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Preparing systems engineering and co...

McCray, Wilmon Wil L., Jr.

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Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance./
Author:	McCray, Wilmon Wil L., Jr.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2014,
Description:	237 p.
Notes:	Source: Dissertations Abstracts International, Volume: 76-03, Section: B.
Contained By:	Dissertations Abstracts International76-03B.
Subject:	Science education. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3639802
ISBN:	9781321246162

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance.
McCray, Wilmon Wil L., Jr.

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance. - Ann Arbor : ProQuest Dissertations & Theses, 2014 - 237 p.

Source: Dissertations Abstracts International, Volume: 76-03, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2014.

This item must not be sold to any third party vendors.

The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI ®) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization model and dashboard that demonstrates the use of statistical methods, statistical process control, sensitivity analysis, quantitative and optimization techniques to establish a baseline and predict future customer satisfaction index scores (outcomes). The American Customer Satisfaction Index (ACSI) model and industry benchmarks were used as a framework for the simulation model.

ISBN: 9781321246162Subjects--Topical Terms:

521340
Science education.
Subjects--Index Terms:

Analytical techniques

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance.
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500 $a Publisher info.: Dissertation/Thesis.
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506 $a This item must not be sold to any third party vendors.
520 $a The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI ®) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization model and dashboard that demonstrates the use of statistical methods, statistical process control, sensitivity analysis, quantitative and optimization techniques to establish a baseline and predict future customer satisfaction index scores (outcomes). The American Customer Satisfaction Index (ACSI) model and industry benchmarks were used as a framework for the simulation model.
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653 $a Computer science
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653 $a Gap in-process improvement and quantitative techniques taught to software engineering
653 $a Process architecture
653 $a Process infrastructure and quality management system (qms)
653 $a Quantitative management techniques to manage engineering business processes for performance using advanced statistical techniques
653 $a Software engineering and systems engineering measurements / metrics
653 $a Statistical techniques
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