the operational aspects, including estimation of the process standard deviation, estimation of the current process average after a twin metric signal, and monitoring process variability using twin metric control. Provides a table of ARLs for six twin metric options. Gives quantitative performance comparisons comparing twin metric to Shewhart and to combined Shewhart‐CUSUM.

The purpose of this paper is to investigate the economic‐statistical design of EWMA charts with variable sampling intervals (VSIs) under non‐normality to reduce the process production cycle cost and improve the statistical performance of control charts. The objective is to minimize the cost function by adjusting the control chart parameters which suffice for the statistical restriction.

First, using the Burr distribution to approximate various non‐normal distributions, the economic‐statistical model of the VSI EWMA charts under non‐normality can be developed. Further, the genetic algorithms will be used to search for the optimal values of parameters of the VSI EWMA charts under non‐normality. Finally, a sensitivity analysis is carried out to investigate the effect of model parameters and statistical restriction on the solution of the economic‐statistical design.

The result of sensitivity analysis shows that a large lower bound of average time to signal when the process is in control increases the control limit coefficient, no model parameter significantly affects the short sampling intervals, and so on.

The economic‐statistical design method proposed in this paper can improve the statistical performance of economic design of control charts and the general idea can be applied to other VSI control charts.

Much research has recently been conducted into the use of models for the economic design of multiple control charts (EDCC). Control chart models generally assume that most process variables are constant and only a limited number of the major variables are varied to reach a local optimum. In the economic design of multiple control charts (EDMCC), multiple control charts are used to analyse many manufacturing process variables simultaneously, in order to produce an optimal design for process control. However, the large number of variables often makes it difficult to solve this optimisation problem manually. This research explores the proposition that EDMCC can be optimised by using a novel genetic algorithm which dynamically adjusts the genetic algorithm’s (GA) operator and parameter settings during operation to ensure optimum effectiveness. This method involves refining the chromosome structure and using orthogonal arrays with fuzzy reasoning to reduce the search space.

This paper presents economic and economic–statistical designs of the adaptive exponentially weighted moving average (AEWMA) control chart for monitoring the process mean. It also aims to compare the effect of estimated process parameters on the economic performance of three charts, which are Shewhart, exponentially weighted moving average and AEWMA control charts with economic–statistical design.

The optimal parameters of the control charts are obtained by applying the Lorenzen and Vance’s (1986) cost function. Comparisons between the economic–statistical and economic designs of the AEWMA control chart in terms of expected cost and statistical measures are performed. Also, comparisons are made between the economic performance of the three competing charts in terms of the average expected cost and standard deviation of expected cost.

This paper concludes that taking into account the economic factors and statistical properties in designing the AEWMA control chart leads to a slight increase in cost but in return the improvement in the statistical performance is substantial. In addition, under the estimated parameters case, the comparisons reveal that from the economic point of view the AEWMA chart is the most efficient chart when detecting shifts of different sizes.

The importance of the study stems from designing the AEWMA chart from both economic and statistical points of view because it has not been tackled before. In addition, this paper contributes to the literature by studying the effect of the estimated parameters on the performance of control charts with economic–statistical design.