Program scheduling : decreasing flow by back-scheduling
Name
958269622-MIT.pdf
Description
Full printable version
Size
7.8 MB
Format
Adobe PDF
Checksum (MD5)
6c186e5fbf38503fd509e6be0052c9c6
Author(s)
Gould, Rebecca (Rebecca L.)
Advisor(s)
Annalisa Weigel and Roy E. Welsch.
Alternative Title
Decreasing flow by back-scheduling
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
The Boeing 737 program has announced that they will continue to increase their production rate from 42 airplanes per month to eventually 52 airplanes per month. In order for the field to accommodate the increased production rates, opportunities are being considered for decreasing airplane production flow and the increasing capacity in the field. This thesis focuses on the opportunity for decreasing schedule flow through improvements in the scheduling process. Currently the field constraints do not heavily influence what order the planes roll out of the factory. This causes the field to deal with constraint variation by addressing the airplane schedule flow in order to make delivery. This thesis contributes to focusing on improving the scheduling process by driving more information being shared upstream in the decision making process and therefore decreasing the number of days needed to prepare the airplane for delivery.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: 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.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 74).
Subjects
Sloan School of Management.
Institute for Data, Systems, and Society.
Engineering Systems Division.
Leaders for Global Operations Program.
MIT Department
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Sloan School of Management
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