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dc.contributor.advisorStanley Gershwin and Stephen Graves.en_US
dc.contributor.authorLi-Carrillo, Carla (Li-Carrillo Paredes)en_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2017-10-18T15:10:50Z
dc.date.available2017-10-18T15:10:50Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111938
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 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.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 72).en_US
dc.description.abstractAmazon inbound operations are staffed following a 'staffing-to-charge' model in which labor is planned to match the incoming volume capacity required by the weekly Sales Operations Planning (S&OP) forecast. Staffing-to-charge is a lean model of staffing that attempts to maximize labor utilization by minimizing the possibility of a labor surplus or deficit. However, due to inaccuracies in the S&OP freight forecast, poor visibility into incoming inventory, and last minute staffing changes, it is often the case that labor capacity is not adequately aligned with the actual unit receipts. This leads to additional labor costs and network inefficiencies. This project explored the current staffing policies and current system constraints such as forecast accuracy, backlog management, and hiring schedules to understand the scope of the problem. From these findings, an alternate method for staffing, known as 'Level loading,' was proposed. Level loading consists of staffing to a known and consistent headcount every day of the week with the intent to reduce staffing costs and labor capacity variability. Level loading was found to improve the efficiency of inbound operations, leading to considerable costs savings for the distribution center. The project also created an optimization model that allows Fulfillment Center managers to plan the transition from their current shifts to level loading; Amazon's Production Planning Team will implement this model by mid-2017. To fully achieve the benefits from level loading, the system requires a change in the planning of incoming freight. In particular, the incoming freight should be scheduled and planned according to a known labor capacity, as set by the level loading policy. This change to freight planning is currently being investigated. The study found that delayed restocking of the network is a costly inefficiency, similar in magnitude to the cost from excess labor capacity. To mitigate this, a labor plan that allows for greater capacity is necessary. The cost savings of more effective inbound operations offsets the additional labor costs of such a plan. The findings of this study are based on an Amazon warehouse, but a staffing model with greater labor capacity can be applied to inbound operations at any distribution center.en_US
dc.description.statementofresponsibilityby Carla Li-Carrillo.en_US
dc.format.extent72 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.subjectMechanical Engineering.en_US
dc.subjectSloan School of Management.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleOptimal staffing recommendation for inbound 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 at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1005922900en_US


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