1. Whom am I ?
Serial Entrepreneur

2. Disruptive Innovation
is the automotive industry to lead on
Industrial mass customization?

3. How to
navigate
through the
scopes ?
Normative
pull
Financial
push?

4. Disruptive Innovation

5. Disruptions..
Vinder strategier

6. Solar energy disruption

7. I’m NOT in the business of
Hollywood Robotics
.. But I have experienced that Nature is a great
inspiration…

8. 3D scanning
• This is ! 1992
• IP69K
• Range: Ø300 - Ø1800
• Productivity: 75 (optional 250) rør/time
• Accuracy: +/- 0.16 mm ( euklidisk 3D 3 *
sigma)
• Interface:
• Async process integration
• Communikation: Networkærk TPC/IP, Digital
I/O, Seriel CAN-bus.
• Scan density:4 mio. points, 0.3 mm
• Computer: Pentium II 266MHZ, Microsoft
Window NT 4.0
• Laser: 30mW/ 690 nm. Class IIIA

9. Enabling Tech Performance Price Index - 3D-RGB vision
Price
Performance

10. Modular Display Inspection

11. Delicate high precision glass mount• No fixture floating in high silicone
• Fixed by means of UV curing adhesive
• Perceptive flex feeding

12. Perception 4 real
Compliant assembly Compliant assembly and self
tuning flex feeding

13. Acquired by Adept Technology januar 2011 relocated to
Apdet Q Plesanton Cal,Dec. 2011.

14. Job2Bdone Marketing strategy Ref. HBS Prof. Clayton Christensen, founder InnoSIGHT Inc.

15. The challenge for perceptive robotics

17. •Price conscious
•Cost efficient
•Productivity
•Maintain quality
•Good ambassadors
•Time to market/volume
•Lead time
•Differentiated service levels
•Increased frequency of product intro.
•Increased product range + variants
•Mass customer designed products
•Existing products to new customers
•Full service/system provider
•Increased availability to products
+
+
+
+
60’s 70’s 80’s 90’s 2000 +
COMPETITIVE PRIORITIES

18. Lots of analytics out there !

20. Introduction to the
A paradigm shift -Strategic Manufacturing
Concept
•

22. What we are up to..
• Unpredictable market forecasts
leads to misperceptions that
effects
• Leak of precise control of inventory
• Quality of deliverability/response time
• Utilization of equipment – ROI suffering
• Frustration and mistrust
– LEAN manufacturing does not SOLVE these challenges well!.

23. •
•
•
•

24. Customer habits and market trend changes fast
and repeatable
• new value networks representing tremendous
growth platform are not addressed profoundly
enough to get in shape – in time!
• Suspicious market segmentation
• NOT by the job costumers are trying to get done BUT rather
due to data availability structures
• types of products and product attributes
• Price point
• Demographics (consumer products) or Industry verticals (small, medium,
global )
• So become Job to done oriented instead
of costumer oriented !!
– Remember this is what set the target to which
developments are oriented !!
What we are up to..

26. Economic potentials
Plant level efficiency
 = 80%
 = 60%
STARGAME
Dedicated
Event: Ramp-up
Events: Machine breakdown, quality,
etc.

27. Did you ever wonder why
these Robots evolved this way ?
• Incremental

28. Industrial robotics innovation looks
mostly like bodybuilding
• Not Pretty!
• Odd performance
factors that does not
match real world
problems
• But the hype is there
• And top level Robotics
Researchers and
Industry Innovators,
does thinks diffrently..

29. If they where perfect ,
the packaging world would look like this!
• Parts would be served in the
outer boundary of the robots
workspace, as the pick operation
is generally significant less
demanding than the associated
placement operation, and the
vertical range similar less varying
• Targets ( boxes, tray etc.) would
have to be positioned as close to
the center of the robots
workspace, and consequently
adopt to a cheese-cut triangular
space
• And pallets becomes a disk
•
•

30. How come ?!
The great minds that have
been researching this area
for decades now, they are
NOT stupid!
So how come ?
Well ! A deep dive into how these
Robots are research, benchmarked
and marketed gave the explanation.
Key performance factors are:
1) Workspace in terms of reach
2) Payload in terms of mass
3) Cycle time in terms of STD cycles
This is NOT a
gripper
problem!
Nor BAD
engineering!
Networking in the Industry and applied
science , experimenting and conducting
intensive analytic and numerical tests and
investigations leads to a new approach..
Which turned up 14 month later to prove a
highly viable and inheriting the power to
disrupt classic robotics..

31. ”Pick ´n place robotics”
Vinder strategier

32. Genetic optimal – Job focus
8%
10%
37%
16%
9%
6%
43%
15%
37%
33%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
rel. Suppirority
Rel.suppoiorty
Rel diff.10K (ethan) og Specialiseter (WR)
100m 10.35 9.58
long jump 8.03 8.95
Ball push 14.55 23.12
High jump 2.05 2.45
400m 46.9 43.18
110m urdle 13.56 12.8
Disco´s 42.53 74.08
Pole jump 5.2 6.14
Spier though 61.96 98.45
1500m 04:33.3 03:26.0
Disciplines 10K OR Forskel
100m 10.35 9.58 8% Usain Bolt (JAM)
Long jump 8.03 8.95 10% Mike Powell (USA)
Ball push 14.55 23.12 37% Randey Barnes
High jump 2.05 2.45 16% Javier Sotomayor
400m 46.9 43.18 9% Michael Johnson (USA)
110m hurdle 13.56 12.8 6% Aries Merritt
Discos 42.53 74.08 43% Jürgen Schult
Pole jump 5.2 6.14 15% Sergey Bubka
Spire though 61.96 98.45 37% Jan Železný
1500m 04:33.3 03:26.0 33% Hicham El Guerrouj
If we just
could
clone
Usain Bolt
Ethan your
great , but
not perfect

33. We all share the same challenges .. its domain invariant
Novel Vision Sensor
Novel Industrial Robotics
Bionic gripping
Stochastic Optimal Process Control
Modular Mechatronics Platform
Productivity & Yield optimization
Novel Mobile Robotics
eLearning competence bridge
3ed party equip. integration
Fish sorting,
grading,
batching, styling
& packaging
Meat, pork and
poultry sorting,
grading,
batching, styling
& packaging
Bakery & Dairy
sorting, grading,
batching, styling
& packaging
Fruit & vegs
sorting, grading,
batching, stying
& packaging
Convenience
Food grading,
portioning,
batching &
styling

34. 1. Industrial automation does not exist in a void. It is
HYPER-connected into a value networks with both
vertical and horizontal integrated companies in
both the Domain and Vendor value chain,
technology platforms and brand owners.
2. The core enabling technologies are adopted from
the appliance automation industry into less
deterministic settings has failed to prove feasible
and not reached a general acceptance, despite an
dramatically increased incentives and technology
anticipations
3. Supply from general Robotics and Machine Vision
System Vendors has not eased the tough barrier of
handling the inherited variance associated with i.e.
natural food, and still stumbles
4. Domain oriented processing primary equipment
and system suppliers has taken an approach to
design in simple robotics to their portfolio, but
faced huge challenges to complete the jobs, and
gain profitability
Get the business model right ! Main assumptions
What's the problem ? Why hasn't it already taken off ?

35. Open robotics .. A big part of the future

36. • Open Source middleware
platform
– Cross HW platform
– Tapped into Open Robotics