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Applying the Cynefin Sense-Awareness...
~
Gilbertson, Russell L.
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,印刷品 : 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.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
246 p.
附註:
Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:
Dissertation Abstracts International79-03B(E).
標題:
Engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10636163
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).
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 246 p.
Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Thesis (Ph.D.)--The George Washington University, 2018.
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.
ISBN: 9780355463682Subjects--Topical Terms:
561152
Engineering.
Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM).
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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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