Toward A Framework for Implementing Systems Engineering Development for Complex Systems Karl L. Brunson, GWU Thomas A. Mazzuchi, D.Sc., GWU Shahram Sarhani, Ph.D., GWU Jeffrey Beach, D.Sc., GWU

1
Toward A Framework for Implementing Systems
Engineering Development for Complex
SystemsKarl L. Brunson, GWUThomas A.
Mazzuchi, D.Sc., GWUShahram Sarhani, Ph.D.,
GWUJeffrey Beach, D.Sc., GWU
2
Outline

• What is the purpose
• Development of ICM Framework
• Life-cycle Risks
• Acquisition Life-cycle
• Complex System Work Breakdown Structure
• Framework Schedule Development
• Risk Assessment of Complex System

3
What is the Purpose?

• Provide a Comprehensive and Flexible Systems
  Engineering Development Framework for Complex
  Systems
• Builds on the strengths and principles of proven
  process models such as1
• Waterfall, V
• Iterative
• Spiral Development
• Agile
• Rapid Unified Process
• Applies key principles that are used throughout
  an acquisition life-cycle1
• Performs risk driven process tailoring throughout
  life-cycle phases
• Incremental Commitment Model
• Boehm, Barry and Lane, Jo Ann, Using the
  Incremental Commitment Model
• to Integrate System Acquisition, Systems
  Engineering and Software Engineering, USC, CSSE

4
Goal to achieve with the Framework
5
Schedule and Cost Risks
6
Life-Cycle Phases and Activities
Verification Validation Loop
Preliminary Detail Design Loop
Requirement Loop
Concept Design Loop
7
Define Work Breakdown Structure of Complex System
8
Develop Baseline Schedule for Complex System
9
Develop Schedule for each Framework
10
Map Risk Drivers to Schedule Tasks

• Risk drivers can be mapped to more than one
  task
• Risk assessments will be translated with
  triangular
• probability distributions for
  consequence/impact
• and with binomial distributions for the
  likelihood

11
Model Schedule Behavior with Risk Drivers

• Run Monte Carlo Simulations for each framework
• Outputs produce probability density
  distributions and
• binomial distributions that associates risk
  drivers to tasks
• via likelihood and consequence
• Indentifies critical path of each framework
• Quantifies the impacts and consequence of risk
  drivers
• Risk dependencies modeled via correlation

12
Risk Assessment of Complex System

• Cumulative distributions for schedule and costs
• Impact of risks on specific tasks
• Probabilistic critical paths for each framework
• identify tasks/activities that will most
  likely delay
• project
• Depends on risk
• Monte Carlo shows whether task was critical per
  iteration
• Correlation between tasks when risk driver
• affects durations
• Task durations can be negatively or positively
  correlated
• Framework selection based upon results of
  schedule and cost
• risk analysis of probability distributions
• Reveals optimal paths to risk reduction