This is a hands-on course where students will learn a mix of theoretical and practical tools. Using these tools, they will solve a variety of supply chain problems, both analytically and numerically. Students will examine data and use this to understand supply, demand, and inventory levels using R to model many of these problems. The course will also review the data and assess its suitability for modelling. Time series, Markov chain, optimal control, linear programming, statistical analysis, and other mathematical tools will be used to reveal the data's secrets. The bottom line is these insights will be used to make recommendations to firms and other decision makers. Students will also look at qualitative problems through the examination and discussions of cases in class. Not everything can be distilled to a number and so a holistic view of business problems will be taken to make more strategic recommendations. Teams of students will then solve and present these case results
Spring | Summer | Fall | ||
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(Session 1) | (Session 2) | |||
2023 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2022 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2021 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2020 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2019 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2018 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2017 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2016 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2015 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2014 |
Data Resource Management (4c)
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2013 |
Data Resource Management (4c)
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2012 |
Data Resource Management (4c)
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Data Resource Management (4c)
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2011 |
Data Resource Management (4c)
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2010 |
Data Resource Management (4c)
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2009 |
Data Resource Management (4c)
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2008 |
Data Resource Management (4c)
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2007 | ||||
2006 | ||||
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1998 |