Digitalizing R&D in manufacturing sector : machine learning, infrastructure, system architecture and knowledge management

This thesis addresses the topic of data utilization and data analytics in research and development (R&D) functions of the manufacturing sector. Many companies in the manufacturing sector have generated significant quantities of data in their histories, but only a tiny part of these data is utilized. With the significant progress in big data analytics and machine learning, the companies in the manufacturing sector are able to upgrade their R&D capability by establishing a system to better collect and analyze their data. Using machine learning can tremendously enhance R&D's capability in interpreting data and giving recommendations regarding solutions. The data system could also help improve an R&D organization's productivity by significantly reducing repeated work. This thesis designs an R&D system that collects R&D data by lab automation, analyzes data by built-in machine learning algorithms, and provides recommendations by gathering inputs for development targets. This thesis also covers aspects of knowledge management within the corporation when implementing such a data system. The organizational capability to implement this data system is also discussed.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Li, Xuedong (Xuedong D.)

Advisor dc:contributor.advisor

• Donna H. Rhodes and Roy E Welsch.

• Engineering and Management Program.
• System Design and Management Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/132888

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132888