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Value of Systems Engineering INCOSE Data 1 Value of Systems Engineering INCOSE Data 1

Heuristic Claim of SE • Better systems engineering leads to • Better system quality/value Heuristic Claim of SE • Better systems engineering leads to • Better system quality/value • Lower cost • Shorter schedule Traditional Design SYSTEM DETAIL PRODUCTION DESIGN INTEGRATION Risk Time TEST Risk “System Thinking” Design Saved Time/ Cost Time 2

NASA Tracking 1980 s Total Program Overrun 32 NASA Programs % Investment in System NASA Tracking 1980 s Total Program Overrun 32 NASA Programs % Investment in System Engineering Effort (SEE) 3

Time-Phased Sensitivity of SE to Total System Life Cycle Cost % Commitment to Technology, Time-Phased Sensitivity of SE to Total System Life Cycle Cost % Commitment to Technology, Configuration, Performance, Cost, etc. 100 75 Cost Incurred 50 System-Specific Knowledge 25 Ease of Change N Detail Construction E Conceptual. Design and And/or E Preliminary Design Development Production D System Use, Phase out, and Disposal Commitment, System-Specific Knowledge, and Cost Systems Engineering is important early in a program to influence the design when 4 incurred costs are low and design changes are easy.

Concept and Technology Development Conceptual System Design Phase High Preliminary System Design Phase Detail Concept and Technology Development Conceptual System Design Phase High Preliminary System Design Phase Detail Design and Development Phase Systems Engineering Design Influence Individual Design Disciplines Low Systems Design and Development Progress Systems Engineering is important early in a program to influence the design when incurred costs are low and design changes are easy. 5

Conclusions • SE effort improves development quality • Cost, schedule, subjective • Hypothesis is Conclusions • SE effort improves development quality • Cost, schedule, subjective • Hypothesis is supported by the data • Optimum SE effort is 10 -15% • Matches data from NASA projects • Cost, schedule overruns are minimized • However, note wide dispersion of data • Quality of the SE effort matters • Lower quality SE reduces effectiveness 6