Knowledge Integration in Fast Changing Environments

Transcription

1 University of LinkÖping, September Knowledge Integration in Fast Changing Environments Stefano Brusoni

2 Table of Contents Background and motivation Research gap From modularity to modularization The role of knowledge integration capabilities Empirical methodology Data Indicators: structural cohesion Early results Conclusions

3 Background, I Globalisation of product markets Increased segmentation of product markets Shortening life cycles Technical change and knowledge specialisation 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 One-to-one mapping between functions and components Standardised interfaces

4 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

5 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.

6 Key issue How do new problem frames come into being? Technological frames the case of radical process innovation robotization of tire production

7 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) MIRS MIRS, a case of radical innovation (Brusoni and Prencipe, 2006)

8 Traditional Manufacturing Process Akron workers, about 1910.

9 Innovative manufacturing process MIRS, Milano Bicocca, about 2000

10 From deposition of layers in flat to deposition of small tapes on a rigid drum

11 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 structural cohesion

12 How do new problem frames come into being? Building blocks 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.

13 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)

14 Networks are structurally cohesive if they remain connected even when nodes are removed Source: Moody and White (2003), fig.1 p.108

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16 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 (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!

17 600 Pirelli vs. Michelin Number of patents

18 Michelin vs. Pirelli connectivity 0,6 0,5 0,4 0,3 0,2 0, Pirelli Michelin

19 Michelin Giant Component Formation FIRST GIANT COMPONENT SECOND GIANT COMPONENT

20 Pirelli Giant Component Formation FIRST GIANT COMPONENT SECOND GIANT COMPONENT

21 Michelin C3M inventors and GC

22 Pirelli MIRS inventors and GC

23 Michelin: vertical integration

24 Pirelli: vertical integration

25 Pirelli vs. Michelin: horizontal integration PIRELLI Layer 7: Stronger Overlap Among Groups MICHELIN Layer 4: Weaker Overlap Among Groups

26 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 2. 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 3. An horizontal structure which also exhibit some extent of integration, i.e. many overlaps among cohesive blocks at the same hierarchical layer.

27 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.

28 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)

29 Thanks!