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dc.contributor.advisorKutanoglu, Erhanen
dc.creatorMcFarland, Ian Christopheren
dc.date.accessioned2012-08-16T18:27:59Zen
dc.date.available2012-08-16T18:27:59Zen
dc.date.issued2012-05en
dc.date.submittedMay 2012en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2012-05-5514en
dc.descriptiontexten
dc.description.abstractDemand forecasting is an important step of a company’s supply chain management process, allowing companies to project their needs for different components that are used in the final product. This is even more important in emerging industries with job order (or project-based) products where historical demands do not exist and components may not be readily available or may involve a long lead time. Developing a demand forecasting model which accurately projects the needs of components for a company can decrease costs while decreasing overall lead times of final products. This demand forecast model takes into account projected component needs along with the likelihood of successfully winning a project bid. The model is extended to four different demand forecasting formulas incorporating different use of the winning probabilities. Historical results are then used to compare the methods and their advantages and disadvantages are discussed.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectDemand forecastingen
dc.subjectSupply chainen
dc.titleDemand forecasting for job order products in highly technological and emerging industriesen
dc.date.updated2012-08-16T18:28:06Zen
dc.identifier.slug2152/ETD-UT-2012-05-5514en
dc.contributor.committeeMemberGotcher, Alanen
dc.description.departmentOperations Research and Industrial Engineeringen
dc.type.genrethesisen
thesis.degree.departmentOperations Research and Industrial Engineeringen
thesis.degree.disciplineOperations Research and Industrial Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen


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