2026-09-21T03:32:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/795052022-01-13T07:54:46Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Joel Clark.
Rand-Nash, Thomas
Massachusetts Institute of Technology. Engineering Systems Division.
Massachusetts Institute of Technology. Engineering Systems Division
2013-07-10T14:50:15Z
2013-07-10T14:50:15Z
2012
2012
http://hdl.handle.net/1721.1/79505
849741757
Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 173-179).
When making technology choice decisions, firms must consider technology costs over time. In many industries, technology costs have been shown to decrease over time due to (a) improvements in production efficiency and the accumulation of worker experience accompanying production, known as "learning-by-doing," and (b) firm investments in research and development, worker training and other process improvement activities, known as "learning-by-investing." Rapid technological progress may mean that new technologies become available while existing technologies still exhibit learning-related cost reductions. In these cases, switching to a new technology means giving up these ongoing benefits while also incurring new technology introduction costs. Additionally, In some industries, high switching costs, regulatory compliance and/or the risks associated with new technologies may require firms to continue allocating production volume and investments to an existing technology whether or not a new technology is introduced. In these cases, firms must decide how to allocate finite production volume and investment resources between technologies. Learning is driven by resource allocation. Therefore, sharing finite resources among multiple learning technologies may reduce the learning-related benefits associated with each. This may lead firms to underestimate technology costs, leading to sub-optimal technology choice and resource allocation decisions. A methodology is presented which couples technology costs over time via capacity and investment resource allocation to characterize the impacts of (1) learning in an incumbent technology, and (2) resource allocation constraints, on technology choice and resource allocation decisions. Case studies in the semiconductor and automotive industries are examined using this method in combination with process based cost modeling. We find that (1) when the existing technology is still learning, diverting resources to a new technology results in an opportunity cost in both technologies which diminishes the benefits of switching technologies; (2) this effect can persist over a wide range of learning rates and technology costs; (3) capacity allocation constraints can significantly change the conditions under which the firm should choose a new technology, and (4) cumulative production volume and investment based learning differentially impact technology costs, leading to different cost-minimizing resource allocation decisions.
by Thomas Rand-Nash.
Ph.D.
179 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
Modeling learning when alternative technologies are learning & resource constrained : cases In semiconductor & advanced automotive manufacturing
Thesis
application/pdf
Publication
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<Abstract>When making technology choice decisions, firms must consider technology costs over time. In many industries, technology costs have been shown to decrease over time due to (a) improvements in production efficiency and the accumulation of worker experience accompanying production, known as "learning-by-doing," and (b) firm investments in research and development, worker training and other process improvement activities, known as "learning-by-investing." Rapid technological progress may mean that new technologies become available while existing technologies still exhibit learning-related cost reductions. In these cases, switching to a new technology means giving up these ongoing benefits while also incurring new technology introduction costs. Additionally, In some industries, high switching costs, regulatory compliance and/or the risks associated with new technologies may require firms to continue allocating production volume and investments to an existing technology whether or not a new technology is introduced. In these cases, firms must decide how to allocate finite production volume and investment resources between technologies. Learning is driven by resource allocation. Therefore, sharing finite resources among multiple learning technologies may reduce the learning-related benefits associated with each. This may lead firms to underestimate technology costs, leading to sub-optimal technology choice and resource allocation decisions. A methodology is presented which couples technology costs over time via capacity and investment resource allocation to characterize the impacts of (1) learning in an incumbent technology, and (2) resource allocation constraints, on technology choice and resource allocation decisions. Case studies in the semiconductor and automotive industries are examined using this method in combination with process based cost modeling. We find that (1) when the existing technology is still learning, diverting resources to a new technology results in an opportunity cost in both technologies which diminishes the benefits of switching technologies; (2) this effect can persist over a wide range of learning rates and technology costs; (3) capacity allocation constraints can significantly change the conditions under which the firm should choose a new technology, and (4) cumulative production volume and investment based learning differentially impact technology costs, leading to different cost-minimizing resource allocation decisions.</Abstract>
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