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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.

To investigate the application of multi‐attribute utility theory (MAUT) to aid in the decision‐making process when performing benchmarking gap analysis.

MAUT is selected to identify the overall best‐in‐class (BIC) performer for performance metrics involving inventory record accuracy within a public sector warehouse. A traditional benchmarking analysis is conducted on 14 industry warehouse participants to determine industry best practices for the four critical warehouse metrics of picking and inventory accuracy, storage speed, and order cycle time. Inventory and picking tolerances are also investigated in the study. A gap analysis is performed on the critical metrics and the absolute BIC is used to measure performance gaps for each metric. The gap analysis results are then compared to the MAUT utility values, and a sensitivity analysis is performed to compare the two methods.

The results indicate that an approach based on MAUT is advantageous in its ability to consider all critical metrics in a benchmarking study. The MAUT approach allows the assignment of priorities and analyzes the subjectivity for these decisions, and provides a framework to identify one performer as best across all critical metrics.

This research study uses the additive utility theory (AUT) which is only one of multiple decision theory techniques.

A new approach to determine the best performer in a benchmarking study.

Traditional benchmarking studies use gap analysis to identify a BIC performer over a single critical metric. This research integrates a mathematically driven decision analysis technique to determine the overall best performer over multiple critical metrics.

The purpose of this paper is to assist facility designers decide when to use the graph‐theoretic (GTH) approach to develop a block layout and when to seek another alternative that requires a lesser effort. Difficulties encountered when a GTH block layout is developed from an adjacency graph could force designers to sacrifice or change some of the adjacencies of the adjacency graph in the GTH block layout. Consequently, the value of the objective function of a GTH block layout could become less than what would be expected from the GTH approach.

A computational study is performed to assess the value of the objective function of block layouts produced by the GTH approach when adjacencies of the adjacency graph are deleted or changed in the GTH block layout and two of the procedures in the literature.

The computational study reveals that the magnitude of the decline in the value of the objective function of the GTH block layout renders it comparable to values obtained by the two procedures selected from the literature.

The results of the study could motivate layout designers to resort to various approaches to develop a block layout rather than sacrifice some adjacencies of the adjacency graph in the GTH block layout, which would adversely affect its objective function value.

Proposes a renewal theory approach to derive the cost model for two dependent processes. Thus, constructs the economic individual X chart and cause‐selecting chart to monitor the two processes. They may be used to maintain the process with minimum cost and effectively distinguish which component of the processes is out of control. The optimal design parameters of these control charts can be determined by minimizing the cost model using a simple grid search method. Gives an example to illustrate the design procedure and application of the economic individual X chart and cause‐selecting chart.

Control charts have played an important role in monitoring the performance of operation processes, ever since their invention. Traditionally, according to Juran's idea and others, x‐bar charts are more sensitive than individual x‐charts. However, such a conclusion is valid only under certain conditions. Individual x‐charts can outperform x‐bar charts in some situations, especially in cases of minor and extreme changes of the center value. Since each chart has its own advantages and disadvantages, the idea of combining the results of these two charts is studied. The finding seems to be useful for practitioners in quality control.

The purpose of this paper is to develop an economic statistical design based on the concepts of adjusted average time to signal (AATS) and ANF for X ¯ control chart under a Weibull shock model with multiple assignable causes.

The design used in this study is based on a multiple assignable causes cost model. The new proposed cost model is compared with the same cost and time parameters and optimal design parameters under uniform and non-uniform sampling schemes.

Numerical results indicate that the cost model with non-uniform sampling cost has a lower cost than that with uniform sampling. By using sensitivity analysis, the effect of changing fixed and variable parameters of time, cost and Weibull distribution parameters on the optimum values of design parameters and loss cost is examined and discussed.

This research adds to the body of knowledge relating to the quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where multiple assignable causes affect the production process.

The cost functions for uniform and non-uniform sampling schemes are presented based on multiple assignable causes with AATS and ANF concepts for the first time.

When there is a change in a process, the MaxMin exponentially weighted moving average (EWMA) control chart shows which parameters have increased or decreased. The MaxMin EWMA may also be viewed as smoothed tolerance limits. Tolerance limits are limits that include a specific proportion of the population at a given confidence level. In the context of process control, they are used to make sure that production will not be outside specifications. In this article, we provide useful coverages for the MaxMin EWMA chart, when also used as tolerance limits. The proposed EWMA smoothed tolerance limits require relatively small sample sizes to attain useful coverages at high confidence levels. The MaxMin EWMA chart has already been successfully field‐tested and subsequently implemented with 100 multi‐stream processes.

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.

IN today's battle for greater productivity management is cast in a cardinal role. Like a military commander, it has first to recognize the nature and purpose of the opponent and then formulate the best strategy to encompass his defeat. One element in that is to know the strength and quality of the available resources, ways in which to improve both, and then deploy them to the best advantage.