Engineering Methods for
Decision-Making
Warren P. Seering
Dryden Flight Research Center
©Massachusetts Institute of Technology WP Seering - 121202 2
The Dilemma
Resource Demand by Year
900
Potential
Projects
Resources Available for Projects
Approved Projects
Support
0
Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Source: V. Chacon SDM Thesis MIT and NASA Dryden
©Massachusetts Institute of Technology WP Seering - 121202 3
Objective Function
Objective: 3 to 5 year demand created by each project.
Measured by averaging the length of a project, the amount
of staff used, and the stability of the demand created by the
project; desired project length is 4 years, desired staff
quantity is 50 people assigned.
Length
(Length of the project / 4 years) * 100
Staff
(# of the staff on the project / 50 staff) * 100
Stability
A measure of the changes or challenges experienced
by a project (100 equates to no changes; 0 equates to
major changes)
Demand
(Length + Staff + Stability) / 3
©Massachusetts Institute of Technology WP Seering - 121202 4
Historical Performance
20
30
40
50
Source: V. Chacon SDM Thesis MIT and NASA Dryden
60
70
80
90
100
110
120
©Massachusetts Institute of Technology WP Seering - 121202 5
Controllable Factors
elements
Budget
[Planned/actual]*100
Budget stability
Staff
100=no changes
0=major changes
[core capabilities used/17]*100
17 total core capabilities available
Role of core capabilities in the project
100=leading, 65=assisting, 30=consulting
Customer dependency on core capability
100=total dependency
0=no dependency
100=desired mix
0=all in-house or all contract
Required risk mitigation level
100=high, 65=average, 30=low
Skill level of assigned staff
100=expert, 65=average, 30=trainee
[Planned/actual]*100
Staff stability
100=no changes
Core capabilities
Core capability need
Customer dependency
In-house/contract mix
Safety risk
Staff skill
Technical gain
Technical risk
0=major changes
Technical knowledge gained from project
100=high, 65=average, 30=low
Required risk mitigation level
100=high, 65=average, 30=low
©Massachusetts Institute of Technology WP Seering - 121202 6
Source: V. Chacon SDM Thesis MIT and NASA Dryden
Factor Effect on S/N
Match
Matchwith
withCustomer
CustomerNeeds
Needs
Commitment
Commitment
1
1
2
3
2
3
-21.00
-21.00
-22.00
-22.00
-23.00
-23.00
-24.00
-24.00
-25.00
-25.00
-26.00
-26.00
-27.00
-27.00
S/N
S/N
-23.30
-23.30
S/N
S/N
-21.00
-21.00
-22.00
-22.00
-23.00
-23.00
-24.00
-24.00
-25.00
-25.00
-26.00
-26.00
-27.00
-27.00
-25.12
-25.12
-26.20
-26.20
-25.85
-25.85
level
level
Technical
TechnicalGain
Gain
-21.00
-21.00
S/N
S/N
-23.00
-23.00
-25.00
-25.00
1
1
-22.14
-22.14
-25.12
-25.12
2
2
-25.02
-25.02
-27.00
-27.00
3
-22.00
-22.00
level
level
-23.00
-23.00
3
-25.12
-25.12
-28.19
-28.19
-29.00
-29.00
2
1
2
3
-22.88
-22.88
-25.12
-25.12
-26.28
-26.28
3
-25.12
-25.12
-26.18
-26.18
level
level
Risk
RiskAccepted
Accepted
S/N
S/N
-19.00
-19.00
1
-24.00
-24.00
-25.00
-25.00
1
1
2
2
-24.65
-24.65
3
3
-24.09
-24.09
-25.12
-25.12
-26.00
-26.00
-27.00
-27.00
-25.12
-25.12
-26.61
-26.61
level
level
©Massachusetts Institute of Technology WP Seering - 121202 7
Predicted Improvements
after
before
20
30
40
50
Source: V. Chacon SDM Thesis MIT and NASA Dryden
60
70
80
90
100
110
120
©Massachusetts Institute of Technology WP Seering - 121202 8
Improvement
Resource Demand by Year
900
Potential
Projects
Resources Available for Projects
improvement
Approved Projects
Support
0
Year 1
Year 2
Source: V. Chacon SDM Thesis MIT and NASA Dryden
Year 3
Year 4
Year 5
Year 6
©Massachusetts Institute of Technology WP Seering - 121202 9
Benefits
ƒ Reduce volatility of decision outcomes as a way
for managing risk.
ƒ A fresh approach to decision-making and the
analysis and optimization of decision performance.
ƒ A method that makes decisions’ outcomes more
immune to uncontrollable factors.
ƒ Repeatable processes based on proven analytic
engineering methods.
©Massachusetts Institute of Technology WP Seering - 121202 10