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dc.contributor.advisorDonald B. Rosenfield and Jérémie Gallien.en_US
dc.contributor.authorCheung, Christineen_US
dc.contributor.otherLeaders for Manufacturing Program.en_US
dc.date.accessioned2006-11-08T16:48:08Z
dc.date.available2006-11-08T16:48:08Z
dc.date.copyright2005en_US
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/34844
dc.descriptionThesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2005.en_US
dc.descriptionIncludes bibliographical references (p. 75-76).en_US
dc.description.abstractCompanies like Procter & Gamble that operate on a make-to-stock strategy use forecasts to drive their manufacturing, selling, and buying processes. Because forecasting future demand is not an exact science, inventory management models have been developed to accommodate these uncertainties. There has been a significant improvement in inventory management of base products, where forecasts are based on historical sales information. Because the bulk of forecasting methods depend on this use of historical data, little effort to date has been focused on inventory management of a new product. The use of traditional time- series forecasting methods is not realistic and companies typically resort to using judgmental or analogous (e.g. curve-fitting) means, which are less applicable in making short-range production and inventory decisions. The lack of a new product forecasting method poses a significant problem in the cosmetic industry, which faces an increasing dependence on the introduction of new products for sales growth. Inventory and supply chain management is made even more difficult by the short product-life cycle, long lead times, and complexity and number of SKUs. As the industry trends toward increasing the pace of new product launches, forecast accuracy of a new product in its initial launch stages becomes more critical to manage the supply network's inventory and capacity. This document outlines a supply strategy for new product introductions that improves information management in the forecasting process to optimize supply and inventory planning.en_US
dc.description.abstract(cont.) This method is designed to improve product pipeline forecasts as well as basic replenishment forecasts in the first few months of a product's launch. The model was tested and validated by historical simulations on a cosmetic product line. Results showed significant inventory reductions compared to current inventory management policies.en_US
dc.description.statementofresponsibilityby Christine Cheung.en_US
dc.format.extent76 p.en_US
dc.format.extent3928460 bytes
dc.format.extent3931566 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
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/1721.1/7582
dc.subjectSloan School of Management.en_US
dc.subjectChemical Engineering.en_US
dc.subjectLeaders for Manufacturing Program.en_US
dc.titleA short-range forecasting and inventory strategy for new product launchesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentLeaders for Manufacturing Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineering
dc.contributor.departmentSloan School of Management
dc.identifier.oclc63199379en_US


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