Control charts based on geometric distribution have shown to be very useful in the monitoring of high yield manufacturing processes and other applications. It is well known that the traditional 3-sigma limits will give too many false alarms and the probability limits should be used. This paper shows that the average time to alarm may even increase at the beginning when the process is deteriorated. A new procedure is established for the setting of control limits so that the average run length is maximized when the process is at the normal level. Hence the chart sensitivity can be improved. For the derivation of the control limits in this new procedure, a simple adjustment factor is suggested so that the probability limits can be used after the adjustment.

This study concerns the reliability assessment in high-quality new product development in which there is scarcity of data resulting from few or zero failures or the unavailability of failure time information. In such circumstances, traditional reliability assessment methods tend to be inadequate and ineffective. This paper describes a pragmatic approach adopted to address this practical issue. A Bayesian method using reparameterization of the Weibull distribution is proposed, which elicits priors in a meaningful way from technical experts and based on historical data. Unlike existing procedures found in the literature, the method here is developed from the perspective of availability of failure time data. Through a case study from the hard disk drive industry, it is demonstrated that the proposed method can provide an effective and practical solution to the challenging real-life problem. Furthermore, it is shown that failure time information has a significant effect on the inference about the Weibull shape parameter.

Insights are offered on the interpretation of results of analysis of designed experiments with response expressed on a nominal or ordinal scale, in terms of formulation of a cause‐and‐effect mathematical model as well as the subsequent choice of factor settings for a future desired response. As generic design of experiments software packages are based on procedures of parametric statistics, the inherent limitations peculiar to the analysis of categorical data by such software packages are illustrated by a numerical example for the benefit of non‐statisticians among quality practitioners. Copyright © 2016 John Wiley & Sons, Ltd.

In studying the service quality of a service provider, customers are often asked to rate the services they have received through a questionnaire. Based on the responses received, various models/techniques that analyze the scores of different components have been proposed to evaluate overall service quality. In particular, the average score of a service provider often serves as a basis for comparison between two or more service providers. In practice, the scores associated with each response level (e.g., agree, strongly agree) are often based on intuition or some simple rule, which may not correctly reflect the differences between different response levels. Therefore, it is important to examine the robustness of a conclusion (e.g., service provider A is better than service provider B) with respect to changes in the scores assigned to each response level when studying the service quality of service providers through customer surveys. It is commonly assumed that the difference between the average scores of two service providers provides a good indication of the robustness of the conclusion. In this paper, we illustrate that this may not be true and highlight that a separate analysis on the robustness of a conclusion is necessary. We describe how the robustness of a conclusion can be represented and show how it can be computed in practice.

Environmental quality for the general population is dominated by air quality. Thus modeling of air quality is the first step toward any program for quality improvement. This paper describes the use of the ARIMA (Autoregressive Integrated Moving Average) time series modeling approach, illustrated by the tracking of the daily mean PM2.5 concentration in the north region of Singapore. A framework for ARIMA forecasting revised from the general Box-Jenkins procedure is first outlined; T-test and three information criteria, namely, AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), HIC (Hannon-Quinn Information Criterion) are employed in addition to analyses on the ACF (auto-correlation function) and PACF (partial autocorrelation function). With forecasting as the primary objective, the emphasis is on out-of-sample forecasting more than in-sample fitting. It is shown that for 30 such forecasts, one-step ahead MAPE (mean absolute percentage error) has been found to be as low as 8.0%. The satisfactory result shows the classical time series modeling approach to be a promising tool to model compound air pollutants such as PM2.5; it enables short-term forecasting of this air pollutant concentration for public information on air quality.

To many in the Quality profession, the terms Quality Management (QM) and Quality Engineering (QE) tend to be used interchangeably, or at least there has been little official attempt to distinguish them.

This book discusses the application of quality and reliability engineering in Asian industries, and offers information for multinational companies (MNC) looking to transfer some of their operation and

Statistical design of experiments (DOE) is widely used today for process and product characterization and optimization. Owing to cost and time considerations, sometimes only a minimum number of experimental runs can be conducted, with added challenges in analysis when the experimental outcomes cannot be measured on a continuous scale and are expressed only in qualitative terms such as 'excellent', 'satisfactory' and 'poor': such outcomes are variously described as 'categorical', 'attribute', 'qualitative', 'discrete' or 'counted' in nature. This paper offers practical techniques of handling small experiments with such non-standard DOE response data which are otherwise impossible to analyze by standard statistical software. The suggested procedures, built upon what is called a Likelihood Transfer Function (LTF), do not require complex data analysis but would yield results consistent with the constraints of experimental conditions as well as the objectives of stakeholders. Copyright (C) 2015 John Wiley & Sons, Ltd.

Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage-wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright (C) 2015 John Wiley & Sons, Ltd.

In the traditional use of design of experiments (DOE), the analysis of a designed experiment proceeds only when the designed plan has been fully carried out and the response values are all available, a standard practice that could be inefficient in time and cost and inadequate when quick indicative results are needed. A procedure referred to as ‘iterative designed experiment analysis (IDEA)’ is proposed for suggesting results progressively and giving early indications in situations where response values are available only gradually. The procedure is expected to be useful in the early stages of experimental discovery, especially in factor screening situation. Two case studies illustrate and suggest the potential of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Though Six Sigma has proven to be an effective framework for performance improvement in a wide variety of industries for many years, the future development of Six Sigma needs to be explored. In this paper, we briefly review the background and development of Six Sigma and suggest ways to enhance and extend the effectiveness of Six Sigma in the coming years. We propose the future development of Six Sigma from three perspectives: strategy, integration and innovation. Some challenges are addressed for Six Sigma's spreading to service systems. Research shows that when Six Sigma becomes more pervasive and inclusive, it will offer opportunities for excellence in performance in the production of goods and services in a wide variety of businesses.

Over the last two decades, a number of studies have examined the trade-off involved in concurrent engineering (CE), time reduction versus additional effort for downstream rework. This study presents an overview of the recent CE modeling literature that examined this trade-off. We find that most CE models are built on the assumption that development stages are dependent where the principal information exchange between consecutive design stages is unidirectional, from upstream stage to downstream stage. According to literature review and field study, we believe such assumption is reasonable, because in many situations, current execution of design stages actually occurs within two sub-stages (Testing 1 and Development 2) which are sequentially dependent. In the future, we may also build analytical models based on interdependent stages so as to better understand the impact of project properties on best CE policies and product development performance.

One of the greatest challenges in managing product development projects is identifying an appropriate sequence of many coupled activities. The current study presents an effective approach for determining the activity sequence with minimum total feedback time in a design structure matrix (DSM). First, a new formulation of the optimization problem is proposed, which allows us to obtain optimal solutions in a reasonable amount of time for problems up to 40 coupled activities. Second, two simple rules are proposed, which can be conveniently used by management to reduce the total feedback time. We also prove that if the sequence of activities in a subproblem is altered, then the change of total feedback time in the overall problem equals to the change in the subproblem. Because the optimization problem is NP-complete, we further develop a heuristic approach that is able to provide good solutions for large instances. To illustrate its application, we apply the presented approach to the design of balancing machines in an international firm. Finally, we perform a large number of random experiments to demonstrate that the presented approach outperforms existing state-of-art heuristics. (C) 2014 Elsevier B.V. All rights reserved.

Ongoing changes in the world of quality engineering have not been adequately captured in traditional textbooks or training materials. This article discusses the "megatrends" that are emerging and advocates that serious efforts be made to address, among other things, the new meaning of "customer satisfaction" as well as the performance of an organization. Thoughts and arguments are presented in terms of the concerns of three categories of stakeholders: business leaders, quality professionals, and end customers. Accordingly, a "New 5S" perspective is advocated for effectiveness as well as "future proofing" of the quality profession.

Six Sigma as a framework for eliminating defects at the project level and improving performance and customer satisfaction at the corporate level has been generally recognised. This case-oriented paper reports an important Six Sigma management case study at the world's largest cold rolling mill situated in China. The descriptions of measures taken at the company level, as well as that of the exemplary application experience of this company, would constitute a most comprehensive account of the impact brought about by Six Sigma to the company. A Black Belt project was conducted to improve the cold rolling capability to meet the thickness requirements using the Six Sigma methodology - DMAIC (define, measure, analyse, improve and control) principle. The implementation of Six Sigma methodology led to a significant financial impact on the profitability of the company. Seven key factors were also found to be instrumental to the successful Six Sigma management implementation in the company.

Systems with dynamic characteristics have gained increasing attention of both researchers and engineers in industry in recent years. The purpose of this article is to provide an overview of investigations on what might be called dynamic response in experimental design, so as to facilitate further research in this area. We briefly review the development of research on dynamic problems in diverse fields such as statistical quality control, biostatistics, experimental design and repeated measurements. Discussions in the past are specially sorted out on the definition and classification of dynamic response problems as encountered in general experimental design and robust design, and various perspectives of system types and response types. Four categories of modelling techniques in classical experimental design are summarized from the literature according to distinct application conditions; this would provide a useful basis for future investigations. Copyright © 2014 John Wiley & Sons, Ltd.

Global sourcing, regarded by many companies as an important measure to enhance competitive advantages particularly in today’s “globalized economy”, faces challenges related to the management of supply chains across large geographical distances between suppliers and markets. This paper examines a number of practical issues and suggests general strategies to attain the best quality of global sourcing based on methods consisting of mixed sourcing strategies, implementation of appropriate technology, and supplier selection and management. Two common but contrasting industries, the apparel industry and food industry, are first used to illustrate in detail various ways to achieve global sourcing practices despite different inherent products characteristics. A general guideline with general applicability is then developed, with emphasis on several particular aspects: First, any mixed sourcing strategy should be built on product and industry characteristics. Secondly, categorization of products according to the main uncertainty that exists in the supply chain is a desirable way to reduce or avoid problems associated with global sourcing. Thirdly, global sourcing is value-adding only if it could enlarge the competitive advantages of products. Finally, these three aspects are inter-related and should always be considered in totality for evaluation and implementation.

This paper reports a study of the key success factors of what have been recognized as successful service enterprises in China, each considered representative of its respective industry. The grounded theory approach was used to analyze information collected from these enterprises, resulting in the identification of the attributes shared by these enterprises: customer-oriented service, service management, service innovation, and corporate social responsibility. Based on these attributes, a survey was conducted to verify the relationships among these attributes and important outcomes, namely customer satisfaction, perceived service quality, and enterprise reputation. The results of the statistical analysis indicate that the four attributes have positive impacts on service outcomes. The findings are of far-reaching importance in view of the vast potential service markets in China.

Research on product development has pointed to a challenge in integrating sustainability considerations into existing engineering practices rather than adding additional sets of practices and tools. The question is what practices are suitable for consideration? One set of practices and tools, deemed suitable due to its focus on long-term impacts and customer focus, is Quality Management. Within this area, the Robust Design Methodology has a historic connection to sustainability vis-à-vis quality loss caused by a product not only to an individual customer, but to society at large. Hence, there appears to be a neglected connection to the sustainability area. This paper explores how efforts based on the Robust Design Methodology may better contribute to sustainability and, more specifically, to sustainable product development. This paper reviews earlier Robust Design Methodology case studies that reveal how it supports sustainability. However, the reviews also reveal that efforts so far have focused only on the manufacturing and use phases of a product's lifecycle. Hence, adaptations of the methodology are needed, such as more conceptual and qualitative tools and explicit inclusion of eco-design indicators as a response variable in, for example, Design of Experiments. Adapting the Robust Design Methodology enables meeting the key aspects of an eco-design tool: addressing early integration of environmental aspects in development processes, having a lifecycle approach, and being a multi-criteria approach.

Abstract Six Sigma as a quality improvement framework has gained considerable popularity in the past two decades. Its extension Lean Six Sigma has also been embraced by many organizations for improvement of quality and business competitiveness. One important factor for the popularity of Six Sigma and Lean Six Sigma is their potential for improving service systems, in contrast to the conventional perceptions that only manufacturing systems can benefit from statistics-based methodologies. There are however a number of issues related to the nature of service systems that must be resolved before the full benefits of Lean Six Sigma can be realized. In this paper, these issues are discussed from a practical point of view from three angles: analytical, organizational, and personal. Awareness of the existence of such issues, if not the answers to all of them, is a pre-requisite to effective adoption of Lean Six Sigma tools.