Difference between revisions of "ANLY482 AY2017-18 Term 2"
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− | <td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2017- | + | <td>[https://wiki.smu.edu.sg/ANLY482/ANLY482_AY2017-18T2_Group04 Provide an easy to understand project title]</td> |
<td>Describe the content of the project. It should not be more than 350 words.</td> | <td>Describe the content of the project. It should not be more than 350 words.</td> | ||
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Revision as of 10:16, 4 January 2018
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Project Title | About the Project | Student Member(s) | Project Supervisor | Sponsor |
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Provide an easy to understand project title | Describe the content of the project. It should not be more than 350 words. |
Group 01 - Group Name
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | XXX Pte. Ltd. |
Close-the-Loop | Arup is an engineering consultancy designing architecture, infrastructure, and urban planning worldwide. As part of their work on Downtown Line 3, engineering design and construction has happened in phases, producing data on the design specifications made in response to ground surveying data collected, and data on the implementation of those designs by contractors in each phase.
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Group 02 - Josh & Fried Investigations |
Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | Arup Singapore Pte. Ltd. |
Describe the content of the project. It should not be more than 350 words. |
Group 03
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | YCH Group Pte Ltd | |
Provide an easy to understand project title | Describe the content of the project. It should not be more than 350 words. |
Group 04
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | SGAG Media Pte. Ltd. |
Predictive Parking Enforcement - Tackling indiscriminate parking by bike-sharing users | In_Progress. |
Group 08
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | oBike Asia Pte. Ltd. |
Koi Logistics Analysis | KOI is one of the most well-known bubble tea brands in Singapore, founded in 2006. Despite this, it is facing logistics problems in their daily operations such that employees in different branches have difficulty predicting the right amount of ingredients to order.
Moreover, space constraints of each branch is different and have to be taken into consideration, since not every branch can order the same amount of quantity, leading to a steep learning curve for new employees. Current employees also do not fully understand how much ingredients would be needed during promotional periods of certain products. As such, our project aims to focus on predictive analysis tools such as multinomial regression models, along with data (e.g. sales, shipment, promotion details) provided by KOI to better understand correlations between different factors and sales, helping employees make better informed decisions on how much to order in the future. |
Group 09
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | KOI Thé Singapore Pte Ltd |
Delivery Schedule Optimisation |
In Progress
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Group 14
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | Logistic Company X |
In_progress | In_Progress. |
Group 15
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | Asia Pacific Breweries Pte. Ltd. |
In_progress | In_Progress. |
Group 17
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | National Council of Social Service |
In_progress | In_Progress |
Group 25
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | The Chope Group Pte. Ltd. |
In_progress | In_Progress. |
Group 31
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Prof. Kam Tin Seong Associate Professor of Information Systems (Practice) | oBike |