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The importance of quality and statistical quality control as strategic tools, well established in the manufacturing community, has recently been extended to the service sector. The application of statistical quality control techniques within the banking industry is discussed. Recommendations for establishing a quality control programme within a typical banking service environment are provided.

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.

Examines some principles of the design and implementation of decision support systems in the context of statistical quality control. These principles are a function both of the level within the organization at which the decision is made and of the type of decision that is made. The principles are well known in the field of decision support systems and have proved effective. Argues that these principles are of great value to researchers, managers and quality specialists in the field of statistical quality control as well. The argument is supported by a number of actual examples in the application of statistical quality control where knowledge of the principles proved of value in improving quality.

The purpose of this paper is to develop a double-objective economic statistical design (ESD) of (X ‾) control chart under Weibull failure properties with the Linex asymmetric loss function. The authors have expressed the probability of type II error (β) as the statistical objective and the expected cost as the economic objective.

The design used in this study is based on a double-objective economic statistical design of (X ‾) control chart with Weibull shock model via applying Banerjee and Rahim's model for non-uniform and uniform schemes with Linex asymmetric loss function. The results in the least average cost and β in uniform and non-uniform schemes by Linex loss function, compared with the same schemes without loss function.

Numerical results indicate that it is not possible to reduce the second type of error and costs at the same time, which means that by reducing the second type of error, the cost increases, and by reducing the cost, the second type of error increases, both of which are very important. Obtained based on the needs of the industry and which one has more priority has the right to choose. These designs define a Pareto optimal front of solutions that increase the flexibility and adaptability of the X ‾ control chart in practice. When the authors use non-uniform schemes instead of uniform schemes, the average cost per unit time decreases by an average and when the authors apply loss function, the average cost per unit time increases by an average. Also, this quantity for double-objective schemes with loss function compared to without loss function schemes in cases uniform and non-uniform increases. The reason for this result is that the model underestimated the costs before using the loss function.

This research adds to the body of knowledge related to flexibility in process quality control. This article may be of interest to quality systems experts in factories where the choice between cost reduction and statistical factor reduction can affect the production process.

The cost functions for double-objective uniform and non-uniform sampling schemes with the Weibull shock model based on the Linex loss function are presented for the first time.

Statistical process control (SPC) can be applied to service processes, but the framework and methods may deviate from applications in industry. This was investigated in several pilot projects covering a range of service processes. Three main issues emerged. First, in addition to the traditional distinction in back office and front office processes, the customer process is leading in achieving control and improvement of service quality. Second, process diagnosis of service processes from back office to customer may reveal quality problems. Finally, measurements in front office and customer processes are needed to control these processes. From current practice and literature, it is not evident which types of measurements are adequate to provide prompt feedback to front office personnel. Introduces a framework for measurements, which suggests the type of measurement depending on the type of service process, whether it is the front office or customer process, and the type of quality characteristic in Kano’s model (must be, the more the better or delighting). Uses practical examples from the pilot projects to illustrate the issues.

Statistical process control (SPC) is widely applied for control and improve processes in manufacturing, but very few studies have reported on the successful application of SPC in the food industry, in particular. The purpose of this paper is to critically assess the status of SPC in the UK food manufacturing industry and to suggest future research avenues.

A research project was carried out in the UK food manufacturing sector through questionnaires. The results of the study were analysed using descriptive statistics and statistical tests to be applied in the hypothesis testing.

Findings revealed that 45 per cent of the respondents are SPC users and x ¯ -R and x ¯ -S charts are the most commonly applied SPC charts in this industry. It was determined that top management commitment is the most critical factor, while lack of SPC training is the most alarming challenge, and lack of awareness of SPC and its benefits are the main reasons for the food companies not implementing SPC.

The study considered only the food manufacturing companies. Future research could be addressed toward the food service and food supply chain.

The paper provides information to food companies in the UK on most common practiced and important quality tools, SPC charts and critical success factors in the food industry. Moreover, the most challenging factors of SPC implementation in the food industry are presented.

This study depicted the current state of SPC practices in the food industry and the process performance in SPC and non-SPC companies is compared.

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.

Quantitative metrics should be used as a risk management option whenever possible. This work proposes a framework for the risk quantification and the resulting risk-based design of control charts to monitor quality control points.

Two quality control models were considered for the risk quantification analysis. Estimated operating characteristic curves, expressing the defect rate (on a ppm basis) as a function of the sample size, process disturbance magnitude and process capacity, were devised to evaluate the maximum rate of defective product of the processes. The proposed framework applicability on monitoring critical control points in Hazard Analysis and Critical Control Point (HACCP) systems was further evaluated by Monte Carlo simulations.

Results demonstrate that the proposed monitoring systems can be tuned to achieve an admissible failure risk, conveniently expressed as the number of non-conforming items produced per million products, and these risks can be properly communicated. This risk-based approach can be used to validate critical control point monitoring procedures in HACCP plans. The expected rates of non-conforming items sent out to clients estimated through stochastic simulation procedures agree well with theoretical predictions.

The procedures outlined in this study may be used to establish the statistical validity of monitoring systems that uses control charts. The intrinsic risks of these control systems can be assessed and communicated properly in order to demonstrate the effectiveness of quality control procedures to auditing third parties.

This study provides advancements toward practical directives for the implementation of statistical process control in the food industry. The proposed framework allows the assessment and communication of intrinsic failure risks of quality monitoring systems. It may contribute to the establishment of risk-based thinking in the constitution of quality management systems.

The purpose of this paper is to put forward the view that systems theory, cybernetics, information theory, and dissipative structure theory should be the fundamental principles underlying quality management. These “new sciences” relate to quality management not just in terms of philosophy but also in the requirements of the well‐structured quantitative analysis methods.

Discusses several issues in quality management and control from the views of the new sciences, then proposes the concept of computer‐aided statistical process control (CASPC) to bridge traditional statistical process control and automatic process control, and the use of ARIMA models to achieve CASPC for linear systems.

Notes that there are implications of the new sciences for quality control and presents a discussion of same.

Is of value by pointing out that computer‐aided statistical process control for dynamic systems can be the realistic way of leading towards more advanced systematic control approaches.

The purpose of this paper is to evaluate and improve the quality and the reliability of pre‐treatment quality controls of an efficient technique of radiotherapy called IMRT (intensity‐modulated radiation therapy). The aim is then to determine if the controls can be safely reduced while keeping an optimal level of quality.

The statistical process control method (SPC) was applied to quality assurance in IMRT. In order to characterize prostate and head‐and‐neck treatment process variability, individual value control charts and moving‐range control charts were established.

Control charts showed that prostate and head‐and‐neck treatment processes are only subject to random causes of variability, which means they are statistically controlled. It was proved that both processes are statistically stable and capable.

The paper shows that SPC is an efficient method to objectively determine if quality controls can be reduced.