7 resultados para Statistical quality control

em Digital Commons at Florida International University


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This thesis chronicles the design and implementation of a Internet/Intranet and database based application for the quality control of hurricane surface wind observations. A quality control session consists of selecting desired observation types to be viewed and determining a storm track based time window for viewing the data. All observations of the selected types are then plotted in a storm relative view for the chosen time window and geography is positioned for the storm-center time about which an objective analysis can be performed. Users then make decisions about data validity through visual nearest-neighbor comparison and inspection. The project employed an Object Oriented iterative development method from beginning to end and its implementation primarily features the Java programming language. ^

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The concept of the quality control circle (QCC) has worked well in Japanese industry in increasing efficiency, production, and profits. The author explores the QCC, its history and advantages, and tells how it could be adapted quite easily and effectively to the hospitality industry

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This thesis chronicles the design and implementation of a Intemet/Intranet and database based application for the quality control of hurricane surface wind observations. A quality control session consists of selecting desired observation types to be viewed and determining a storm track based time window for viewing the data. All observations of the selected types are then plotted in a storm relative view for the chosen time window and geography is positioned for the storm-center time about which an objective analysis can be performed. Users then make decisions about data validity through visual nearestneighbor comparison and inspection. The project employed an Object Oriented iterative development method from beginning to end and its implementation primarily features the Java programming language.

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As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

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The service-producing industries have experienced problems in quality in the 1980s because of intense competition. The author discusses how these problems have been compounded in the fast food industry and how quality control can lead to success.

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In - Managing Quality In the Hospitality Industry – an observation by W. Gerald Glover, Associate Professor, Hospitality Management Program, Appalachian State University, initially Glover establishes: “Quality is a primary concern in the hospitality industry. The author sees problems in the nature of the way businesses are managed and discusses approaches to ensuring quality in corporate cultures.” As the title suggests, the author wants to point out certain discrepancies in hospitality quality control, as well as enlighten you as to how to address some of these concerns. “A discussion of quality presents some interesting dilemmas. Quality is something that almost everyone wants,” Assistant Professor Glover notes. “Service businesses will never admit that they don't provide it to their customers, and few people actually understand what it takes to make it happen,” he further maintains. Glover wants you to know that in a dynamic industry such as hospitality, quality is the common denominator. Whether it be hotel, restaurant, airline, et al., quality is the raison d’être of the industry. “Quality involves the consistent delivery of a product or service according to the expected standards,” Glover provides. Many, if not all quality deficiencies can be traced back to management, Glover declares. He bullet points some of the operational and guest service problems managers’ face on a daily basis. One important point of note is the measuring and managing of quality. “Standards management is another critical area in people and product management that is seldom effective in corporations,” says Glover. “Typically, this area involves performance documentation, performance evaluation and appraisal, coaching, discipline, and team-building.” “To be effective at managing standards, an organization must establish communication in realms where it is currently non-existent or ineffective,” Glover goes on to say. “Coaching, training, and performance appraisal are methods to manage individuals who are expected to do what's expected.” He alludes to the benefit quality circles supply as well. In addressing American organizational behavior, Glover postures, “…a realization must develop that people and product management are the primary influences on generating revenues and eventually influencing the bottom line in all American organizations.” Glover introduces the concept of pro-activity. “Most recently, quality assurance and quality management have become the means used to develop and maintain proactive corporate cultures. When prevention is the focus, quality is most consistent and expectations are usually met,” he offers. Much of the article is dedicated to, “Appendix A-Table 1-Characteristics of Corporate Cultures (Reactive and Proactive. In it, Glover measures the impact of proactive management as opposed to the reactive management intrinsic to many elements of corporate culture mentality.

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As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.