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Massachusetts Institute of Technology

787 mid-body job precedence networks for improving production rate

Abstract

dc:description.abstract

In a complex manufacturing environment, generating schedules, identifying deviations, and recovering from delays have a significant impact on total operational performance. At Boeing's 787 plant, a precedence network was generated which defines the entire build sequence for a mid-body fuselage. This job-level build sequence enables planners to generate optimized and feasible resource-constrained schedules. The network also forms the foundation for a visual control system on the factory floor. This web-based tool is designed to improve routine production control decisions at all levels by presenting build status in a cohesive and concise format. Using this tool, the plant's stakeholders can effectively identify and prioritize schedule deviations before they cascade lead to major delays, resulting in an overall improvement in resource efficiency and production rate.

Degree

thesis:*
Department dc:contributor.department
Leaders for Global Operations Program at MIT
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2012

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Chen, Jason L., 1979-
Advisor dc:contributor.advisor
  • Daniel E. Whitney and Steven J. Spear.

Subjects

dc:subject × 3

Rights

dc:rights
Statement dc:rights
  • M.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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/73380
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/73380

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Chen, Jason L., 1979-. 787 mid-body job precedence networks for improving production rate. Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/73380