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Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM).

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM)./
作者:	Gilbertson, Russell L.
面頁冊數:	1 online resource (246 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
標題:	Engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355463682

Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM).
Gilbertson, Russell L.

Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM). - 1 online resource (246 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2018.

Includes bibliographical references

Different classes of problems warrant different classes of solutions. There is no agreed set of unified principles and models to support systems engineering use over a wide range of domains. Nor is there a set of consistent terminology and definitions. These two deficiencies impede the adoption of systems engineering and create problems. On schedule delivery of a system meeting stakeholder needs at an acceptable cost is dependent upon selection and application of a system engineering method (SEM) appropriate for the class of system problem (COSP). Real world problems possess a degree of complexity that requires a commensurately complex approach as stakeholders are demanding increasingly capable systems that are growing in complexity, yet complexity-related system misunderstanding is at the root of significant cost overruns and system failures. INCOSE and IEEE recommend system complexity as a basis for selection and tailoring of SE processes; however, neither society provides a definition of complexity nor a methodology for SEM selection. Selection of a complexity appropriate SEM is dependent on understanding COSP which is currently difficult to define, observe, or measure. This research develops a diagnostic assessment model (DAM), based on the Cynefin framework, that infers COSP and then recommends a complexity appropriate SEM to reduce system miscategorization and therefore reduce the risk of system failure. An empirical healthcare case study is used to demonstrate SEMDAM's application and efficacy.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355463682Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM).
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502 $a Thesis (Ph.D.)--The George Washington University, 2018.
504 $a Includes bibliographical references
520 $a Different classes of problems warrant different classes of solutions. There is no agreed set of unified principles and models to support systems engineering use over a wide range of domains. Nor is there a set of consistent terminology and definitions. These two deficiencies impede the adoption of systems engineering and create problems. On schedule delivery of a system meeting stakeholder needs at an acceptable cost is dependent upon selection and application of a system engineering method (SEM) appropriate for the class of system problem (COSP). Real world problems possess a degree of complexity that requires a commensurately complex approach as stakeholders are demanding increasingly capable systems that are growing in complexity, yet complexity-related system misunderstanding is at the root of significant cost overruns and system failures. INCOSE and IEEE recommend system complexity as a basis for selection and tailoring of SE processes; however, neither society provides a definition of complexity nor a methodology for SEM selection. Selection of a complexity appropriate SEM is dependent on understanding COSP which is currently difficult to define, observe, or measure. This research develops a diagnostic assessment model (DAM), based on the Cynefin framework, that infers COSP and then recommends a complexity appropriate SEM to reduce system miscategorization and therefore reduce the risk of system failure. An empirical healthcare case study is used to demonstrate SEMDAM's application and efficacy.
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