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Massachusetts Institute of Technology

Aerospace automated drilling and fastening technology product selection framework

Abstract

dc:description.abstract

Ascent Aerospace is a global tooling and factory automation supplier for the Aerospace industry. Ascent's customers are typically introducing automation for the first time, or have already introduced automation to their production systems and are wary of its challenges. Choosing the appropriate technology is essential in ensuring successful implementation for both Ascent's customers and Ascent itself. Ascent has two different business units that produce equipment to automate the drilling and fastening of aerospace structures. These two units each come with vast product portfolios, and distinct approaches to address customer needs. This thesis focuses on an efficient method of evaluating how Ascent's current products align with customer's requirements, as well as identifying any technology gaps needing further exploration.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Sloan School of Management
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Talus, Zachary David.
Advisor dc:contributor.advisor
  • Julie Shah and Michael Cusumano.

Subjects

dc:subject × 3

Rights

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

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/122598
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/122598

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

Talus, Zachary David.. Aerospace automated drilling and fastening technology product selection framework. Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122598