Search results

The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry.

Data were collected during production of a wheat-based dough in a food industry and the control charts were developed with these steps: to collect the master sample from different production batches; to verify, by graphical methods, the quantity and the characterization of the number of mixing probability distributions in the production batch; to adjust the theoretical model of probability distribution of each subpopulation in the production batch; to make a statistical model considering the mixture distribution of probability and assuming that the statistical parameters are unknown; to determine control limits; and to compare the mixture chart with traditional control chart.

A graph was developed for monitoring a multi-stream process composed by some parameters considered in its calculation with similar efficiency to the traditional control chart.

The control chart can be an efficient tool for customers that receive product batches continuously from a supplier and need to monitor statistically the critical quality parameters.

The present paper aims to present the results of a simulation study on the behavior of the four 95 percent bootstrap confidence intervals for estimating C_pk when collected data are from a multiple streams process.

A computer simulation study is developed to present the behavior of four 95 percent bootstrap confidence intervals, i.e. standard bootstrap (SB), percentile bootstrap (PB), biased‐corrected percentile bootstrap (BCPB), and biased‐corrected and accelerated (BCa) bootstrap for estimating the capability index C_pk of a multiple streams process. An analysis of variance using two factorial and three‐stage nested designs is applied for experimental planning and data analysis.

For multiple process streams, the relationship between the true value of C_pk and the required sample size for effective experiment is presented. Based on the simulation study, the two‐stream process always gives a higher coverage percentage of bootstrap confidence interval than the four‐stream process. Meanwhile, BCPB and BCa intervals lead to better coverage percentage than SB and PB intervals.

Since a large number of process streams decreases the coverage percentage of the bootstrap confidence interval, it may be inappropriate to use the bootstrap method for constructing the confidence interval of a process capability index as the number of process streams is large.

The present paper is the first work to explore the behavior of bootstrap confidence intervals for estimating the capability index C_pk of a multiple streams process. It is concluded that the number of process streams definitively affects the performance of bootstrap methods.

This chapter explores two comparative longitudinal cases studies involving policy entrepreneurs inside and outside government in Taiwan. From 2003 to 2007, the Ministry of Education (MOE) in Taiwan played the conventional role of policy entrepreneur to initiate the “University Corporation Project.” Through this project, the MOE sought to transform all national universities in Taiwan into independent entities, gaining them more autonomy and increasing their accountability as well. From 2008 to 2014, as a policy entrepreneur outside the government, National Cheng Kung University (NCKU) took the lead and proposed the “University Autonomous Governance Project.” This project sought an alternative solution based on public universities’ needs to improve university autonomy and accountability.

The method undertaken in this study includes document analysis and participants’ observation. First, both policy entrepreneurs adopted the strategy of power sharing, but the effectiveness of the strategy is determined by the interaction between policy entrepreneurs and stakeholders. Moreover, facing a multiple-principals condition in both cases, both policy entrepreneurs should negotiated with or compete with other potential agenda competitors. Those findings will be a detailed roadmap for policy makers in East Asia once they plan to initiate policy entrepreneurship in their countries.

The purpose of this chapter is to critically examine the extensive calls for enhanced evidence within the sport-for-development field. The chapter questions whether these are appropriate and realistic.

The chapter utilizes current literature to deconstruct the assumptions that increased evidence will legitimize the field of sport-for-development, improve practice and enhance future policy. The authors’ own experiences, working as external evaluators, are also drawn upon to critique the value of current “evidence.”

The chapter illustrates how current calls for evidence are somewhat misguided and are unlikely to fully realize the intended consequence of validating sport-for-development or improving future practice. Utilizing personal reflections, the impact that Global North/Global South power imbalances have on data is discussed, suggesting that this will rarely lead to data that provide a detailed understanding of work in practice.

The chapter builds on the work of other authors illustrating the importance of disconnecting research from evidence and monitoring and evaluation in the sport-for-development field.

The chapter utilizes previous literature but also provides a rarely available personal perspective on the issue of evidence that continues to permeate the rationale behind undertaking research within sport-for-development.

Chapter 7 synthesizes the perception research into plausible design and configuration strategies for the learning experience dimension of a psychophysical learning system. The processes used in all five senses to reduce information into a perception are again used to create learning activities and processes, which facilitate the learning and discriminate meaning from the learning objects and activities. This process attends to the interactions across the categories of content to determine the critical components of the discipline to include in the learning experience. Once again, the focus of the psychophysical learning experience is placed on the structure of the (external) discipline, which is used to configure the learning experiences.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the thirteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the twelfth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of analyses of large data sets. Many manufacturing companies can now seize this opportunity to leverage their data to gain competitive advantages for continuous improvement. Six Sigma has been among the most popular approaches for continuous improvement. The data-driven nature of Six Sigma applied in a big data environment can provide competitive advantages. In the traditional Six Sigma implementation – define, measure, analyze, improve and control (DMAIC) problem-solving strategy where a human team defines a project ahead of data collection. This paper aims to propose a new Six Sigma approach that uses massive data generated to identify opportunities for continuous improvement projects in a manufacturing environment in addition to human input in a measure, define, analyze, improve and control (MDAIC) format.

The proposed Six Sigma strategy called MDAIC starts with data collection and process monitoring in a manufacturing environment using system-wide monitoring that standardizes continuous, attribute and profile data into comparable metrics in terms of “traffic lights.” The classifications into green, yellow and red lights are based on pre-control charts depending on how far a measurement is from its target. The proposed method monitors both process parameters and product quality data throughout a hierarchical production system over time. An attribute control chart is used to monitor system performances. As the proposed method is capable of identifying changed variables with both spatial and temporal spaces, Six Sigma teams can easily pinpoint the areas in need to initiate Six Sigma projects.

Based on a simulation study, the proposed method is capable of identifying variables that exhibit the biggest deviations from the target in the Measure step of a Six Sigma project. This provides suggestions of the candidates for the improvement section of the proposed MDAIC methodology.

This paper proposes a new approach for the identifications of projects for continuous improvement in a manufacturing environment. The proposed framework aims to monitor the entire production system that integrates all types of production variables and the product quality characteristics.