Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, February, 2021 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 145-152). / 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. / by Xuedong Li. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/132888 |
| Date | January 2021 |
| Creators | Li, Xuedong (Xuedong D.) |
| Contributors | Massachusetts Institute of Technology. Engineering and Management Program., System Design and Management Program., Massachusetts Institute of Technology. Engineering and Management Program |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 195 pages, application/pdf |
| 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., http://dspace.mit.edu/handle/1721.1/7582 |
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