Transcript Innovation in Engineering Education - IEEE-USA

Turning Vision
into Value
Innovation in Engineering
Education
9.1.07
Howard Richard Lieberman [email protected] 650-561-9000
Turning Vision
into Value
New World - Service Economy
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US GDP 80% Service
Generalists vs. specialists
T shaped people
Integrity and balance
Service Science emerging
Howard Richard Lieberman [email protected] 650-712-8100
Slide 2
Turning Vision
into Value
Engineering and Research in 2020
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Simulation vs. analytics
Distance vs. traditional learning
Engineering manager vs. engineer
Much more collaboration
New academic - industry relationships
Howard Richard Lieberman [email protected] 650-712-8100
Slide 3
Turning Vision
into Value
Intensives
 New educational models needed
 More pre and post processing
 More perspectives
 More asynchronous
 Less linear - more like reality
Howard Richard Lieberman [email protected] 650-712-8100
Slide 4
Turning Vision
into Value
Very Different Teams
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No longevity
Have not worked together before
Formed and disbanded on project basis
Disparate cultures
Differing identities, beliefs, values,
behaviors and expectations
Howard Richard Lieberman [email protected] 650-712-8100
Slide 5
Turning Vision
into Value
21 St Century Project Management
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Much
Much
Much
Much
smaller plans
faster development times
more flexibility required
more collaboration
Howard Richard Lieberman [email protected] 650-712-8100
Slide 6
Turning Vision
into Value
ISPER Process
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Imagine
Scope
Plan
Execute
Review
Howard Richard Lieberman [email protected] 650-712-8100
Slide 7
Turning Vision
into Value
Innovation Dashboard
There has to be a way for
everyone to see what you
mean by innovation!
Indicator
Observers
Shared
Perspective
Indicators
of
Innovation
Howard Richard Lieberman [email protected] 650-712-8100
Slide 8
Turning Vision
into Value
Measuring Innovation
 Measurement from a scientific perspective requires repeatability.
 Objective third parties should obtain similar results to each
other.
 This is a basic tenet of science and engineering.
 Quantification also implies the existence of units.
 Before leaping into specific numerical characterizations it is often
useful to look at relative indicators of any given phenomena.
 Innovation is no exception - the qualitative usually precedes the
quantitative. Just as the relative precedes the absolute.
 Once qualitative vocabulary and attribute importance are shared
... quantitative accuracy can follow.
Howard Richard Lieberman [email protected] 650-712-8100
Slide 9
Turning Vision
into Value
Innovation Indicators
conversations
advocates
new products
projects
champions
inventions
prototypes
resources
external ideas
failures
new features
outreach
indicators precede metrics
Howard Richard Lieberman [email protected] 650-712-8100
Slide 10
Turning Vision
into Value
Open Source
 Major efforts not owned by one entity.
 Traditional IP models do not support
collaboration
 Quality control is a big problem.
 Resource is infinite.
Howard Richard Lieberman [email protected] 650-712-8100
Slide 11
Turning Vision
into Value
Open Source Handbook
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Innovation Management Handbook
Traditional morphing into Online
Many voices aggregated into volumes.
Invite people to get involved.
OSH.SVII.ORG (in progress)
Howard Richard Lieberman [email protected] 650-712-8100
Slide 12