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The double exponentially weighted moving average (DEWMA), which is known in the literature as Brown′s one‐parameter linear method for forecasting is proposed as a control tool for process monitoring and detecting shifts in the process mean. Obtains a closed‐form expression for the asymptotic standard deviation of the proposed DEWMA control statistic and discusses the determination of its average run length. Provides examples and comparisons between the proposed DEWMA and the standard EWMA. The results reveal that the proposed DEWMA control scheme performs much better than a Shewhart scheme for small and moderate shifts in the process mean and it has average run length properties similar to those for EWMA control schemes. However, DEWMA has smaller variability and it allows more smoothing of the data with no compromise in the sensitivity of detecting shifts in the process mean. It also shifts the range of the design parameters for optimal ARL to larger values as compared with EWMA schemes. Such properties are more desirable for some industrial processes.

The majority of quality control charts are employed for normally distributed data. In reality this assumption is not always valid, as an alternative the Burr distribution is considered here.

Having previously derived integral equations for the average run length, a key measure of the performance of a control chart, approximate solutions are derived using Gaussian quadrature.

Polynomials closely approximating the average run length for the three most popular control charts, using their usual parameterisation, are obtained.

This is an extension of the Burr distribution which is noted for its ability to fit numerous scenarios.

These charts are widely applicable within engineering, finance, medicine, environmental statistics and many other fields. These problems are typically said to fall in the domain of risk management. It is hoped that this paper will add to the body of practitioners already employing this technique.

Control charts are widely employed, however, applications are usually restricted to the normal distribution. This is the first time it has been applied to the Burr distribution and original polynomials derived for the average run length.

“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.

Explains that the shifts of a process may be classified into a set of modes (or classifications), each of which is incurred by an assignable cause. Presents an algorithm to determine the process shift mode and estimate the run length when an out‐of‐control status is signalled by the x‐ or s chart in statistical process control. The information regarding the process shift mode and run length is very useful for diagnosing the assignable cause correctly and promptly. The algorithm includes two stages. First, the process shift modes are established using the sample data acquired during an explorative run. Afterwards, whenever an out‐of‐control case is detected, Bayes’ rule is employed to determine the active process shift mode and estimate the run length. In simulation tests, the proposed algorithm attains a fairly high probability (around 0.85) of correctly determining the active process shift mode and estimating the run length.

This paper seeks to propose a univariate robust control chart for location and the necessary table of factors for computing the control limits and the central line as an alternative to the Shewhart X¯ control chart.

The proposed method is based on two robust estimators, namely, the sample median, MD, to estimate the process mean, μ, and the median absolute deviation from the sample median, MAD, to estimate the process standard deviation, σ. A numerical example was given and a simulation study was conducted in order to illustrate the performance of the proposed method and compare it with that of the traditional Shewhart X¯ control chart.

The proposed robust X¯_MDMAD control chart gives better performance than the traditional Shewhart X¯ control chart if the underlying distribution of chance causes is non‐normal. It has good properties for heavy‐tailed distribution functions and moderate sample sizes and it compares favorably with the traditional Shewhart X¯ control chart.

The most common statistical process control (SPC) tool is the traditional Shewhart X¯ control chart. The chart is used to monitor the process mean based on the assumption that the underlying distribution of the quality characteristic is normal and there is no major contamination due to outliers. The sample mean, X¯, and the sample standard deviation, S, are the most efficient location and scale estimators for the normal distribution often used to construct the X¯ control chart, but the sample mean, X¯, and the sample standard deviation, S, might not be the best choices when one or both assumptions are not met. Therefore, the need for alternatives to the X¯ control chart comes into play. The literature shows that the sample median, MD, and the median absolute deviation from the sample median, MAD, are indeed more resistant to departures from normality and the presence of outliers.

The purpose of this paper is to introduce a new design of the X¯ chart to catch smaller shifts in the process average as well as to maintain the simplicity like the Shewhart X¯ chart so that it may be applied at shopfloor level.

In this paper, a new X¯ chart with two strategies is proposed which can overcome the limitations of Shewhart, CUSUM and EWMA charts. The Shewhart chart uses only two control limits to arrive at a decision to accept the Null Hypothesis (H₀) or Alternative Hypothesis (H₁), but in the new X¯ chart, two more limits at “K” times sample standard deviation on both sides from center line have been introduced. These limits are termed warning limits. The first strategy is based on chi‐square distribution (CSQ), while the second strategy is based on the average of sample means (ASM).

The proposed X¯ chart with “strategy ASM” shows lower average run length (ARL) values than ARLs of variable parameter (VP) X¯ chart for most of the cases. The VP chart shows little better performance than the new chart; but at large sample sizes (n) of 12 and 16. The VSS X¯ chart also shows lower ARLs but at very large sample size, which should not be used because, as far as possible, samples should be taken from a lot produced under identical conditions. The inherent feature of the new chart is its simplicity, so that it can be used without difficulty at shopfloor level as it uses only a fixed sample size and fixed sampling interval but it is very difficult to set the various chart parameters in VP and VSS X¯ charts.

A lot of effort has been expended to develop the new strategies for monitoring the process mean. Various assumptions and factors affecting the performance of the X¯ chart have been identified and taken into account. In the proposed design, the observations have been assumed independent of one another but the observations may also be assumed to be auto‐correlated with previous observations and performance of the proposed chart may be studied.

The research findings could be applied to various manufacturing and service industries as it is more effective than the Shewhart chart and simpler than the VP, VSS and CUSUM charts.

The purpose of this paper is to introduce a new design of an R chart to catch smaller shifts in the process dispersion as well as maintaining the simplicity so that it may be applied at shopfloor level.

Here a new R chart has been proposed which can overcome the limitations of Shewhart, CUSUM and EWMA range charts. The concept of this R chart is based on chi‐square (χ²) distribution. Although CUSUM and EWMA charts are very useful for catching the small shifts in the mean or standard deviation, they can catch the process shift only when there is a single and sustained shift in process average or standard deviation.

It was found that the proposed chart performs significantly better than the conventional (Shewhart) R chart, CUSUM range schemes proposed by Chang and Gan for most of the process shifts in standard deviation. The ARLs of the proposed R chart is higher than ARLs of CUSUM schemes for only ten cases out of 40. The performance of the proposed R chart has also been compared with the variance chart proposed by Chang and Gan for various shifts in standard deviation. The ARLs of the proposed R chart are compared with Chang's R chart for sample sizes of 3 and it can be concluded from the comparisons that the proposed R chart is much better than Chang's variance chart for all shift ratios for sample size of three. Many difficulties related to the operation and design of CUSUM and EWMA control charts are greatly reduced by providing a simple and accurate proposed R chart scheme. The performance characteristics (ARLs) of the proposed charts are very comparable to a great degree with FIR CUSUM, simple CUSUM and other variance charts. It can be concluded that, instead of considering many parameters, it is better to consider single sample size and single control limits because a control chart loses its simplicity with a greater number of parameters. Moreover, practitioners may also find difficulty in applying it in production processes. On the other hand, CUSUM control charts are not effective when there is a single and sustained shift in the process dispersion.

A lot of effort has been done to develop the new range charts for monitoring the process dispersion. Various assumptions and factors affecting the performance of the R chart have been identified and taken into account. In the proposed design, the observations have been assumed independent of one another but the observations may also be assumed to be auto‐correlated with previous observations and the performance of the proposed R chart may be studied.

The research findings could be applied to various manufacturing and service industries as it is more effective than the conventional (Shewhart) R chart and simpler than CUSUM charts.

The proposed exponentially weighted moving average combined Shewhart cumulative score (EWMA‐CUSCORE) procedure for controlling the process mean cumulate scores of ‐1, 0, 1 or 2h assigned to each moving average of the current and past sample mean values depending on a preassigned interval in which its value falls. It will be shown by average run length (ARL) comparisons that the proposed scheme performs better than the Shewhart type schemes, the combined Shewhart cumulative score type schemes, the cusum type schemes and the standard EWMA type schemes for detecting shifts in the process mean when the underlying process control variable is normal.

The purpose of this paper is to propose a double exponentially weighted moving average control chart using repetitive sampling (RS-DEWMA) for a normally distributed process variable to improve the efficiency of detecting small process mean shift.

The algorithm for the implementation of the proposed chart is developed and the formulae for the in-control and out-of-control average run lengths (ARLs) are derived. Tables of ARLs are presented for various process mean shift. The performance of the proposed chart is investigated in terms of the average run-length for small process mean shift and compared with the existing DEWMA control chart. Numerical examples are given as illustration of the design and implementation of the proposed chart.

The proposed control chart is more efficient than the existing DEWMA control chart in the detection of small process mean shifts as it consistently gives smaller ARL values and quickly detects the process shift. However, the performance of the proposed chart relatively deteriorates for large smoothing constants.

The application of repetitive sampling in the control chart literature is gaining wide acceptability. The design and implementation of the RS-DEWMA control chart offers a new approach in the detection of small process mean shift by process control personnel.

This paper fills a gap in the literature by examining the performance of the repetitive sampling DEWMA control chart. The use of repetitive sampling technique in the control chart is discussed in the literature, however, its use based on the DEWMA statistic has not been considered in this context.

The growth in flexible working by employees in many office‐based organisations means that workstation sharing at the employer's premises is increasingly attractive. However, because of peaks and troughs in demand it is difficult to decide how many workstations should be provided. The purpose of this paper is to investigate the cost‐effectiveness of alternative workstation sharing strategies.

The study used an agent‐based simulation model with two input variables: the employees' reaction if they are blocked (i.e. they find that all workstations are already occupied), and the number of workstations at the employer's premises. The simulation was run for 56 scenarios. The results were evaluated by assigning cost penalties for workstations, blocking and displacement; there were eight cost regimes reflecting different organisational characteristics.

The simulations showed trade‐offs between the activity and space variables, in terms of utilisation, blocking and displacement. When costs were applied the output of the simulation runs, the most cost‐effective scenarios varied markedly with the different cost regimes.

The variation in optimum strategies with different model input values suggests that cost‐effective workstation sharing strategies must be developed on a case‐by‐case basis. The simplifying assumptions in the model must be considered when applying the findings to real organisations.

Quantified analysis of the cost‐effectiveness of workstation sharing strategies has not been found previously in the literature.