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Crash quality- an approach for evaluating spending on quality improvement initiatives
Title
Crash quality- an approach for evaluating spending on quality improvement initiatives.
Creator
Ferreira, Labiche, Hosni, Yasser A., Engineering
Abstract / Description
University of Central Florida College of Engineering Thesis; The quality movement has become popular among corporations big and small for one reason: empirical evidence suggests that quality and productivity (and hence profitability) are linked. Unfortunately, while many firms accept that quality and productivity go together, few actually track the gains associated with their quality improvement programs. Companies also tend to spend on quality improvement with no indication of estimation of...
Show moreUniversity of Central Florida College of Engineering Thesis; The quality movement has become popular among corporations big and small for one reason: empirical evidence suggests that quality and productivity (and hence profitability) are linked. Unfortunately, while many firms accept that quality and productivity go together, few actually track the gains associated with their quality improvement programs. Companies also tend to spend on quality improvement with no indication of estimation of the impact of funding on the targeted process. It would be of great value to know: (1) the impact of spending to enhance the product/process quality level, and (2) the point at which expenditures for quality improvement are not economical. This research involves modeling the quality level of a product composed of integrated components/processes and the costs associated with quality improvement. Presented in this research is a methodology for determining the point at which the target quality level is reached. This point signifies when future spending should be re-directed. The research defines this point as the "Crash Quality Point (CQP)." Cases of a single process level and double level three-stage process are modeled to conceptualize CQP. The finding from the output analysis reveal that the quality level approaches the target level at varying points in time. Any spending beyond this point does not have an impact on the quality level compared to the period prior to the Crash Quality Point. Spending past this point is futile and these funds could be spent on the quality improvement projects. The special case modeled also illustrates the use of this tool in the selection of processes for improvements based on the quality level of the process. This is an added advantage in scenarios where funds are limited and management is constrained to improve process quality with limited funds. Using a real world example validates the proposed CQP methodology. The results of the validation indicate that the model developed can assist managers in forecasting the budget requirements for quality spending based on the quality improvement goals. The tool also enables managers to estimate the point in time at which allocations of funds may be directed for process reengineering. The CQP method will enable quality improvement professionals to determine the economical viability and the limits in expenditures on quality improvement. It enables managers to evaluate spending alternatives and approximate when the point of diminishing return is reached.
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Date Issued
2000
Identifier
CFR0011594, ucf:53046
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFR0011594
An Engineering Analytics Based Framework for Computational Advertising Systems
Title
An Engineering Analytics Based Framework for Computational Advertising Systems.
Creator
Chen, Mengmeng, Rabelo, Luis, Lee, Gene, Keathley, Heather, Rahal, Ahmad, University of Central Florida
Abstract / Description
Engineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of...
Show moreEngineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of Computational Advertising. The key was to know the different fields very well and do the right selection. However, people first need to understand and be experts in the flow of the complex application of Computational Advertising and based on the characteristics of each step map the right field of Engineering analytics and traditional Industrial Engineering. Then build the apparatus and apply it to the respective problem in question.This dissertation consists of four research papers addressing the development of a framework to tame the complexity of computational advertising and improve its usage efficiency from an advertiser's viewpoint. This new framework and its respective systems architecture combine the use of support vector machines, Recurrent Neural Networks, Deep Learning Neural Networks, traditional neural networks, Game Theory/Auction Theory with Generative adversarial networks, and Web Engineering to optimize the computational advertising bidding process and achieve a higher rate of return. The system is validated with an actual case study with commercial providers such as Google AdWords and an advertiser's budget of several million dollars.
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Date Issued
2018
Identifier
CFE0007319, ucf:52118
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007319