EzLog: Data visualization for logistics
With the increasing availability of data in the logistics industry due to the digitalization trend, interest and opportunities for leveraging analytics in supply chain management to make data-driven decisions is growing rapidly. In this paper, we introduce EzLog, an integrated visualization prototyp...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2019
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/4474 https://ink.library.smu.edu.sg/context/sis_research/article/5477/viewcontent/EzLog_Data_Visualization_for_Logistics__Paper_id_22_.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | With the increasing availability of data in the logistics industry due to the digitalization trend, interest and opportunities for leveraging analytics in supply chain management to make data-driven decisions is growing rapidly. In this paper, we introduce EzLog, an integrated visualization prototype platform for supply chain analytics. This web-based platform built by two undergraduate student teams for their capstone course can be used for data wrangling and rapid analysis of data from different business units of a major logistics company. Other functionalities of the system include standard processes to perform data analysis such as supervised extraction, transformation, loading (ETL), data type validation and mapping. Weather, real-time stock market and Twitter data can also be collected through EzLog’s web crawling function, as examples of external data that can be leveraged for more insights. Aiming to be user-centric, inputs from end-users were actively pursued in the design of the platform. Easily scalable, Logisticians can access the platform on their machines through Amazon Web Services (AWS) instances to perform descriptive and predictive analysis, including sentiment analysis and topic modeling, to better capture insights and identify patterns in logistics data. |
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