Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM)
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Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM) by Russell L. Gilbertson Bachelor of Electrical Engineering, June 1983, University of Minnesota Master of Business Administration, December 1985, Rensselaer Polytechnic Institute A Dissertation submitted to The Faculty of The School of Engineering and Applied Science of the George Washington University in partial fulfilment of the requirements for the degree of Doctor of Philosophy January 19, 2018 Dissertation directed by Bereket Tanju Professorial Lecturer in Engineering Management and Systems Engineering and Timothy Eveleigh Professorial Lecturer in Engineering Management and Systems Engineering The School of Engineering and Applied Science of The George Washington University certifies that Russell L. Gilbertson has passed the Final Examination for the degree of Doctor of Philosophy as of October 24, 2017. This is the final and approved form of the dissertation. Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM) Dissertation Research Committee Bereket Tanju, Professorial Lecturer in Engineering Management and Systems Engineering, Dissertation Co-Director Timothy Eveleigh, Professorial Lecturer in Engineering Management and Systems Engineering, Dissertation Co-Director Shahram Sarkani, Professor of Engineering Management and Systems Engineering, Committee Member Thomas Mazzuchi, Professor of Engineering Management and Systems Engineering & of Decision Sciences, Committee Member Amir Etemadi, Assistant Professor of Engineering and Applied Science, Committee Member ii Dedication I wish to dedicate this work to my wife, Ms. Debra Daniels who supported me throughout this endeavor and my mother, Ms. Sandra J. Gilbertson, who passed away before I completed this work, but inspired me to always keep learning. In addition to proving to myself that I could do this, I wanted to demonstrate to my children and their spouses; Jason & Irina, Daniel & Tabitha; my step-children and their spouses, Eric, Melynda & Shawn; and my grandchildren, Elizabeth, Declan, and Finley, that it is never too late to learn. iii Acknowledgements I would like to acknowledge the assistance, advice and guidance provided by Drs. Bereket Tanju, Timothy Eveleigh, Thomas A. Mazzuchi, Shahram Sarkani, Steven Stuban, and Jason Dever from the Department of Engineering Management & Systems Engineering (EMSE), School of Engineering and Applied Science (SEAS) of The George Washington University. I would like to thank Dr. Jimmie McEver, John Hopkins University Applied Physics Laboratory, and Dr. John MacCarthy, Director, Systems Engineering Education Program, Institute for Systems Research, University of Maryland-College Park, for taking time to meet and discuss my research. Much of the research methodology and approach was developed while teaching at the A. James Clark School of Engineering, University of Maryland for Dr. MacCarthy – an experience for which I am truly grateful. I would also like to thank Dr. Sarah Sheard, Software Engineering Institute, Carnegie Mellon University, and Dr. Brian E White, MITRE (Retired), for sharing their work electronically via ResearchGate and providing comments along the way. This dissertation would have been much different without my George Washington University systems engineering cohort classmates Dr. Alan Ravitz and Dr. Blake Roberts. Alan peaked my interest in medical systems and introduced several technologies that helped significantly. Blake provided a sounding board and support throughout the entire process. Thank you, gentlemen. Finally, I would not have thought that obtaining a PhD degree while working was possible if not for Dr. Jason Siebel who shared his positive experiences with the George Washington University’s SEAS/EMSE program during a time we worked together. iv Abstract Applying the Cynefin Sense-Awareness Framework to Develop a Systems Engineering Method Diagnostic Assessment Model (SEMDAM) 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. v Table of Contents Dedication ......................................................................................................................... iii Acknowledgements .......................................................................................................... iv Abstract ...............................................................................................................................v Table of Contents ............................................................................................................. vi List of Figures .................................................................................................................. xii List of Tables .................................................................................................................. xiv List of Acronyms ..............................................................................................................xv 1 Introduction ................................................................................................................1 1.1 GENERAL DESCRIPTION OF THE PROBLEM ............................................................4 1.2 MAJOR RESEARCH QUESTIONS .............................................................................7 1.3 SIGNIFICANCE & JUSTIFICATION ...........................................................................8 1.4 SCOPE AND LIMITATIONS ......................................................................................9 1.5 OVERVIEW OF DISSERTATION .............................................................................10 2 Literature Review ....................................................................................................12 2.1 SE THEORETICAL FOUNDATIONS ........................................................................15 2.1.1 Theories from Philosophy ..............................................................................16 2.1.2 Theories from Classical Sciences ..................................................................18 2.1.3 Theories from Systems Science ......................................................................20 2.1.4 Summary of SE Theoretical Foundations ......................................................31 2.2 DEFINITION OF SYSTEM USED .............................................................................32 2.2.1 System Life Cycle Model ................................................................................33 2.2.2 System Function .............................................................................................34 2.2.3 System Structure.............................................................................................35 vi 2.2.4 System Behavior .............................................................................................36 2.3 ENGINEERING ORDERED SYSTEMS (EOS) ...........................................................37 2.3.1 EOS Standards of Practice ............................................................................40 2.3.2 Classical Sciences Assumptions Underpinning EOS .....................................42 2.3.3 Codifying TSM ...............................................................................................48 2.3.4 Codifying SoSM .............................................................................................50 2.4 ENGINEERING UN-ORDERED SYSTEMS (EUOS) .................................................54 2.4.1 EUOS Standards of Practice .........................................................................55 2.4.2 Codifying ESM ...............................................................................................56 2.4.3 Codifying CSM ...............................................................................................59 2.5 CYNEFIN SENSE-MAKING FRAMEWORK ...............................................................60 2.5.1 Introduction to Sense-Making ........................................................................60