Short Programs

Value-Driven Tradespace Exploration for System Design

This course is currently only offered as a custom program. The below description should be taken as an example of content and can be tailored to meet company needs. If you have been thinking about a customized course for your group of 25 or more, please review additional information on the Custom Programs page.

Course Summary  |  Learning Objectives  |  Who Should Attend  |  Course Outline  | 
Participants' Comments  |  About the Lecturers  |  Updates

Course Summary

One of the key contemporary challenges in developing successful systems is to be able to make effective architectural design choices in the face of complexity and a changing world. The course presents a new method for tradespace exploration based on a value driven perspective, allowing designers to better understand and meet both present and future stakeholder needs and expectations. The Multi-Attribute Tradespace Exploration (MATE) methodology was developed at MIT for exploring tradespaces of possible architectures rather than settling quickly on an optimum. The power of the method comes primarily from the ability to quantitatively assess many design choices very early in the design process, along with gaining insight into how stakeholder preferences relate to technically feasible design alternatives. This ability allows system architects, designers and analysts to explore many design options, comparing many system designs on a common basis, to ensure “right” decisions are made at the beginning of system development. This capability enables quantitative assessment of factors such as variability in technical performance and cost, how they relate to stakeholder expectations, and the impact of changes in markets or government policy on the potential for system success by allowing exploration of a large number of possible situations, including speculative (“what if”) scenarios. The course will cover several advanced topics including system design for selected “ilities” (flexibility, adaptability, survivability, scalability, modifiability, and robustness) into the architecture; a systematic method for handling many types of uncertainties (requirements, technical, economic etc.); and integration with policy and product development issues. Strategic issues such as temporal considerations in tradespace exploration, use of real option approaches, and exploring complex tradespaces of families of designs and systems of systems will also be discussed.

Learning Objectives

The participants of this course will be able to:

1. Understand the motivation for and increasing importance of tradespace exploration in the context of the contemporary engineering environment and associated challenges.

2. Grasp how the value driven perspective can help designers better understand both present and future stakeholder needs and expectations.

3. Identify where in the system lifecycle the tradespace exploration and system design activities should be performed.

4. Understand fundamental concepts including stakeholder preferences, decisional vs. experienced value, latent value, attributes, design vectors, and Multi-Attribute Utility Theory.

5. Be aware of how the Multi-Attribute Tradespace Exploration (MATE) method has been applied to real-world systems, and implemented as a model-based method as well as “back-of-envelope” approach.

6. Choose and use appropriate tools for analysis of the tradespace, including Multi-Attribute Utility Theory, N-squared or Design Structure Matrix analysis, physical modeling, and analysis tools and software.

7. Set up and justify a tradespace analysis using system attributes, stakeholder utilities, and a scoped and quantified design vector.

8. Analyze a simple tradespace using the Multi-Attribute Tradespace Exploration (MATE) framework, and identify and assess the effects of risk, uncertainty, and/or policy effects using appropriate techniques.

9. Identify the metrics for quantifying changeability of a design, and active vs. passive strategies and methods for increasing perceived changeability of a system.

10. Gain insight into the latest thinking on advanced topics including valuation of selected “ilities”, uncertainty management, change taxonomy, and tradespace networks.

11. Have knowledge of strategic issues such as temporal considerations in tradespace exploration, and exploring complex tradespace for families of designs and systems of systems, with pointers to latest published literature in the field and topics of ongoing research.

Who Should Attend

This course is targeted for systems architects, systems engineers, system analysts, systems designers, product developers, technical strategists, and engineering decision makers involved in system/product architecting and design and/or the selection of designs from among a set of alternatives. The course will be of particular benefit to professionals who must make strategic technical decisions in the face of a dynamic environment involving complex decision drivers including multiple stakeholders, changing technologies, and constrained resources.

Outline of the Course

Day 1: Introduction to Value Driven Tradespace Exploration for System Design and Basics of Utility Theory
Participants will be introduced to the value driven perspective as a paradigm for system architecting and design. The motivations for and challenges of tradespace exploration will be discussed with regard to the contemporary engineering environment and methods in use. Practical approaches to setting up the tradespace will be described and experienced. Participants will be introduced to applied utility theory, including Multi-Attribute Utility, with exposure to the benefits and limitations of the theory.

Topics will include:

• Why Use Trade Studies?
• Motivations, Drivers, Challenges
• Benefits of the Value Driven Perspective
• Overview of Classical Methods for Architecting and Design
• Tradespace Exploration and Design in Context of System Lifecycle
• Basics of Architecting and Design Methods
• Introduction to Multi-Attribute Decision Making
• Exercise 1: Setting up a Simple Decision Space
• Introduction to Tradespace Exploration
• Exercise 2: Setting up a Simple Tradespace

• Basics of Applied Utility Theory
• Benefits, Limitations, and Considerations
• Comparison with Other Methods

Day 2: Utility Theory and Tradespace Modeling
Lectures will discuss elaboration of the tradespace, how to model and explore the tradespace, and include lessons from tradespace exploration from several studies. Participants will use a spreadsheet tool to explore an example tradespace to gain hands-on experience. Participants will learn about advanced concepts and approaches through looking at a number of case studies, including the lessons learned and use of the tradespace exploration results. A framework of uncertainty and risk will be discussed, along with strategies for managing uncertainty. There will be a deep discussion of uncertainty and risk, and how these are treated in the tradespace, with an example exercise. Challenges and issues from the decision maker perspective will be examined.

Topics will include:

• Modeling and Exploring the Tradespace
• Exercise 3: Exploring the Tradespace
• Lessons learned from Case Studies in Tradespace Exploration
• Enhancing SE Practice with MATE
• Uncertainty and Risk
  • Challenges, Considerations, and Issues for Decision Makers
• Exercise 4: Uncertainty on the Tradespace

Day 3: Strategic and Advanced Topics
The final day of the class will cover concepts and emerging thinking on several strategic and advanced topics. The use of enabling laboratories to enable exploration and using integrated concurrent engineering with MATE will be discussed. Strategic thinking on how to value “ilities” (e.g., flexibility, survivability, etc.) will be presented with examples of analysis. Lectures and interactive discussion will focus on strategic issues such as temporal considerations in tradespace exploration, exploring complex tradespace for families of designs and systems of systems, and influences on tradespaces.

Topics will include:

• Development of a Tradespace Exploration Laboratory
• Flexibility and other “ilities”
• Dynamic Tradespace Techniques
• Challenges of Tradespace Exploration and Decision Making in Highly Complex Systems
• Approaches to Value Robustness Over Time
• Exercise 5: Time on the Tradespace
• Policy and Other Non-Technical Influences on the Tradespace
• Summary of a New Method
  • Selected Research Highlights and Expected Future Research

Note: Various case studies and examples are used throughout the course to highlight concepts or demonstrate application of the Multi-Attribute Tradespace Exploration methodology. These include simple illustrative cases using familiar consumer products and engineering cases from space, aerospace, and transportation sectors. Participants do not require in-depth domain knowledge for lectures or the exercises, but basic engineering and design knowledge is assumed. Participants who are able to bring laptops to the class will have the opportunity to perform several exercises during the course to enhance their learning through use of a spreadsheet-based tool that will be provided. The exercises will be based on a real system example.

Participants' Comments

Manager of Software Design, TriZetto Group
“Very well put together. I have used the material in my profession within weeks of taking the course. I was able to easily reference the materials and revisit the topics I was looking for.”

Space Systems Engineer, The Aerospace Corporation
“Fabulous, the course was extremely useful, and we are pushing forward on applying it internally, as well as keeping MIT involved.”

Analyst, Defense Science & Technology Agency (DSTA)
The hands-on exercises helped translate theory to application."

Senior System Engineer, Northrop Grumman - Space Technology
“Excellent overview of the state-of-the-art. The course provided sufficient information and in-class practice to assess and justify the need for this capability. The preparation, presentation and teaching was of high caliber, commensurate with what is expected of a world-class university.”

About The Lecturers

Dr. Donna H. Rhodes
Donna Rhodes is currently a Senior Lecturer in the MIT Engineering Systems Division and a Principal Research Scientist in the MIT Center for Technology Policy and Industrial Development (CTPID). She is a co-founder and research lead for the MIT Systems Engineering Advancement Research Initiative (SEAri) and is also affiliated with MIT’s Lean Advancement Initiative (LAI). Dr. Rhodes has research interests and advises ongoing research projects in the following areas: advanced systems engineering methods; systems engineering leading indicators; defense and commercial systems practices and case studies; value based decision analysis; systems principles and practices applied to enterprises; managing for complexity and uncertainty; systems-of-systems engineering; and strategies for high performing enterprises in the engineering systems context. Prior to joining MIT, Dr. Rhodes had 20 years of experience in the aerospace/defense systems, systems integration, and commercial product industries, where she held senior management positions at IBM Federal Systems, Lockheed Martin, and Lucent Technologies. Dr. Rhodes has been very involved in the evolution of the systems engineering field, as well as the development of several university graduate programs. She has served on a number of boards and study panels to advance systems practice and education and to address issues of national and international importance. She has published numerous papers and research reports in the field of systems, and has co-authored industry and corporate engineering policies, standards, and reports. She has been an invited speaker for numerous international and national events in the field of systems. Dr. Rhodes received her Ph.D. in Systems Science from the T.J. Watson School of Engineering at SUNY Binghamton. She is a Past President and Fellow of the International Council on Systems Engineering (INCOSE), and is a recipient of the INCOSE Founders Award and several INCOSE Distinguished Service Awards. She is also member of IEEE, AIAA, and ASME.

Dr. Adam Ross
Adam M. Ross is currently a Research Scientist in the MIT Engineering Systems Division. He is one of the co-founders of the MIT Systems Engineering Advancement Research Initiative (SEAri) and lead research scientist on several research contracts. He actively conducts research and advises undergraduate and graduate students. Previously Dr. Ross was a Postdoctoral Associate with the MIT Center for Technology Policy and Industrial Development (CTPID) and a research assistant with the MIT Lean Aerospace Initiative. He has work experience with government, industry, and academia including NASA Goddard, JPL, Smithsonian Astrophysical Observatory, Boeing Satellite Systems, MIT, Harvard, and Florida State University, performing both science and engineering research. Dr. Ross has published papers in the area of system conceptual design and selection, including many publications describing his research and development of the Multi-Attribute Tradespace Exploration (MATE) method. He has presented research presentations, tutorials, and workshops on the MATE method to a number of government, industry, and academic organizations. He received his dual B.A. in Physics and Astronomy and Astrophysics from Harvard University in 2000, and his M.S. in Aeronautics and Astronautics and M.S. in Technology and Policy from MIT in 2003. He also holds a Ph.D. in Technology, Management, and Policy of Engineering Systems from MIT (2006). He is a member of INCOSE, IEEE, and AIAA.

In 2008-2009, Dr. Ross and Dr. Rhodes received four best conference paper awards and INCOSE journal paper of the year award for works relevant to this course. For more information on the MIT Systems Engineering Advancement Research Initiative (SEAri), and access to course reading list please visit http://seari.mit.edu.

Updates

There are no updates at this time.