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dc.contributor.advisorDavid Simchi-Levi and Stephen Graves.en_US
dc.contributor.authorBalent, Zachariah (Zachariah Francis)en_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2018-11-28T15:43:30Z
dc.date.available2018-11-28T15:43:30Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119330
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.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, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 55).en_US
dc.description.abstractAs Dell seeks to continually improve customer experience, the company is identifying new and innovative ways to improve on-time delivery. Inventory shortages that occur prior to production account for approximately 35% of missed delivery dates. When these part shortages occur, demand planners must apply "extended" lead times to these parts to ensure that Dell's customers have the correct expectation for when their order will be delivered. This project focuses on part shortage problems and how to generate accurate lead times for customers commitments. Previous research on the topic on lead time setting has focused predominately on buffering and measuring uncertainty in supply chains, which detail the benefits of having appropriate levels of safety stock and flexibility. However, prior research does not adequately describe methods for adjusting product lead times under uncertain supply conditions. The project develops a deterministic model for identifying when parts in Dell's supply chain require lead time adjustments due to supply shortages and then for setting the new lead times. Additionally, this project includes a statistical analysis of previous extended lead time events. After a five-week testing period, the deterministic model was quite accurate in identifying what parts require extended lead times. This offers a 3% improvement in identifying when extended lead times are needed as it decreases human error in missed and late lead time extensions. Predominant sources of error resulted from backlog management issues, part deviations in production, and miscellaneous data errors. The statistical analysis yields two insights into part recovery in Dell's supply chain: (1) larger volume shortages take shorter time to recover than small volume shortages, and (2) approximately 80% of all part shortages recover within 10 days. This research offers valuable insight into the problems associated with lead times in Dell's supply chain and recommends ways to best mitigate these errors. As Dell develops more robust and comprehensive databases on its inventory, future research can identify methods to accurately and automatically update lead times in real-time.en_US
dc.description.statementofresponsibilityby Zachariah Balent.en_US
dc.format.extent55 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.subjectCivil and Environmental Engineering.en_US
dc.subjectSloan School of Management.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleImproving lead time setting and on-time delivery commitments under uncertain supply conditionsen_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 Civil and Environmental Engineering
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1065522779en_US


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