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dc.contributor.advisorEdgar Blanco.en_US
dc.contributor.authorSeminario, Carlos (Carlos Manuel Seminario Velarde)en_US
dc.contributor.authorMarks, Emmanuelen_US
dc.contributor.otherMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.date.accessioned2012-01-30T17:01:49Z
dc.date.available2012-01-30T17:01:49Z
dc.date.copyright2011en_US
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/68900
dc.descriptionThesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 139-141).en_US
dc.description.abstractCompanies that operate cold supply chains can greatly benefit from information availability and data generation. The abundance of information now available to cold chain operators and harvested from every echelon of the supply chain, ranging from the procurement process to the sales and customer service processes, provides an opportunity for logistics organizations to monitor and improve their operations. It is increasingly imperative to transform data into meaningful information that creates a competitive advantage for early adopters. This thesis attempts to determine how to make best use of and effectively interpret the information generated by trailer mounted temperature sensors and geospatial data collection devices during refrigerated transportation of packaged salads. The study covers only the transportation segment from the manufacturer's distribution center to the customer's (grocery retailer) distribution center. This thesis uses regression analysis in an effort to create a model that effectively uses realtime transportation information to identify the elements that can create a competitive advantage for cold chain operators. The main performance measurements subject to analysis in this thesis are reefer-unit fuel consumption and rejections of salad products at the customer's drop location. Regression yields a formula that can predict more than 70% reefer fuel consumption. However, with the independent variables available in the data at our disposal, it is not possible to build a model the effectively predicts product rejections. The findings of this thesis can help operators of transportation cold chains better manage fuel consumption by isolating and improving the independent variables we identified.en_US
dc.description.statementofresponsibilityby Carlos Seminario and Emmanuel Marks.en_US
dc.format.extent150 p.en_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.subjectEngineering Systems Division.en_US
dc.titleUsing real-time truck transportation information to predict customer rejections and refrigeration-system fuel efficiency in packaged salad distributionen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.in Logisticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.identifier.oclc773601000en_US


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