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dc.contributor.advisorBerthold Horn and Karen Zheng.en_US
dc.contributor.authorBen Nun, Shaien_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2017-10-18T14:42:42Z
dc.date.available2017-10-18T14:42:42Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111863
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, in conjunction with the Leaders for Global Operations Program at MIT, 2017.en_US
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 79).en_US
dc.description.abstractAmazon's leadership has set a goal to achieve a highly automated warehouse by 2020. Automation involves two challenges, physical movement and decision-making. Decision making often increase variability, defects and consumes time. The goal of this paper is to offer a structured framework to design a machine-learning based solution to automate decisions. It will leverage recorded decisions made by Amazon's employees every minute. Using the stow problem as an example, the paper will showcase how captured decision data can be a source of knowledge about processes and products. The stow case is a perfect example for non-trivial continuous decision making process. The workers' decisions are recorded but to this day Amazon does not leverage them for learning purposes. Our framework will offer a few outcomes: 1) High accuracy decision model, improving forecasting abilities from 59% to 95% 2) Self healing mechanism and learning system 3) Coaching and training tool. Using only the first outcome, cost savings are estimated to be over $25M annually across the US network. This paper will also discuss the psychological implication for decision automation while keeping manual work as part of the process.en_US
dc.description.statementofresponsibilityby Shai Ben Nun.en_US
dc.format.extent79 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.subjectSloan School of Management.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleA framework For decision automation in operationsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentLeaders for Global Operations Program.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1005229453en_US


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