Knowledge Partitioning in the Inter-firm Dividsion of Labor: the case of Automotive product Development
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Takeishi2.pdf
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9768f7a3ec86c62960b612e0ac4d8fce
Author(s)
Takeishi, Akira
Date Issued
July 23, 1999
Abstract
This paper demonstrates the importance of knowledge for effective
management of outsourcing. Drawing on an empirical study on automakers?
management of supplier involvement in product development in Japan, this paper shows
that the level of own knowledge is critical for automakers to gain better outcome from
engineering outsourcing. While the actual tasks of designing and manufacturing
components could be outsourced, automakers should retain the relevant knowledge to
obtain better quality of component design. Knowledge partitioning should be
distinguished from task partitioning.
Furthermore, the results indicate that effective pattern of knowledge
partitioning differs by the nature of component development project in terms of
technological newness. For regular projects, it is more important for automakers to have
a higher level of architectural knowledge (how to coordinate various components for a
vehicle) than of component-specific knowledge, which is supposed to be provided by
suppliers. However, when the project involves new technology for the supplier, it is
important for the automaker to have a higher level of component-specific knowledge to
solve unexplored engineering problems together with the supplier. In innovative
projects effective knowledge partitioning seems to demand some overlaps between an
automaker and a supplier, rather than efficient and clear-cut boundaries.
This paper further reveals that some automakers manage knowledge better than
others by combining various organizational mechanisms, including career development
policies, extensive documentation of technological information, internal training
programs, and incentive schemes. Difficulty of implementing those mechanisms in a
consistent and complementary manner seems to explain why there is a significant
variance among automakers in knowledge level.
Subjects
empirical study
task partitioning
outsourcing
knowledge partitioning
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