Developing Business AI Implementation Methodology and Proof
of Concept ML Models to Improve Suture Quality at
Extrusion/Orientation
Name
rawden-ksrawden-mba-mgt-2021-thesis.pdf
Description
Thesis PDF
Size
2.38 MB
Format
Adobe PDF
Checksum (MD5)
c08f2f92cb8c7a20e4fd4e835ac95c79
Author(s)
Rawden, Katherine Suzanne
Advisor(s)
Freund, Daniel
Simchi-Levi, David
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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