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Productivity improvement in a network of learning factories: a learning curve analysis

Author: Lapre, M. A ; Van Wassenhove, Luk N.INSEAD Area: Technology and Operations Management Series: Working Paper ; 98/10/CIMSO2 Publisher: Fontainebleau : INSEAD Center for Integrated Manufacturing and Service Operations (CIMSO) 1998.Language: EnglishDescription: 32 p.Type of document: INSEAD Working Paper Online Access: Click here Abstract: In 1984, Dutton and Thomas introduced a categorization of factors for accelerating the learning curve. This Paper is the first -to our knowledge- that explores all these factors in a total factor productivity learning curve analysis. It does so in a network of production lines in three factories deliberately set up to acquire and share knowledge. The results indicate that even in such a network, transfer of knowledge is non obvious. First we find limited evidence that explicit knowledge is easier to transfer than tacit knowledge. Second, productivity improvement hinges on stability in process conditions particularly stable capacity utilization, continuity in raw material suppliers and non-increasing reject rates. Third successful learning by experimentation in dynamic production environments requires control over resources and process setting across production stages Next title: Creating and transferring knowledge for productivity improvement in factories (RV of 98/10/CIMSO) - Lapré, Michael A.;Van Wassenhove, Luk N. - 2000 - INSEAD Working Paper
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In 1984, Dutton and Thomas introduced a categorization of factors for accelerating the learning curve. This Paper is the first -to our knowledge- that explores all these factors in a total factor productivity learning curve analysis. It does so in a network of production lines in three factories deliberately set up to acquire and share knowledge. The results indicate that even in such a network, transfer of knowledge is non obvious. First we find limited evidence that explicit knowledge is easier to transfer than tacit knowledge. Second, productivity improvement hinges on stability in process conditions particularly stable capacity utilization, continuity in raw material suppliers and non-increasing reject rates. Third successful learning by experimentation in dynamic production environments requires control over resources and process setting across production stages

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