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dc.contributor.advisorFreund, Daniel
dc.contributor.advisorSimchi-Levi, David
dc.contributor.authorRawden, Katherine Suzanne
dc.date.accessioned2022-01-14T14:45:07Z
dc.date.available2022-01-14T14:45:07Z
dc.date.issued2021-06
dc.date.submitted2021-06-10T19:13:24.348Z
dc.identifier.urihttps://hdl.handle.net/1721.1/139021
dc.description.abstractDigital 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.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright retained by author(s)
dc.rights.urihttps://rightsstatements.org/page/InC-EDU/1.0/
dc.titleDeveloping Business AI Implementation Methodology and Proof of Concept ML Models to Improve Suture Quality at Extrusion/Orientation
dc.typeThesis
dc.description.degreeS.M.
dc.description.degreeM.B.A.
dc.contributor.departmentSloan School of Management
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.identifier.orcid0000-0002-0061-3734
mit.thesis.degreeMaster
thesis.degree.nameMaster of Science in Civil and Environmental Engineering
thesis.degree.nameMaster of Business Administration


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