{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/146697"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/146697","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Digital Thread and Analytics Model to Improve Quality Controls in Surgical Stapler","abstract":"Ethicon, Inc. currently collects data in various stages of its supply chain, but the information is fragmented across the end-to-end chain, resulting in a reactive supply chain. This study seeks to understand the data maturity of Ethicon's surgical stapler through exploratory data analysis and experimental data modeling with machine learning techniques in order to provide recommendations on strategies for digital readiness in a medical device and outline potential opportunities digitization can bring. The goals of this project are: 1. Enable end-to-end visibility into the currently supply chain by building a digital thread for a surgical stapler product 2. Create visualizations to provide visibility and insight into the existing production process 3. Use advanced analytics models to identify key components or measurements that affect the product's Force to Fire final quality inspection results The digital thread and models built laid the groundwork for the Ethicon team to understand the current state of their systems and will be used as the team conducts experiments to further understand the actual devices being built.","abstract_html":"Ethicon, Inc. currently collects data in various stages of its supply chain, but the information is fragmented across the end-to-end chain, resulting in a reactive supply chain. This study seeks to understand the data maturity of Ethicon's surgical stapler through exploratory data analysis and experimental data modeling with machine learning techniques in order to provide recommendations on strategies for digital readiness in a medical device and outline potential opportunities digitization can bring. The goals of this project are: 1. Enable end-to-end visibility into the currently supply chain by building a digital thread for a surgical stapler product 2. Create visualizations to provide visibility and insight into the existing production process 3. Use advanced analytics models to identify key components or measurements that affect the product's Force to Fire final quality inspection results The digital thread and models built laid the groundwork for the Ethicon team to understand the current state of their systems and will be used as the team conducts experiments to further understand the actual devices being built.","abstract_has_math":false,"creators":["Hau, Han-Ching Elizabeth"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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This study seeks to understand the data maturity of Ethicon's surgical stapler through exploratory data analysis and experimental data modeling with machine learning techniques in order to provide recommendations on strategies for digital readiness in a medical device and outline potential opportunities digitization can bring. The goals of this project are: 1. Enable end-to-end visibility into the currently supply chain by building a digital thread for a surgical stapler product 2. Create visualizations to provide visibility and insight into the existing production process 3. Use advanced analytics models to identify key components or measurements that affect the product's Force to Fire final quality inspection results The digital thread and models built laid the groundwork for the Ethicon team to understand the current state of their systems and will be used as the team conducts experiments to further understand the actual devices being built."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.B.A.","S.M."]},{"key":"dc:title","label":"Title","values":["Digital Thread and Analytics Model to Improve Quality Controls in Surgical Stapler"]}]}],"canonical_facts":{"dc:contributor.advisor":["Welsch, Roy E.","Daniel, Luca"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","Sloan School of Management"],"dc:creator":["Hau, Han-Ching Elizabeth"],"dc:date.accessioned":["2022-11-30T19:41:59Z"],"dc:date.available":["2022-11-30T19:41:59Z"],"dc:date.issued":["2022-05"],"dc:description.abstract":["Ethicon, Inc. currently collects data in various stages of its supply chain, but the information is fragmented across the end-to-end chain, resulting in a reactive supply chain. This study seeks to understand the data maturity of Ethicon's surgical stapler through exploratory data analysis and experimental data modeling with machine learning techniques in order to provide recommendations on strategies for digital readiness in a medical device and outline potential opportunities digitization can bring. The goals of this project are: 1. Enable end-to-end visibility into the currently supply chain by building a digital thread for a surgical stapler product 2. 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Use advanced analytics models to identify key components or measurements that affect the product's Force to Fire final quality inspection results The digital thread and models built laid the groundwork for the Ethicon team to understand the current state of their systems and will be used as the team conducts experiments to further understand the actual devices being built."],"dc:description.degree":["M.B.A.","S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/146697"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Digital Thread and Analytics Model to Improve Quality Controls in Surgical Stapler"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Business Administration","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:14Z"}