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A capital equipment capacity planning methodology for aerospace parts manufacturing in a high-mix, low volume environment

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dc.contributor.advisor David Hardt and Roy Welsch. en_US
dc.contributor.author Reveley, Matthew A. (Matthew Aaron) en_US
dc.contributor.other Leaders for Global Operations Program. en_US
dc.date.accessioned 2012-09-27T15:30:37Z
dc.date.available 2012-09-27T15:30:37Z
dc.date.copyright 2012 en_US
dc.date.issued 2012 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/73414
dc.description Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2012. en_US
dc.description Page 99 blank. Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 97-98). en_US
dc.description.abstract A static capacity planning model was developed and tested following a four-phased framework. This model was developed for the purposes of capital planning for capacity requirements at a large aerospace parts manufacturing plant. Implications for capacity planning of the nature of the aerospace industry, as well as the company and plant being studied are discussed, as well as the current state of capacity planning. In phase I of model development, an appropriate modeling solution is selected. In phase II, information is collected from the user base as to the desired user experience and functionality of the model, as well as the parameters that should be considered in it. Phase III involves assessment of the parameters' impact on capacity, and identification of appropriate data sources to feed the model. Additionally, phase III recommends changes to current data structures in order to optimize the balance of model accuracy with minimal incremental resource allocation. In phase IV, the mathematical model is explained, and the user interface is developed. With a working model, the results are validated with the shop floor, identifying gaps in data sources previously unobservable. Following model development and validation, the model is applied to a subset of the shop, and used to develop recommendations for addressing predicted future capacity constraints. Application of the model reveals a blind spot in current heuristics-based planning, where high development loads can lead to immediate capacity constraints, but effects of the experience curve can actually cause this constraint to disappear on its own, without the need for excess equipment purchases. Finally, extensions of the research and lessons learned are discussed, suggesting future project work within the plant studied, as well as elsewhere in the company and in other companies or plants. en_US
dc.description.statementofresponsibility by Matthew A. Reveley. en_US
dc.format.extent 99 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Sloan School of Management. en_US
dc.subject Mechanical Engineering. en_US
dc.subject Leaders for Global Operations Program. en_US
dc.title A capital equipment capacity planning methodology for aerospace parts manufacturing in a high-mix, low volume environment en_US
dc.title.alternative capacity planning methodology for aerospace parts manufacturing in a high-mix, low volume environment en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.description.degree M.B.A. en_US
dc.contributor.department Sloan School of Management. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Mechanical Engineering. en_US
dc.contributor.department Leaders for Global Operations Program. en_US
dc.identifier.oclc 810337355 en_US


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