Decision Analysis

Synopsis

Decision analysis supports system life cycle development throughout all phases and system hierarchical levels. The course presents the trade study process as part of the systems engineering process, and introduces various decision analysis methods, including the traditional trade study methods, trade space for Cost as Independent Variable (CAIV), Analytic Hierarchy Process (AHV) as a part of the Analytic Network Process (ANP), Potentially All Pairwise Rankings of All Possible Alternatives (PAPRIKA), and Decision Analysis with Uncertain Information/Data.

Key Topics

  • Understand the trade study process and its role in the overall systems engineering process.

  • Learn the traditional trade study methods: defining selection criteria, identifying weights, identifying alternatives, defining scoring criteria, scoring alternatives, calculating ratings for alternatives, and performing sensitivity analysis.

  • Learn how to develop decision trees as hierarchical guidance for different levels of trade studies.

  • Learn the trade study role and contribution to Cost as Independent Variable (CAIV).

  • Learn how to use and apply decision analysis methods including Analytic Hierarchy Process (AHV) as part of the Analytic Network Process (ANP), Weighted Sum Model (WSM), Potentially All Pairwise Rankings of All Possible Alternatives (PAPRIKA), and Decision Analysis with Uncertain Information/Data.

  • Learn how to write a credible, organized, structured, and thorough trade study report.

Who Should Attend: This course will be of interest to engineers, program and project managers, software engineers, information technology specialists, educators, and other professionals who need to learn how to make optimal decisions.

Course Information:
Type of Course: Instructor-Led Short Course
Course Level: Intermediate
Course Length: 2 days
AIAA CEU's available: Yes

 

Outline
  • Introduction
    • Why do we need trade study?
    • Systems engineering process overview.
    • Role of trade study and its process.
     
  • Traditional Trade Study Methods
    • How to determine selection criteria.
    • Methods (including QFD) for determining weights for selection criteria.
    • How to identify and down-select too many alternatives.
    • Introduce different scoring methods for selection criteria.
    • Score alternatives against selection criteria.
    • Calculate ratings for alternatives.
    • Perform sensitivity analysis.
    • How to write a credible, structured and thorough trade study report.
     
  • Develop decision trees.
  • Study trade space for CAIV.
  • The Analytic Hierarchy Process (AHP)
    • Discuss the process and the difference with Analytic Network Process (ANP).
    • Learn the methodology by following an example.
     
  • Make decision with Weighted Sum Model (WSM).
  • Potentially All Pairwise Rankings of All Possible Alternatives (PAPRIKA)
    • Build additive multi-attribute value models with an example.
    • Learn how to solve too many pairwise combinations and rankings problems.
     
  • Decision Analysis with Uncertain information/data
    • Maximax method
    • Maximin method
    • Criterion of realism method
    • Equally likely method
    • Minimax regret method
     
  • Conclusions
    • Risk Areas/Suggestions/Guiding Principles
Materials
 
Instructors

John C. Hsu, Ph.D., P.E., AIAA Fellow, ESEP (INCOSE), implemented the first break-through systems engineering (SE) application and a pioneer in developing and establishing SE process, metrics, templates, methods, and tools for the Boeing Company. He serves as an Adjunct professor at the California State University Long Beach teaching SE graduate course, The University of California at Irvine Certification Program, AIAA professional development short courses Lecturer, Royal Academy of Engineering Visiting Professor and Honorary Professor of Queens University. He has worked at The Boeing Company as Technical Manager, Project Manager, and Principal Investigator with over 30 years of diversified experience in systems engineering, aeronautical engineering, mechanical engineering, nuclear engineering, software development, and engineering management. John earned his Ph.D. in Mechanical and Aerospace Engineering, M.S. in Nuclear Engineering, M.S. in Mechanical Engineering and a registered Professional Engineer.

 

 

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