Scaling Metal Additive Manufacturing from R&D to Production

Metal additive manufacturing (AM) has been successfully commercialized, yet widespread adoption has not been achieved so far. This is partly because companies struggle to operate AM factories profitably and efficiently at industrial scale. This thesis proposes a data strategy to address this challenge and support the rapid growth and successful operation of an additive manufacturing factory – from R&D to production. The central idea is to connect relevant data to the central unit of a build. A build is proposed as one unit of manufacturing in AM. Connecting commercial data, information about geometry, processing, materials, post-processing, and testing to a build allows to gain a system-level understanding while also being able to dive into details where needed. After implementation, the framework can be used to (i) qualify processes and certify materials, (ii) improve quoting quality and efficiency, (iii) support engineering and R&D, (iv) derive critical operations KPIs such as revenue per build, builds per week, and days per build, which can be used for budgeting and capacity planning as well as business control, (v) make strategic decisions on capital expenses and headcount planning, as well as (iv) ensure traceability of materials and parts. Together, these applications support decision makers as well as commercial and technical staff in their work, both strategic as well as during day-to-day operations.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Weißbach, Reimar

Advisors dc:contributor.advisor

• Roemer, Thomas
• Hart, John

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/155975

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/155975