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The purpose of this paper is to explore the epistemological origin of Shewhart's and Deming's ideas in their development of a theory of quality.

The approach takes the form of a literature review.

Walter. A. Shewhart's and W. Edwards Deming's ideas concerning a theory of quality originated not solely from insights about variation within statistics but also from the field of philosophy, particularly epistemology. Shewhart and Deming, both seen as quality pioneers, were strongly influenced by the conceptualistic pragmatist Clarence Irving Lewis and his theory of knowledge. This is, and has often been, a neglected connection; however, in today's competitive business environment knowledge and competence have become crucial success factors. Thus, the epistemology‐related origin of their theory of quality has become increasingly interesting and important to explore. First, a summary version of Clarence Irving Lewis' theory of knowledge will be presented here as expressed in his work Mind and the World Order: Outline of a Theory of Knowledge (1929). Second, examples of some important connections between Lewis, and chosen parts of Shewhart's and Deming's theory of quality will be given, for example the plan‐do‐study‐act cycle, operational definitions and profound knowledge. It will also be indicated how the social element in knowledge is emphasised in the works of Lewis, Deming, and Shewhart.

By exploring the epistemological background of Deming's and Shewhart's ideas of a theory of quality, it might be able to better comprehend the profound ideas they left behind and improve the understanding and use of their theory of quality today.

Dr W.A. Shewhart, “founder” of the modern quality movement and inventor of the control chart, was greatly influenced by the pragmatist philosopher, C.I. Lewis. However, Lewis's influence is less clear. Shewhart did not refer to Lewis in his 1931 book and it was not until the 1939 publication of his lectures that we find references to Lewis. While Shewhart's work has been read and understood by statisticians, this paper argues that to fully understand his work, one needs a background in philosophy of science. To make the point, this paper uncovers similarities between Lewis's pragmatism and Shewhart's invention of the control chart. Not least is a theory of prediction. The paper concludes that Shewhart had formed the core of his theory before reading Lewis, and that Mind and the World Order (Lewis, C.I., Mind and the World Order: Outline of a Theory of Knowledge, Dover Publications, New York, NY, 1929) provided a convenient post hoc rationalisation. The basis for a theory of management by prediction is a significant outcome of 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.

Performance measurement and the balanced scorecard is becoming ubiquitous. However, this paper will argue that some of the early work on performance measurement and management conducted in the early part of the twentieth century has been overlooked by more recent writers. In particular, prediction in today’s literature is not the same concept as that developed by Dr Walter Shewhart in the 1920s. This paper traces the development of performance measurement from its accounting and operational roots until today and concludes that current use of performance measurement could benefit from earlier developments. In particular, the paper argues that the current obsession with testing success maps has limitations as they view the world as being static – relationships holding for all time. The paper proposes that a more dynamic view is taken to these relationships so managers do not become trapped in an outdated strategy map.

W. Edwards Deming, famous for his work with the Japanese following the Second World War, had a unique set of theories and approaches that were clearly his own. While much has been written about his experience and views, this article will focus on those individuals who made a significant impact on the formation of his views during the 1920s and 1930s and what he gained from each of them. Walter A. Shewhart was clearly the individual who had the most profound influence on Deming’s views and subsequent approaches to quality. But more than an influence, Shewhart was Deming’s mentor. Others who influenced Deming during this period include: Clarence Irving Lewis, Sir Ronald A. Fisher, and Jerzy Neyman. Those who wish to understand Deming’s theories can gain from studying Deming’s experience and views. In addition, a more detailed understanding of Deming can be gained by also studying the work and theories of those who influenced him.

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.

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 concept of the proposed R chart is based on the sum of chi squares (χ²). The average run lengths (ARLs) of the proposed R chart are computed and compared with the ARLs of a standard R chart, Shewhart variance chart proposed by Chang and Gan, a CUSUM range chart (with and without FIR feature) proposed by Chang and Gan and also with an EWMA range chart proposed by Crowder and Hamilton for various chart parameters. This paper aims to show that only FIR CUSUM schemes perform better than the proposed R chart but other CUSUM and EWMA schemes are less efficient than the proposed R chart.

The concept of the proposed R chart is based on the sum of chi squares (χ²). The proposed R chart divides the plot area into three regions, namely: outright rejection region; outright acceptance region; and transition region. The NULL hypothesis is rejected if a point falls beyond the control limit, and accepted if it falls below the warning limit. However, when a point falls beyond the warning limit, but not beyond the control limit, the decision is taken on the basis of individual observations of the previous H samples, which are considered to evaluate statistic U, that is the sum of chi squares. The NULL hypothesis is rejected if U exceeds a predefined value (U*) and accepted otherwise.

The comparisons also show that the CUSUM, EWMA and proposed R charts outperform the Shewhart R chart by a substantial amount. It is concluded that only FIR CUSUM schemes perform better than the proposed R chart, as it is second in ranking. The other CUSUM and EWMA schemes are less efficient than the proposed R chart.

CUSUM and EWMA charts can catch a small shift in the process average but they are not efficient to catch a large shift. Many researchers have also pointed out that these charts' applicability is limited to the chemical industries. Another limitation of CUSUM and EWMA charts is that they can catch the shift only when there is a single and sustained shift in the process average. If the shift is not sustained, then they will not be effective.

Many difficulties related to the operation and design of CUSUM and EWMA control charts are greatly reduced by providing a simple and accurate proposed scheme. The performance characteristics (ARLs) of the proposed charts described in this paper are very much comparable with FIR CUSUM, CUSUM, EWMA and other charts. It can be concluded that, instead of considering many chart parameters used in CUSUM and EWMA charts, it is better to consider a simple and more effective scheme, because a control chart loses its simplicity with multiple parameters. Moreover, practitioners may also experience difficulty in using these charts in production processes.

It is a modification of the Shewhart Range Chart but it is more effective than the Shewhart Range chart, as shown in the research paper.

The principal application domain for statistical process control (SPC) charts has been for process control and improvement in manufacturing businesses. However, the number of applications reported in domains outside of conventional production systems has been increasing in recent years. Implementing SPC chart approaches in non‐standard applications gives rise to many potential complications and poses a number of challenges. This paper reviews non‐standard applications of SPC charts reported in the literature from the period 1989 to 2000, inclusive. Non‐standard applications are analysed with respect to application domain, data sources used and control chart techniques employed. Applications are classified into five groups according to the types of problem to which control chart techniques have been applied. For each group the nature of the applications is described and analysed. The review does not show a paradigm shift in the types of SPC control chart applications but does show clearly that the application boundaries extend considerably beyond manufacturing and that the range of problems to which SPC control chart techniques can be applied is much wider than commonly assumed. The paper highlights the critical fundamental and technical issues that need to be addressed when applying SPC chart techniques in a range of non‐standard applications. Wider managerial issues of importance for successful implementations in non‐standard applications of SPC control charts are also discussed.

Combined Shewhart‐cumulative score (cuscore) quality control schemes are available for controlling the mean of a continuous production process. In many industrial applications, it is important to control the process variability as well. The proposed combined Shewhart‐cumulative‐score (cuscore) procedure for detecting shifts in process variability uses the procedures developed by Ncube and Woodall (1984) to monitor shifts in the process mean of continuous production processes. It is shown, in the one‐sided case, by average run length comparisons, that the proposed schemes perform significantly better than comparative Shewhart procedures and in some cases even better than cusum schemes when using some process variability quality characteristics.