2026-09-19T18:30:49Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/289262022-01-13T07:54:46Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Stephen C. Graves.
Johnson, Jeffrey D. (Jeffrey David), 1979-
Massachusetts Institute of Technology. Engineering Systems Division.
Massachusetts Institute of Technology. Engineering Systems Division
2005-09-27T19:06:08Z
2005-09-27T19:06:08Z
2005
2005
http://hdl.handle.net/1721.1/28926
60504176
Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.
Includes bibliographical references (leaves 88-92).
Within the semiconductor industry, the variability in both supply and demand is quite high; this uncertainty makes supply chain planning very difficult. We analyze the current tools and processes at a large semiconductor manufacturing company and then propose a framework for improvement based on hierarchical production planning. We present an appropriate decomposition for this specific planning problem and illustrate some limitations of traditional inventory models. New safety stock equations are developed for this planning problem based on a simple analysis using the basic ideas from probability theory. We also devise a new method to determine lead times that more accurately captures the actual lead time seen in the supply chain. Finally, an algorithm is developed to determine appropriate inventory levels and production allocation. These ideas, when used together, provide a powerful framework to properly manage supply chains in highly stochastic environments.
by Jeffrey D. Johnson.
S.M.
99 leaves
5031557 bytes
5042848 bytes
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en_US
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
Managing variability in the semiconductor supply chain
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