Technology and Knowledge Transfer under the Open Innovation Paradigm
-
Upload
pedro-parraguez -
Category
Documents
-
view
226 -
download
7
description
Transcript of Technology and Knowledge Transfer under the Open Innovation Paradigm
Technology and Knowledge Transfer under the Open Innova8on Paradigm
The Problems of Discovery and Matching Between “Technology Push and Pull”
www.openinnovate.co.uk
Pedro Parraguez Ruiz [email protected]
Presenta(on Content
Findings Conclusions
From literature
and interviews
Proposed models and tools
Research triggers
Objec(ves
Areas of study
Final remarks
Context
2
www.openinnovate.co.uk
Context
Findings Conclusions
From literature
and interviews
Proposed models and tools
Research triggers
Objec(ves
Areas of study
Final remarks
Context
3
www.openinnovate.co.uk
Research triggers
Open Innova8on boHlenecks and
unfulfilled promises
Disconnec8on between tech
transfer, knowledge transfer and OI
Inadequate IT tools to deal with the data deluge in OI and tech transfer
4
Research objec(ves
• Review and analysis of the most common barriers to successful technology transfer as well as of the tools and methods already developed to deal with them.
• Create a new integral framework to model and understand technology and knowledge transfer processes under the open innova8on paradigm.
• Propose a process or system to improve the main T&K transfer issues iden8fied.
5
www.openinnovate.co.uk
Research nature
Rela(onal instead of transac6onal
T&K mapping, scou(ng and sourcing
Precursors of innova8on, the detec8on of knowledge transfer opportuni(es,
collabora(on and co-‐crea(on 6
www.openinnovate.co.uk
Technology and Innova8on
Management
TIM
Management of Innova8on processes
Models & Paradigms
Open Innova8on
Technology & Knowledge Transfer
Innova8on/Design Theories
C-‐K Engineering Design Theory
Methods & Techniques TRIZ
Knowledge & Informa8on Management
Informa8on Technology
Tools
Seman8c Analysis
Informa8on Aggrega8on and
Clustering
Data Mining
Context Domain Area Subject
Areas of study
7
0
50
100
150
200
250
300
350
400
450
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Volume of publica(ons indexed in ISI Web of Knowledge per topic per year
Technology Transfer Knowledge Transfer Open Innova8on C-‐K Design Theory TRIZ
Volume of publica(ons per area and (meline
8
www.openinnovate.co.uk
Volume of ISI publica(ons about TT and OI
1 3 9 6 0
50
100
150
200
250
300
350
400
450
2003 2004 2005 2006 2007 2008 2009
Volume of publica(ons indexed in ISI Web of Knowledge per topic per year
Technology Transfer Open Innova8on Technology Transfer & Open Innova8on 9
www.openinnovate.co.uk
R D
i
Research: usually in Universities and Research Centres. Motivated by scientific curiosity and disruptive discoveries.
Development: Increasingly in high tech SMEs (ex spin offs). Sometimes in big corporations and universities.
Innovations: Due to the need of market expertise and commercialization players are usually successful mainly in global companies.
needs
needs
needs
offers
offers
The full R&D + i potential is highly distributed and requires collaboration and co-creation to be exploited
Science + Eng
Engineering & design
marketing
The gaps between R, D and i
www.openinnovate.co.uk
If it doesn’t have commercial prospects
If there is no interestin the offer
If it hascommercial
value
If there isan interested
party
Research FundingResearch centre infrastructure and
accumulated knowledgeScientific Discovery
Evaluation of the discovery/invention and its potential applications
Scientific Publication
Application for a patent or other
IP rights
Technology is “packed” to be offered
in the market
Patent becomes part of the passive portfolio
of IP
Negotiations to licence, sell or create
an spin-off
Final transaction and exchange of IP
Generation Evaluation and Selection Technology Push Transaction
TTO usually does not get involved
TTO offers support and expertise in commercial evaluation and IP
Usually TTO is fully responsible for this process
Once IP is clearedit is possible to publish
www.openinnovate.co.uk
Technology Transfer VS Intermediated Open Innova(on
Visual model
11
If it doesn’t have commercial prospects If there is no interest
in the offer
If it hascommercial
value
Scientific Publications
Technology is “packed” to be offered in the
market
Passive patents
Final transaction and exchange of IP
Open innovation networksCompany with a need
Technology Push Technology Pull
Researchers
Classic university technology transfer model Open innovation through innov. intermediaries
www.openinnovate.co.uk
Interac(ons and Problems under Technology Push-‐Pull
Issues:
• Linear process: Low itera8on and co-‐crea8on à lack of feedback loops.
• Middle point is non existent.
• Problems of iden8fying opportuni8es and knowledge • More than 1792 ac8ve needs (Innocen8ve + Ninesigma + Yet2.com + others. August 2010)
• Con8nuous explicit knowledge genera8on (papers, patents...)
Visual model
12
Final transactions and exchanges of
IP
Technology Push Technology Pull
Researchers
Researchers
Researchers
Researchers
Company with a need
Company with a need
Company with a need
Company with a need
Company with a need
Open innovation networks
www.openinnovate.co.uk
Interac(ons and Problems under Technology Push-‐Pull
Visual model
13
Open Innova(on Brokers
14
www.openinnovate.co.uk
Screencast: Innocen8ve, Ninesigma and Yet2.com
=> A fragmented landscape of technology brokers with a few big players
=> Yet2.com technology offers: 5067
Open Innova(on Brokers
15
www.openinnovate.co.uk
Findings
Findings Conclusions
From literature
and interviews
Proposed models and tools
Research triggers
Objec(ves
Areas of study
Final remarks
Context
16
www.openinnovate.co.uk
Final transactions and exchanges of
IP
Technology Push Technology PullResearchers
Researchers
Researchers
Researchers
Company with a need
Company with a need
Company with a need
Company with a need
Company with a need
Open innovation networks
Virtual hub for “discovery and matching”
Company with a needNegotiations and
collaboration
www.openinnovate.co.uk
The case for a virtual hub
Discovery and Matching
17 Drawing the fron8er of what is possible…
www.openinnovate.co.uk
Integra(ve Framework
C-‐K Open Innova(on
Tech Transfer ?
18
Tradi8onal Concept-‐
Knowledge Design Theory
Armand
Hatchuel and Benoît Weil
K: Knowledge, something that is known
to be true or false
C: Concepts, something for which is currently not
possible to say if it is true or false
www.openinnovate.co.uk
K(b)
K(a)
K(c)
K(d)
K(e)
C 1
C 3C 2
C 4 C 5
C 7C 6
Concept Space Knowledge Space
Conjunction C->K
Disjunction K->C
K(f) new
C->C K->K
K->C
The knowledge space contains
explicit expertise databases and technologies. It is structured as islands each of
them representing
different domains.
Concepts evolve overtime
partitioning themselves in
continuous interaction with K. At the end of the process (by
means of a conjunction) new
knowledge (embodied for example in a
new product) is produced (C7).
The sourcing of the required knowledge to materialize a concept into new knowledge (or technologies) is the critical step where this study is focused.
This can be seen graphically in the disjunction K(c)->C(2).
Concepts are defined and constrained by a list of
requirements (to fulfil the objectives of a required new
product or process).
Knowledge can be internal or external to the organization. At the end of a
successful design process a concept will be always transformed in new
knowledge (in this case technologies are included in the definition of K)
Tradi8onal Concept-‐
Knowledge Design Theory
Armand
Hatchuel and Benoît Weil
20
www.openinnovate.co.uk
K(Papers)
K(Patents)
K(g)
K(c)
K(f)
C 1
C 3C 2
C 4 C 5
C 7C 6
Concept Space Knowledge Space
Conjunction C→K
Disjunction K->C
K(j) new
C→C
K→K
T&K offer
K→C Knowledge can be
identified, clustered
and aggregated as needed, curating and
indexing relevant
databases.
Technology Needs
Concepts can evolve and interact with different sources of K till they are
mature enough to be transfered.
To connect C with a relevant K, the aggregated database of each of them can be explored and matched semantically with the help of TRIZ. This
generates relevant alerts through a dashboard.
At the individual firm level
K(h)
K(a) K(b)
K(d)K(e)
K(i)
K(α) Company
C→K
Concept-‐Knowledge
Design Theory re-‐
interpreta8on
(Firm level)
21
www.openinnovate.co.uk
C Timeline. Analogue to TRL
Concept-‐Knowledge
Design Theory re-‐
interpreta8on
(Aggregated level)
22
www.openinnovate.co.uk
K(Papers)
K(Patents)
K(g)
K(c)
K(f)
C2
Concept Space Knowledge Space
K(N1, N2, N3) new
The visualization shows Cs at two different stages. The smaller nodes represent individual needs in T=1 while the big nodes represent clustered groups of needs ready to
be matched with relevant K in T=2. The clusters “Speed”, “Feedback” and “Segmentation” are only examples of
underlying common problems for those needs.
Aggregated level
K(h)
K(a) K(b)
K(d)K(e)
K(i)
K(β) correlations
needs-K
K→C
C3
C1
C7
C6
C5
C10
C12
C11
C14
C17
C18
C9
C8
C4
C13
C16
C15
CN 1
CN 2
CN 3
CN
1:
Seg
men
tatio
nC
N2:
Fe
edba
ckC
N3:
S
peed
Clu
ster
s of
nee
ds(T
=2)
Concept-‐Knowledge
Design Theory re-‐
interpreta8on
(Aggregated level)
23
www.openinnovate.co.uk
www.openinnovate.co.uk
K(Papers)
K(Patents)
K(g)
K(c)
K(f)
C2
Concept Space Knowledge Space
K(N1, N2, N3) new
The visualization show Cs at two different stages. The smaller nodes represent individual needs in T=1 while the big nodes represent clustered groups of needs ready to
be matched with relevant K in T=2. The clusters “Speed”, “Feedback” and “Segmentation” are only examples of
underlying common problems for those needs.
Aggregated level
K(h)
K(a) K(b)
K(d)K(e)
K(i)
K(β) correlations
needs-K
K→C
C3
C1
C7
C6
C5
C10
C12
C11
C14
C17
C18
C9
C8
C4
C13
C16
C15
CN 1
CN 2
CN 3
CN
1:
Seg
men
tatio
nC
N2:
Fe
edba
ckC
N3:
S
peed
Clu
ster
s of
nee
ds(T
=2)
Integrated Theore(cal Framework
• C-‐K Engineering Design Theory
• TRIZ, Theory for Inven8ng Problem Solving
• Informa8on Management Technologies
• Data Mining and Aggrega8on
• Seman8c Analysis
C-‐K adapted model
24
Barriers for TT
Priority
Culture
25
Exis(ng tools for TT
26
Exis(ng tools for TT
27
Screencast: TerMine, Wikimindmap, Creax Func8on Database and Seman8c Representa8ons.
www.openinnovate.co.uk
Experiment
28
Experiment
3 Main technology needs brokers
29
www.openinnovate.co.uk
Experiment
3 randomly selected needs (RFPs) from different domains
30
www.openinnovate.co.uk
Experiment
31
Experiment
32
Tool Proposal
NEEDS:
• SMEs should be provided with appropriate support to enable them to access the knowledge they require from home and abroad. Government could map key global communi8es of prac8ce for the benefit of SMEs.
• Small firms should be helped to iden(fy and use interna(onal agents.
• A register of global university exper(se should be compiled.
• Firms need advice on effec8ve network management.
• Government must con8nue to fund exis(ng network support.
Based on NESTA report “Sourcing knowledge for innovation” May 2010
33
Dashboard: Matches by need
Tool Proposal
34
www.openinnovate.co.uk
Dashboard: Matches by K
Tool Proposal
35
www.openinnovate.co.uk
Dashboard: High probability matches
Tool Proposal
36
www.openinnovate.co.uk
Conclusions
Findings Conclusions
From literature
and interviews
Proposed models
Research triggers
Objec(ves
Areas of study
Final remarks
Context
37
www.openinnovate.co.uk
Conclusions
• Exploit the “long tail” of technology needs and research.
• Using the pool of explicit scien(fic knowledge already available.
• Allows researchers to focus on what they are best at.
• Solu8ons from distant domains.
• Problems can be solved by an accessible expert in the same region or somebody associated in a close social network.
• SMEs have a good chance of enjoying the benefits of open innova(on networks if provided with the correct tools. 38
Poten(al Beneficiaries
39
www.openinnovate.co.uk