University of LinkÖping, September 17 2009
Knowledge Integration
in Fast Changing Environments
Stefano Brusoni
[email protected]
Table of Contents
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Background and motivation
†
Research gap
†
†
†
From modularity to modularization
The role of knowledge integration capabilities
Empirical methodology
†
†
Data
Indicators: structural cohesion
†
Early results
†
Conclusions
Background, I
†
†
†
†
†
¾
¾
Globalisation of product markets
Increased segmentation of product markets
Shortening life cycles
Technical change and knowledge specialisation
9 Increase in breadth and depth of relevant knowledge bases
From product to platforms
Modularity as a possible response to increasing complexity in firms’
learning environments
Definition
¾ One-to-one mapping between functions and components
¾ Standardised interfaces
Background, II
†
Starting point: opportunism vs. knowledge generation
† Zingales vs. Simon.
†
Key managerial problem is not about monitoring opportunistic individuals,
but rather the selection of the ‘problem’ which is most likely to generate
desirable and appropriable knowledge and capabilities.
† After the problem is chosen, the manager must organized
employees in order to solve it.
†
Problem here is identifying the criteria to match the right problem with the
right type of institutional set up.
†
Modularity provides ‘criteria’ to compare alternative problem frames
Motivation
†
Long term viability of ‘modular organizations’ depend upon the
ability of introducing new architectures and platforms
†
BUT: Lack of empirical analysis of processes of modularization, or remodularization, or de-modularization.
†
Modularity literature normally accepts the idea that architectural and
component-level knowledge are fully separable
†
Some firms specialize on developing architectures, others focus on
components ? ? ?
†
The Turing machine-view of industrial evolution: platform- and industryevolution are themselves ‘modular’ processes.
Key issue
How do new problem ‘frames’ come into being?
† ‘Technological’ frames – the case of radical
process innovation Æ robotization of tire
production
The empirical context: robotized tires production
‰
Very mature
‰ Major technological innovation in 1920s followed by shake out (Klepper
and Simons, 1996)
‰ Radial revolution in the late 1960s and acquisitions of US firms (Sull et
al. 1996)
‰ Very concentrated sector (top 10 firms have over 85% of sales in 2000)
‰
Very ‘odd’ recent history.
‰ Rapid increase in market segmentation
‰ Revamping of innovative efforts
‰
‰
‰
Microelectronics as the fastest growing patent class (Acha and Brusoni
(2005)
Rapid diffusion of radical process innovations (Brusoni and Sgalari, 2005) Î
MIRS
MIRS, a case of radical innovation (Brusoni and Prencipe, 2006)
Traditional Manufacturing Process
Akron workers, about 1910.
Innovative manufacturing process
MIRS, Milano Bicocca, about 2000
From deposition of layers in flat …
… to deposition of small tapes on a rigid drum
Knowledge integration capabilities
• Pirelli was not the technological pioneer
† It was actually far behind Michelin in 1997
† Yet, Pirelli won the technological race.
† How did it happen?
† Brusoni and Cassi (2009) Reinventing the Wheel
† Pirelli’s development effort relied on a network which was more
integrated than Michelin’s.
† To operationalize integration Æ structural cohesion
How do new ‘problem frames’ come into
being?
†
Building blocks
†
†
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Knowledge transfer literature. From which we take the notion that both
connections among people and the presence of key individuals which act as
‘reservoirs’ or repositories of knowledge embedded in organizations is key
(Argote and Ingram, 2000)
Complex adaptive systems literature. From which we take the notion
problems (i.e.s strategies) are made up of many interconnected elements
(Kauffman 1993, Rivkin 2000, Fleming and Sorenson 2001).
‘[T]he structure of the network of knowledge elements can guide the
process of recombinatory search for new inventions, and thus directly
affect the utility of such inventions’ (Yayavarm and Auja (2005, p. 4).
†
†
Collaborations among individuals involved in innovative activities within two
different firms
Successful knowledge integration requires the presence of focal individuals
that connect and integrate interdependent areas.
Structural cohesion
A group of individuals is cohesive if it is resilient to the removing of nodes.
• Moody & White, ASR 2003
Cohesion corresponds to Harary’s node connectivity: the minimum number of
actors (nodes) who, if removed, would disconnect the group (network).
Cohesive Blocking. The identification of cohesive groups can be applied
recursively in order to identify subgroups nested in the original group
This recursive procedure permits to:
• identify cohesive groups and their position related to the others
(vertical integration)
• observe overlap between groups at the same hierarchical level, since
individuals are allowed to be members of different groups
(horizontal integration)
Networks are structurally cohesive if they remain connected
even when nodes are removed
0
1
2
3
Source: Moody and White (2003), fig.1 p.108
Patent Data: Pirelli and Michelin
Michelin
Pirelli
Selection criterion
Patent
Inventors
Patent
Inventors
Technological Content
(3 digit IPC class)
700 (93.7)
510 (92.4)
415 (48.9)
326 (46.4)
Directly connected
27 (3.6)
17 (3.6)
186 (22.4)
128 (18.2)
Indirectly connected
-
-
93 (11.2)
68 (9.7)
Not connected
20 (2.7)
25 (4.5)
137 (16.5)
180 (25.6)
Total
747 (100)
552 (100)
831 (100)
702 (100)
Network of inventors capture the structure of knowledge!
Pirelli vs. Michelin – Number of patents
600
500
400
300
200
100
0
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Michelin vs. Pirelli – connectivity
0,6
0,5
0,4
0,3
0,2
0,1
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Pirelli
Michelin
Michelin – Giant Component Formation
60
50
40
30
FIRST GIANT COMPONENT
20
10
SECOND GIANT COMPONENT
0
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Pirelli – Giant Component Formation
70
60
50
40
FIRST GIANT COMPONENT
30
20
SECOND
GIANT
COMPONENT
10
0
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Michelin – C3M inventors and GC
25
20
15
10
5
0
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Pirelli – MIRS inventors and GC
25
20
15
10
5
0
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Michelin: vertical integration
Pirelli: vertical integration
Pirelli vs. Michelin: horizontal integration
PIRELLI Layer 7:
Stronger Overlap
Among Groups
MICHELIN Layer 4:
Weaker Overlap Among
Groups
Pirelli vs. Michelin
†
Pirelli succeeded in catching up with Michelin because it relied on a
more integrated knowledge base.
†
By ‘more integrated’, we mean three distinct characteristics of the
network:
1.
More connected, i.e. a larger number of node is reachable by the
others
A core-periphery structure ‘vertically’ centred around the MIRS
development team, i.e. an increasingly cohesive groups nested
inside each other, with the deepest group including MIRS’ project
leader
An horizontal structure which also exhibit some extent of integration,
i.e. many overlaps among cohesive blocks at the same hierarchical
layer.
2.
3.
Pirelli vs. Michelin
(an NK interpretation)
† Compared to Michelin, Pirelli bet on a higher K
† This higher K is reflected:
† In the new way of organizing tire design and production
† In the adaptation process (landscape is to some extent
endogenous)
† In the organizational structure (networks of inventors)
† In the strategy (new products, new processes, new niches, new
customers)
† At the same time, robotized processes decouples market
evolution from manufacturing processes
† Rugged landscape, but not coupled.
Conclusions
† Knowledge integration matters to introduce radical innovation
† Innovation based upon the recombination of existing
components (in both cases)
† Structural cohesion and SNA can be used to ‘visualize’ the
presence of knowledge integration capabilities.
† Pirelli developed an ‘integrated’ product, technology and
market strategy
† Michelin remained focused on a much narrower ‘frame’ for
longer (i.e. robotics as process innovation only)
† This is captured in our network data
† Seeking now: some performance indicators in terms of market
shares (at the niche level)
Thanks!
[email protected]