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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.

The purpose of this paper is to develop a statistical control chart based model for earlier defect identification.

The paper used statistical process control methods and an auto‐regression model to model the identification of the initiation point of a random defect. Conventional statistical process control (SPC) methods have been widely used in process industries for process abnormality detections. However, their practicability and achievable performance are limited due to the assumptions that a continuous process is operated in a particular steady state and that all variables are normally distributed. Because the case considered here does not meet the requirement of conventional SPC methods, we proposed adaptive statistical process control charts based on an autoregressive model to distinguish defects from normal changes in operating conditions. The method proposed has been tested on a set of vibration data of rolling element ball bearings

Several control charts have been used and compared in this paper to identify the initial point of a defect. Overall, the adaptive Shewhart average level chart is a good choice since it overcomes the drawback of adaptive moving charts by working out the limits using all the bearings' data, with no such a need for a subjective threshold level. They are also not very sensitive to the small casual changes in the data.

The model developed can be served as part of a prognosis tool for maintenance decision making since once the earlier warning point has been identified, corrective maintenance actions may be taken. It has practical application areas in vibration based monitoring or any monitoring scheme where a trend in the monitored measurements may exist. The method proposed is easy to use and can be implemented in any condition based maintenance software packages.

The approach proposed in this paper is a new application of existing methods and of original contribution from a point of view of applicability. It adds value to the existing literature and is of value to practitioners.

The purpose of this paper is to present the application of a procedure for the quality control of stainless steel tubes produced for automotive exhaust systems from a leading company in the steel sector, based on the Delphi method in accordance with the ISO/TS 16949:2009 and the ISO 9000:2008. Using Delphi methodology, it was possible to identify the main problems in the production lines object of the study, the main defects and their causes. Statistical methods were used to monitor process compliance and capacity. The panel of experts involved in Delphi method was able to identify causes of non‐compliance and suggest corrective actions.

The quality procedure implemented involves the application of the Delphi method and the ISO/TS 16949:2009 standard in conjunction with ISO 9000:2008 to the production line of welded tubes for exhaust systems. The statistical methods used to monitor the process were mainly control charts. Capability index, Cp and Cpk, were used to measure the process attitude to produce compliant outputs. Dimensional data were acquired by non‐destructive testing on diameters and X‐R charts were used to graphically represent the process state of control. Destructive tests were performed to monitor the welding quality and P‐chart were used to assess the proportion of nonconforming units.

In this work, a procedure was developed in order to characterize the production process of TXM tubes realized in the line 31 of the leader company plant. The use of Delphi methodology, in order to incorporate experts opinions in the quality control of stainless steel tubes, was one of the main points of this work. The panel of experts worked together to identify process issues, define their causes and propose corrective actions. The paper provides an overview about the quality approach of one of the world's largest companies in the production of steel and shows also how the statistical tools are used in order to manage process behavior.

The value of this paper is to illustrate an innovative approach to a real life quality problem; it demonstrates how the application of qualitative and quantitative quality instruments in accordance with technical specification can help in increasing and maintaining product compliance and in optimizing the management of resources.

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.

Focuses on the Statistical Process Control (SPC) implementation phase in an effort to underline that SPC is not just control charts, and that many steps have to be accomplished before these charts are used. In addition, highlights the role of training and presents it as an ongoing process which involves everyone in the organization. These SPC implementation steps are not meant to be a checklist; they provide guidelines that can be modified in accordance with organizational‐specific requirements.

The purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.

A two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.

The validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.

This study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.

The application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

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 devise a robust statistical process control methodology that will enable service managers to better monitor the performance of correlated service measures.

A residuals control chart methodology based on least absolute value regression (LAV) is developed and its performance is compared to a traditional control chart methodology that is based on ordinary least squares (OLS) regression. Sensitivity analysis from the goal programming formulation of the LAV model is also performed. The methodology is applied in an actual service setting.

The LAV based residuals control chart outperformed the OLS based residuals control chart in identifying out of control observations. The LAV methodology was also less sensitive to outliers than the OLS approach.

The findings from this study suggest that the proposed LAV based approach is a more robust statistical process control method than the OLS approach. In addition, the goal program formulation of the LAV regression model permits sensitivity analysis whereas the OLS approach does not.

This paper shows that compared to the traditional OLS based control chart, the LAV based residuals chart may be better suited to actual service settings in which normality requirements are not met and the amount of data is limited.

This paper is the first study to use a least absolute value regression model to develop a residuals control chart for monitoring service data. The proposed LAV methodology can help service managers to do a better job monitoring related performance metrics as part of a quality improvement program such as six sigma.

Assessing the impact of a quality improvement intervention on an organization is particularly difficult in a high contact service operation where the intangible service encounter is the unit of output. Frequently, accounting or financial data must be used to evaluate the effectiveness of the intervention; however, these data may be problematic with respect to sample size and masking effects due to aggregation. Presents a systems model which describes metaphorically how an unstable process can continue to show no performance gains despite continued input of resources into improvement initiatives. A special type of Shewart control chart, known as the X‐chart, is developed as a methodology for assessing process performance after an improvement programme has been implemented. An X‐chart is used to analyse performance data collected in a real service setting where service quality standards were deployed in the front line phase of the operation. Although traditional analysis of variance concluded that there was no significant improvement in performance, the X‐chart indicates that real performance gains were occurring. The X‐chart provides management with an easy‐to‐use decision tool which can help assess the effectiveness of many different types of organizational change initiatives.