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Massachusetts Institute of Technology

Continuous Improvement Framework for a Multi-model Production Line

Abstract

dc:description.abstract

In manufacturing, it is vital to identify current or potential bottlenecks and create plans to either eliminate, mitigate, or prevent them. There are many ways in which to assess a given system and identify the bottleneck: visual inspection, production data, anecdotal evidence, and experience, to name a few. Most strategies are a blend of methods with experience and anecdotal evidence comprising the majority of the approach which leads to large discrepancies between assessors. This thesis details a method to standardize the assessment method of under performing portions of a manufacturing line while still giving the assessor the ability to leverage their experience, expertise, and creativity to solve the problem. This framework will be applied in a case study conducted at Nissan North America’s Canton, Mississippi Assembly Facility resulting in reclamation of approximately 50 minutes of production time eliminating the overtime requirement for a pair of manufacturing cells.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Sandifer, Darron
Advisors dc:contributor.advisor
  • Willems, Sean
  • Hardt, David

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/151922
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/151922

Chain of custody

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

Sandifer, Darron. Continuous Improvement Framework for a Multi-model Production Line. Massachusetts Institute of Technology, 2023. https://hdl.handle.net/1721.1/151922