{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/117925"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/117925","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Improving supply chain planning with advanced analytics : analyzing lead time as a case study","abstract":"Over the years, supply chain management has continued to change and evolve to become a major component in competitive strategy to enhance organizational productivity and profitability. While considerable research has been done in formulating accurate and robust demand forecasts, many areas for improvement remain in supply chain planning. In particular, many planning parameters (e.g., lead time, waste, yield, run rate, capacity, etc.), which are vital inputs into the planning process, are often not given the consideration they deserve. Oftentimes, the planned values of these parameters were not scientifically derived in the first place, or their actual values may have changed since the planned values' original inception and now differ significantly from its planned value. This research examined one type of planning parameter in particular - lead time, and showed there is room for improvement in how lead time is managed and considered within the current planning process. The research showed that using predictive analytics to predict lead time (predictive lead time) can reduce the deviation between the planned and actual values in the lead time parameter..Moreover, the analyses showed that using predictive lead time can reduce the safety stock cost, the manual labor required in exception management (re-planning), and the manual labor in purchase order management.","abstract_html":"Over the years, supply chain management has continued to change and evolve to become a major component in competitive strategy to enhance organizational productivity and profitability. While considerable research has been done in formulating accurate and robust demand forecasts, many areas for improvement remain in supply chain planning. In particular, many planning parameters (e.g., lead time, waste, yield, run rate, capacity, etc.), which are vital inputs into the planning process, are often not given the consideration they deserve. Oftentimes, the planned values of these parameters were not scientifically derived in the first place, or their actual values may have changed since the planned values' original inception and now differ significantly from its planned value. This research examined one type of planning parameter in particular - lead time, and showed there is room for improvement in how lead time is managed and considered within the current planning process. The research showed that using predictive analytics to predict lead time (predictive lead time) can reduce the deviation between the planned and actual values in the lead time parameter..Moreover, the analyses showed that using predictive lead time can reduce the safety stock cost, the manual labor required in exception management (re-planning), and the manual labor in purchase order management.","abstract_has_math":false,"creators":["Yau, Darryl (Chun Him)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Supply Chain Management Program.","school":null,"contributors":[],"advisors":["Christopher Caplice."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:21:05Z","subjects":["Supply Chain Management Program."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/117925"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 72-74)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Over the years, supply chain management has continued to change and evolve to become a major component in competitive strategy to enhance organizational productivity and profitability. While considerable research has been done in formulating accurate and robust demand forecasts, many areas for improvement remain in supply chain planning. In particular, many planning parameters (e.g., lead time, waste, yield, run rate, capacity, etc.), which are vital inputs into the planning process, are often not given the consideration they deserve. Oftentimes, the planned values of these parameters were not scientifically derived in the first place, or their actual values may have changed since the planned values' original inception and now differ significantly from its planned value. This research examined one type of planning parameter in particular - lead time, and showed there is room for improvement in how lead time is managed and considered within the current planning process. The research showed that using predictive analytics to predict lead time (predictive lead time) can reduce the deviation between the planned and actual values in the lead time parameter..Moreover, the analyses showed that using predictive lead time can reduce the safety stock cost, the manual labor required in exception management (re-planning), and the manual labor in purchase order management."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng. in Supply Chain Management"]},{"key":"dc:title","label":"Title","values":["Improving supply chain planning with advanced analytics : analyzing lead time as a case study"]}]}],"canonical_facts":{"dc:contributor.advisor":["Christopher Caplice."],"dc:contributor.department":["Massachusetts Institute of Technology. Supply Chain Management Program."],"dc:contributor.other":["Massachusetts Institute of Technology. Supply Chain Management Program."],"dc:creator":["Yau, Darryl (Chun Him)"],"dc:date.accessioned":["2018-09-17T15:50:14Z"],"dc:date.available":["2018-09-17T15:50:14Z"],"dc:date.issued":["2018"],"dc:description":["Thesis: M. 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Oftentimes, the planned values of these parameters were not scientifically derived in the first place, or their actual values may have changed since the planned values' original inception and now differ significantly from its planned value. This research examined one type of planning parameter in particular - lead time, and showed there is room for improvement in how lead time is managed and considered within the current planning process. The research showed that using predictive analytics to predict lead time (predictive lead time) can reduce the deviation between the planned and actual values in the lead time parameter..Moreover, the analyses showed that using predictive lead time can reduce the safety stock cost, the manual labor required in exception management (re-planning), and the manual labor in purchase order management."],"dc:description.degree":["M. Eng. in Supply Chain Management"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/117925"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT 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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Supply Chain Management Program."],"dc:title":["Improving supply chain planning with advanced analytics : analyzing lead time as a case study"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:05Z"}