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Developing Business AI Implementation Methodology and Proof of Concept ML Models to Improve Suture Quality at Extrusion/Orientation

Author(s)
Rawden, Katherine Suzanne
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Advisor
Freund, Daniel
Simchi-Levi, David
Terms of use
In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/
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Abstract
Digital transformation has begun to infiltrate all industries, signifying the advent of a new era: the fourth industrial revolution Through the digital transformation of business processes, key bottlenecks that limit firm growth can be mitigated, allowing for unprecedented scalability, scope, and opportunities for learning. The goals of this project are twofold within the value chain of a product family of absorbable sutures. The first goal is to provide an assessment of the driving motivations and structural transformation required for a medical device manufacturer to deploy business artificial intelligence (AI) and evolve into a digital firm. The second goal is to apply digital transformation methodology and machine learning (ML) to a proof of concept use case. To accomplish the first goal, a road map was developed for the deployment of business AI and an assessment of the digital maturity of the suture value chain was conducted. Random forest and linear regression ML models were developed to assist in root cause analysis at the extrusion/orientation process step of the suture manufacturing process.
Date issued
2021-06
URI
https://hdl.handle.net/1721.1/139021
Department
Sloan School of Management; Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Publisher
Massachusetts Institute of Technology

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