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Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develo

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Contributorsde Guzman, Lorenzo (Co-author) / Chmelnik, Nathan (Co-author) / Martz, Emma (Co-author) / Johnson, Katelyn (Co-author) / Ju, Feng (Thesis director) / Courter, Brandon (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / School of Politics and Global Studies (Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develo

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

ContributorsJohnson, Katelyn Rose (Co-author) / Martz, Emma (Co-author) / Chmelnik, Nathan (Co-author) / de Guzman, Lorenzo (Co-author) / Ju, Feng (Thesis director) / Courter, Brandon (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develo

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

ContributorsChmelnik, Nathan (Co-author) / de Guzman, Lorenzo (Co-author) / Johnson, Katelyn (Co-author) / Martz, Emma (Co-author) / Ju, Feng (Thesis director) / Courter, Brandon (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develo

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

ContributorsMartz, Emma Marie (Co-author) / de Guzman, Lorenzo (Co-author) / Johnson, Katelyn (Co-author) / Chmelnik, Nathan (Co-author) / Ju, Feng (Thesis director) / Courter, Brandon (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
Understanding customer preference is crucial for new product planning and marketing decisions. This thesis explores how historical data can be leveraged to understand and predict customer preference. This thesis presents a decision support framework that provides a holistic view on customer preference by following a two-phase procedure. Phase-1 uses cluster

Understanding customer preference is crucial for new product planning and marketing decisions. This thesis explores how historical data can be leveraged to understand and predict customer preference. This thesis presents a decision support framework that provides a holistic view on customer preference by following a two-phase procedure. Phase-1 uses cluster analysis to create product profiles based on which customer profiles are derived. Phase-2 then delves deep into each of the customer profiles and investigates causality behind their preference using Bayesian networks. This thesis illustrates the working of the framework using the case of Intel Corporation, world’s largest semiconductor manufacturing company.
ContributorsRam, Sudarshan Venkat (Author) / Kempf, Karl G. (Thesis advisor) / Wu, Teresa (Thesis advisor) / Ju, Feng (Committee member) / Arizona State University (Publisher)
Created2017