Difference between revisions of "ANLY482 AY2017-18 T1 Group2"
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| − | [[ | + | [[ANLY482_AY2017-18_T1_Group2 Project EZLin_About Us|<font face ="Lucida Grande" color="#FFFFFF"><strong> ABOUT US </strong></font>]] |
| style="border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900;" width="1%" | | | style="border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900;" width="1%" | | ||
| style="padding:0.3em; font-family:Arimo; font-size:110%; border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900; text-align:center;" width="10%" | | | style="padding:0.3em; font-family:Arimo; font-size:110%; border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900; text-align:center;" width="10%" | | ||
| − | [[ | + | [[ANLY482_AY2017-18_T1_Group2 Project EZLin_Project Overview|<font face ="Lucida Grande" color="#FFFFFF"><strong> PROJECT OVERVIEW</strong></font>]] |
| style="border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900;" width="1%" | | | style="border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900;" width="1%" | | ||
| style="padding:0.3em; font-family:Arimo; font-size:110%; border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900; text-align:center;" width="10%" | | | style="padding:0.3em; font-family:Arimo; font-size:110%; border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900; text-align:center;" width="10%" | | ||
| − | [[ | + | [[ANLY482_AY2017-18_T1_Group2 Project EZLin_Project_Management|<font face ="Lucida Grande" color="#FFFFFF"><strong>PROJECT MANAGEMENT </strong></font>]] |
| style="border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900;" width="1%" | | | style="border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900;" width="1%" | | ||
| style="padding:0.3em; font-family:Arimo; font-size:110%; border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900; text-align:center;" width="10%" | | | style="padding:0.3em; font-family:Arimo; font-size:110%; border-bottom:2px solid #bf1900; border-top:2px solid #bf1900; background:#bf1900; text-align:center;" width="10%" | | ||
| − | [[ | + | [[ANLY482_AY2017-18_T1_Group2 Project EZLin_Documentation|<font face ="Lucida Grande" color="#FFFFFF"><strong> DOCUMENTATION</strong></font>]] |
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| + | | style="vertical-align:top;width:20%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:1px solid #b21301; font-family:tahoma"> [[ANLY482_AY2017-18_T1_Group2| <b>Current</b>]] | ||
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| + | | style="vertical-align:top;width:20%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:1px solid #b21301; font-family:tahoma"> [[ANLY482_AY2017-18_T1_Group2 Project EZLin_Midterm| <b>Midterm</b>]] | ||
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| + | | style="vertical-align:top;width:20%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:1px solid #b21301; font-family:tahoma"> [[ANLY482_AY2017-18_T1_Group2 Project EZLin_Final| <b>Final</b>]] | ||
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| − | [[ | + | [[File:EZLin Logo.jpg|300px]] |
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<div align="left"> | <div align="left"> | ||
| − | <div style="background: #fcebea; padding: 12px; font-family: Arimo; font-size: 18px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: # | + | <div style="background: #fcebea; padding: 12px; font-family: Arimo; font-size: 18px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #bf1900 solid 32px;"><font color="#bf1900">Project Introduction</font></div> |
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| + | The use of programming to automate and cleanse the dataset has numerous benefits that improves the efficiency and productivity of doing things. Python, an object-oriented programming language, is often well-regarded for its ease-of-usage and large variety of standard libraries such as Pandas and Tensorflow. | ||
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| + | In order to truly understand the data-automation and transformation process, a collaboration with Johnson & Johnson (JnJ) was made to work on a real-life project focusing on JnJ supply chain network. The objective of this project was to not only help the company understand its end-to-end supply chain network but to also offer insights from data through visualisations done on Tableau. This requires the raw data to be rigorously cleansed and transformed in order for any visualisation to be done, which was in line with our aim of understanding the data-automation and transformation process. Through Tableau, the different types of cost and plants were clearly visualised and represented, providing much insights and setting a foundation for an end-to-end supply chain flow for the company. | ||
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| + | The tangible result from this project was the quick data cleaning and transformation process, that helped integrate the different Excel file and allowing JnJ to identify areas in which attention must be paid to improve its supply chain information accuracy. | ||
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| + | [[File:EZLin Progress.PNG|1000px]] | ||
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Latest revision as of 19:44, 3 December 2017
Project Introduction
The use of programming to automate and cleanse the dataset has numerous benefits that improves the efficiency and productivity of doing things. Python, an object-oriented programming language, is often well-regarded for its ease-of-usage and large variety of standard libraries such as Pandas and Tensorflow.
In order to truly understand the data-automation and transformation process, a collaboration with Johnson & Johnson (JnJ) was made to work on a real-life project focusing on JnJ supply chain network. The objective of this project was to not only help the company understand its end-to-end supply chain network but to also offer insights from data through visualisations done on Tableau. This requires the raw data to be rigorously cleansed and transformed in order for any visualisation to be done, which was in line with our aim of understanding the data-automation and transformation process. Through Tableau, the different types of cost and plants were clearly visualised and represented, providing much insights and setting a foundation for an end-to-end supply chain flow for the company.
The tangible result from this project was the quick data cleaning and transformation process, that helped integrate the different Excel file and allowing JnJ to identify areas in which attention must be paid to improve its supply chain information accuracy.
