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dc.contributor.advisorStephen Graves and David Simchi-Levi.en_US
dc.contributor.authorWu, Yaluen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2015-10-30T18:56:37Z
dc.date.available2015-10-30T18:56:37Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/99572
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 69).en_US
dc.description.abstractDemand Planning forecasts at Nike, Inc. are used by many groups: Supply Planning/Materials Planning, Sourcing, Categories/Merchandising, Finance, S&OP, and Sales. These groups take forecasts as an input to make key decisions. Forecasts, by nature, will be inaccurate. There are two big unknowns to answer as Nike considers how to improve forecast accuracy: 1) how accurate can or should forecasts become (target setting) and 2) what are the causes and impacts of inaccuracy. However, the first step to addressing these questions is to understand and measure forecast accuracy metrics in a consistent way across Nike's various Demand Planning groups. This project investigates the following through the design of a Tableau dashboard * which metrics should be reviewed (accuracy, bias, volatility, etc.) * how they should be computed (what to compare) * at what level of aggregation for which groups * at what level of detail for which groups (category, classification, etc.) * over how many seasons * with which filters In addition to aligning on forecast accuracy metrics, the project also focuses on the dashboard design (determining the most appropriate structure/views, how information is laid out or presented, and the use of labels and color) and on setting the long-term vision for viewing and using forecast accuracy metrics through researching and outlining the process for root cause analysis and target setting.en_US
dc.description.statementofresponsibilityby Yalu Wu.en_US
dc.format.extent69 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.subjectSloan School of Management.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleA Framework for Analyzing Forecast Accuracy Metricsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.contributor.departmentSloan School of Management.en_US
dc.contributor.departmentLeaders for Global Operations Program.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.contributor.departmentSloan School of Management
dc.identifier.oclc924815866en_US


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