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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.

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

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

Accessible STEAM (Science, Technology, Engineering, Art, and Mathematics) education is imperative in creating the future innovators of the world. This business proposal is for a K-8 STEAM Museum to be built in the Novus Innovation Corridor on Arizona State University (ASU)’s Tempe campus. The museum will host dynamic spaces that

Accessible STEAM (Science, Technology, Engineering, Art, and Mathematics) education is imperative in creating the future innovators of the world. This business proposal is for a K-8 STEAM Museum to be built in the Novus Innovation Corridor on Arizona State University (ASU)’s Tempe campus. The museum will host dynamic spaces that are constantly growing and evolving as exhibits are built by interdisciplinary capstone student groups- creating an internal capstone project pipeline. The intention of the museum is to create an interactive environment that fosters curiosity and creativity while acting as supplemental learning material to Arizona K-8 curriculum. The space intends to serve the greater Phoenix area community and will cater to underrepresented audiences through the development of accessible education rooted in equality and inclusivity.

ContributorsPeters, Abigail J (Author) / McCarville, Daniel R. (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
This study aims to explore the prevalence of smartphone, smartwatch, and fitness tracker ownership among college students, and compare the popularity of each device in tracking health-related habits such as physical activity, eating, and sleep. In addition, this study aims to analyze the effectiveness of each device for achieving personal

This study aims to explore the prevalence of smartphone, smartwatch, and fitness tracker ownership among college students, and compare the popularity of each device in tracking health-related habits such as physical activity, eating, and sleep. In addition, this study aims to analyze the effectiveness of each device for achieving personal health goals in all three categories. Research for this study was conducted using an Institutional Review Board (IRB) approved survey that was distributed electronically to various Greek and student organizations around Arizona State University campuses. In total, 183 responses were considered, with participants ranging from ages 18 to 23. Participants were required to own or possess a smartphone to be eligible to complete the survey. After seven days of data collection, the results were then analyzed using Qualtrics. The results revealed that smartwatch and fitness tracker ownership is not prevalent within the Arizona State University demographic. In addition, after comparing device popularity across each habit-tracking category, it is apparent that the smartphone is the most used device for tracking. Finally, when looking at device effectiveness in relation to achieving health goals, smartwatches consistently scored higher than smartphones. Supplemental research should be conducted to further explore the prevalence and effectiveness of habit tracking. This research should include a larger sample size and a more evenly spread gender demographic.
ContributorsMeyer, Allison Hope (Author) / Levinson, Simin (Thesis director) / Carr, Natasha (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05