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dc.contributor.advisorJang, Wooseungeng
dc.contributor.authorRoman, Mattheweng
dc.date.issued2008eng
dc.date.submitted2008 Falleng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on October 5, 2009).eng
dc.descriptionThesis advisor: Dr. Wooseung Jang.eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionM.S. University of Missouri--Columbia 2008.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Industrial and manufacturing systems engineering.eng
dc.description.abstractMaintaining appropriate inventory levels is essential when attempting to maximize potential revenue and customer satisfaction. Within the utilities industry the significance of customer satisfaction is of utmost importance and the ability to predict when and where certain materials will be needed is highly valued. This research was motivated by these requirements and was focused on creating a customized forecasting model which could address the specific needs and demand patterns identified within Ameren's transformer usage. Historical transformer usage was attributed to three primary causes: new construction, storm and emergency, and general maintenance. Each of these displayed a distinctive demand pattern, thus a specific forecast was made for each disaggregate segment. Creating an individual forecasting model for each type of demand provided the ability to address the uniqueness within each demand pattern. More specifically, this approach allowed for the input of a forward-looking trend, generated from external factors, during the new construction forecast, the use of a model which followed historical trends within the general maintenance data, and a long-term averaging model which limited outliers found in the storm and emergency demand pattern. These disaggregate forecasts were then added together to create a final aggregate level forecast for the item or group of items being investigated. This model showed up to a 20% improvement of accuracy over more traditional methods when compared using median absolute percent error.eng
dc.identifier.merlinb71546248eng
dc.identifier.oclc449219573eng
dc.identifier.urihttps://hdl.handle.net/10355/5738
dc.identifier.urihttps://doi.org/10.32469/10355/5738eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2008 Freely available theses (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2008 Theseseng
dc.subject.lcshAmeren Corporationeng
dc.subject.lcshElectric transformerseng
dc.subject.lcshPublic utilities -- Rateseng
dc.subject.lcshConsumer satisfactioneng
dc.titleDisaggregate forecasting models: application to Ameren UE's transformer usageeng
dc.typeThesiseng
thesis.degree.disciplineIndustrial and manufacturing systems engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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