Whom am I ?

Serial Entrepreneur

Disruptive Innovation is the automotive industry to lead on

Industrial mass customization?

How to

navigate

through the

scopes ?

Normative

pull

Financial

push?

Disruptive Innovation

Disruptions..

Vinder strategier

Solar energy disruption

I’m NOT in the business of

Hollywood Robotics

.. But I have experienced that Nature is a great

inspiration…

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

Enabling Tech Performance Price Index - 3D-RGB vision

Price

Pe

rfo

rma

nce

Modular Display Inspection

Delicate high precision glass mount • No fixture floating in high silicone

• Fixed by means of UV curing adhesive

• Perceptive flex feeding

Perception 4 real

Compliant assembly Compliant assembly and self

tuning flex feeding

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

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

The challenge for perceptive robotics

•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

Lots of analytics out there !

Introduction to the

A paradigm shift -Strategic Manufacturing

Concept

•

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!.

•

••

•

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..

Economic potentials

Plant level efficiency

= 80%

= 60%

STARGAME

Dedicated

Event: Ramp-up

Events: Machine breakdown, quality,

etc.

Did you ever wonder why

these Robots evolved this way ?

• Incremental

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..

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

•

•

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..

”Pick ´n place robotics” Vinder strategier

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

Re

l. s

up

po

iort

y

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

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

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 ?

Open robotics .. A big part of the future

• Open Source middleware

platform

– Cross HW platform

– Tapped into Open Robotics