Search results

The purpose of this paper is to compare the implementation of short‐run (i.e., small lot‐size) statistical process control (SPC) techniques for manufacturing between the U.S. and Japan. Using U.S. and Japanese questionnaires, this research focuses on the use of several manufacturing management elements such as setup time, stability of process, and quality improvement. These elements are compared in terms of their respective countries’ short‐run SPC techniques implementation. Barriers to the implementation of short‐run SPC techniques are also examined. In addition, this research identifies current process control techniques used to support short‐run SPC in both countries. Results show how the significantly different short‐run SPC techniques are utilized in the U.S. and Japan.

The present study attempted to replicate the findings of Kolb's research identifying two groups of mediators, which she labeled “Dealmakers” and “Orchestrators.” Seventy‐seven mediators were presented with a written dispute and asked to react the likelihood that they would use each of nine different mediation techniques. The techniques corresponded to Sheppard's taxonomy of Process Control, Content Control, and Motivational Control techniques. They also rated the perceived effectiveness of each of these three types of control with the dispute. Based upon their responses, the mediators were separated into groups using average‐link cluster analysis. The results suggested four clusters: Cluster 1 members corresponded to Kolb's “Dealmakers,” relying upon Process, Content, and Motivational Control techniques. Cluster 2 members did not correspond to either of Kolb's classifications, choosing to use Content and Motivational Control strategies. Cluster 3 members were similar to Kolb's “Orchestrators;” members of this cluster relied upon Process and Content Control techniques only. Cluster 4 members were reluctant to use any of the control strategies. These findings suggest a partial replication and extension of Kolb's initial work. Implications for future research are discussed.

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 monitoring and control of business processes and their variables have strategic importance in order to respond to the dynamics of the world of business. Many monitoring processes are focussed on controlling time and cost and the overall performance is evaluated through a standard set of key performance indicators. These passive approaches do not consider a holistic/system view and therefore ignore the interrelationships between various external and internal variables impacting a business process. This paper investigates an application of multivariate statistical process control techniques [mainly principal component analysis (PCA) and partial least squares (PLS)] which have been successfully used in process and chemical industries, to model, monitor, control and predict business process variables. A prototype, innovative managerial control system (IMCS), was developed to investigate the application of PCA and PLS techniques to monitor, control and predict business process performance. Data was collected and analysed using a case study in a precast concrete building products company. This study has proved that the PCA approach can be effectively used to control business processes. Also, the PLS approach is found to provide better forecasts as compared to commonly used decomposition method. The benefits and limitations of using multivariate statistical process control techniques as applied to business process control are highlighted.

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 compare implementation of short‐run (i.e., small lot‐size) statistical process control (SPC) techniques in just‐in‐time (JIT) manufacturing environments. Using U.S. and Japanese questionnaires, this research focuses on the use of several manufacturing elements such as setup time, stability of process and quality improvement. Barriers to the implementation of short‐run SPC techniques are also examined. Results show significant difference in the way some short‐run SPC techniques are utilized by JIT and non‐JIT manufacturers.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

When objective decisions are to be made, statistical methods should be used based on any objective information in the form of data collected about a product or process. Statistical techniques such as control charts, process capability indices and design of experiments have been used in the manufacturing industry for many years. There are a number of practical and managerial issues related to the application of statistical techniques in studies aimed at improving process and product quality. This paper is a summary of the thoughts and discussions from a recent Internet conference on this issue. Statistical process control techniques and their role in process improvement are first discussed and some issues related to the interpretation and use of experimental design techniques are also summarised. The focus will be on continuous quality improvement using statistical techniques.

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.

Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.

The techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.

The review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.

The review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

This special “Anbar Abstracts” issue of Work Study is split into six sections covering abstracts under the following headings: Operational research and statistics; Project management, method study and work measurement; Business process re‐engineering; Design of work; Performance, productivity and motivation; Stock control and supply chain management.