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dc.contributor.advisorBruce Cameron and Stephen Graves.en_US
dc.contributor.authorSchneider, Christian, S.M. Massachusetts Institute of Technologyen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2016-09-27T15:14:45Z
dc.date.available2016-09-27T15:14:45Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/104390
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionThesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. 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 57).en_US
dc.description.abstractDell is accelerating investments to simplify and improve one of the core competencies it was founded on, customer experience. One goal within this initiative is to increase the percentage of orders that are ontime to a committed Estimated Delivery Date (EDD). EDDs for products vary greatly with the complexity of the customer purchase orders. In order to remain competitive, Dell has set an aggressive goal to provide better on-time delivery performance. Dell needs to quote more accurate lead time commitments to customers and increase the stability of high variability steps in the end-to-end order supply chain. The EDD lead time, from customer order to proof of delivery, consists of a payment (processing) phase, manufacturing (build, inbound logistics, warehouse) phase, and a logistics (delivery) phase. Each of these segments are managed by different organizations within Dell. Understanding what the end-to-end future state looks like will allow functional teams to set improvement targets to achieve Dell's on-time goal. This study has three main objectives: (1) determine the key drivers of variability in the current state process, (2) identify opportunities for more detailed EDD range generation, and (3) quantify targets for individual process steps to drive towards the target future state. Three high volume Build to Order (BTO) regional product lines were chosen as cases to analyze. BTO product lines, compared to Build-to-Stock (BTS), inherently have a more variable supply chain for the processes examined. To meet the main objectives, this thesis examines the hypothesis that a simulation model based on historic order data can be used to quantify existing cycle time performance in the supply chain and deliver targets to achieve Dell's on-time performance target. Key drivers of cycle time variation were identified through process mapping and design of experiment statistical analysis. Results from the modeling and sensitivity analysis produced actionable recommendations for each of the three objectives and lead to a pilot project to improve EDD commitments for an existing desktop product line. Direct to customer shipping, inbound logistics method, and day of week were identified as attributes that were significant drivers of variability and were underutilized in the EDD commitment process. This provided an opportunity for smarter lead time setting. A pilot project for a desktop line adjusted lead times to incorporate direct to customer shipping and day of week, resulting in a 30-40% on-time performance improvement. Finally, modeling results quantified cycle time distribution targets for each process step to achieve Dell's future state goal for on-time delivery. Dell is building on this project by analyzing more regional product lines and exploring opportunities to incorporate machine learning.en_US
dc.description.statementofresponsibilityby Christian Schneider.en_US
dc.format.extent57 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.subjectSloan School of Management.en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectEngineering Systems Division.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleModeling end-to-end order cycle-time variability to improve on-time delivery commitments and drive future state metricsen_US
dc.typeThesisen_US
dc.description.degreeM.B.A.en_US
dc.description.degreeS.M. in Engineering Systemsen_US
dc.contributor.departmentSloan School of Management.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.contributor.departmentLeaders for Global Operations Program.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.contributor.departmentSloan School of Management
dc.identifier.oclc958267757en_US


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