Improving supply chain planning with advanced analytics : analyzing lead time as a case study

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.

Bibliographic Details
Main Author: Yau, Darryl (Chun Him)
Other Authors: Christopher Caplice.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2018
Subjects:
Online Access:http://hdl.handle.net/1721.1/117925
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author Yau, Darryl (Chun Him)
author2 Christopher Caplice.
author_facet Christopher Caplice.
Yau, Darryl (Chun Him)
author_sort Yau, Darryl (Chun Him)
collection MIT
description Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.
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spelling mit-1721.1/1179252019-04-12T08:04:42Z Improving supply chain planning with advanced analytics : analyzing lead time as a case study Yau, Darryl (Chun Him) Christopher Caplice. Massachusetts Institute of Technology. Supply Chain Management Program. Massachusetts Institute of Technology. Supply Chain Management Program. Supply Chain Management Program. 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). 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. by Darryl Yau. M. Eng. in Supply Chain Management 2018-09-17T15:50:14Z 2018-09-17T15:50:14Z 2018 2018 Thesis http://hdl.handle.net/1721.1/117925 1051223499 eng 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. http://dspace.mit.edu/handle/1721.1/7582 83 pages application/pdf Massachusetts Institute of Technology
spellingShingle Supply Chain Management Program.
Yau, Darryl (Chun Him)
Improving supply chain planning with advanced analytics : analyzing lead time as a case study
title Improving supply chain planning with advanced analytics : analyzing lead time as a case study
title_full Improving supply chain planning with advanced analytics : analyzing lead time as a case study
title_fullStr Improving supply chain planning with advanced analytics : analyzing lead time as a case study
title_full_unstemmed Improving supply chain planning with advanced analytics : analyzing lead time as a case study
title_short Improving supply chain planning with advanced analytics : analyzing lead time as a case study
title_sort improving supply chain planning with advanced analytics analyzing lead time as a case study
topic Supply Chain Management Program.
url http://hdl.handle.net/1721.1/117925
work_keys_str_mv AT yaudarrylchunhim improvingsupplychainplanningwithadvancedanalyticsanalyzingleadtimeasacasestudy