Difference between revisions of "ANLY482 Sustainability Jobs Project Overview"

Latest revision as of 00:13, 24 April 2015

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Project Background

Trade transported by sea has grown fourfold since 1970s and it is still growing. In 2011, the 360 commercial ports of America took in goods that are 80 times the value of all American trade in 1960 (George, 2013). And in this industry, container overcapacity has long remain an issue (Business Monitor International, 2012).

To look into the utilisation of the containers, our team has collaborated with Green Transformation Lab (GTL) to explore the possible factors that are affecting utilisation rate. GTL is a joint initiative by SMU and DHL aimed at accelerating the evolution of sustainable logistics across Asia Pacific. Leveraging SMU’s multi-faculty academic excellence and DHL’s sustainability services, expertise and capability in supply chains, the Green Transformation Lab focuses on creating solutions that help companies transform their supply chains, becoming greener, more resource efficient and sustainable. Our team is currently embarking on gaining further insights on DHL’s Ocean Freight data, which contains the information about each individual shipment that a company has when engaging DHL.

The shipping and freight industry is a highly dynamic market. Shippers and shipping lines are constantly looking towards designing better shipping and distribution networks to improve efficiency and vessel economics (Notteboom, 2012). At present, many FCL containers are not fully maximised. Therefore, the scope of this project is to look into improving the fill rate efficiency for different trade lanes and geographical location.

Project Objectives

The dataset for this project contains the shipment data on DHL shipments for an IT company in year 2012 and 2013. This comprehensive set of data contains information on both operational and financial transactions. As this project focuses on the aspect of supply chain, the financial data will thus be ignored. Our objectives for this project is to,

• Identify the various individual factors that affect the fill rate efficiency of containerisation
• Analyse patterns and determine the relationship for these various factors

Factors that will be looked into are:
1. Origin - port where goods are loaded
2. Destination - port where goods are unloaded
3. Location - geographical areas
4. Carriers - companies that transport goods from origin to destination
5. Size of containers
6. Different time periods
7. Trade Lanes
8. Volume (m3)
9. Gross Weight

@@ Line 24: / Line 24: @@
 <br/>
-In the society today, the pace of globalization is undeniable. With remarkable increase in transportation, communication and technology, it has made the world more interdependent than ever. However, as companies reap in the success of globalization, Mother Earth sacrifices. <br/><br/>
+Trade transported by sea has grown fourfold since 1970s and it is still growing. In 2011, the 360 commercial ports of America took in goods that are 80 times the value of all American trade in 1960 (George, 2013). And in this industry, container overcapacity has long remain an issue (Business Monitor International, 2012). <br/>
-Resource depletion and severe environmental issues has not only motivated the increasing interest towards renewable resources but also, sustainability policies. The limelight on sustainability development has proved to be a valuable area for development reaping huge benefits. Businesses are now moving to take an active role in developing practices promoting sustainability with initiatives such as sustainable logistics. <br/><br/>
+To look into the utilisation of the containers, our team has collaborated with Green Transformation Lab (GTL) to explore the possible factors that are affecting utilisation rate. GTL is a joint initiative by SMU and DHL aimed at accelerating the evolution of sustainable logistics across Asia Pacific. Leveraging SMU’s multi-faculty academic excellence and DHL’s sustainability services, expertise and capability in supply chains, the Green Transformation Lab focuses on creating solutions that help companies transform their supply chains, becoming greener, more resource efficient and sustainable.
+Our team is currently embarking on gaining further insights on DHL’s Ocean Freight data, which contains the information about each individual shipment that a company has when engaging DHL.
-The Green Transformation Lab (GTL) is a joint initiative by SMU and DHL aimed at accelerating the evolution of sustainable logistics across Asia Pacific. Leveraging SMU’s multi-faculty academic excellence and DHL’s sustainability services, expertise and capability in supply chains, the Green Transformation Lab focuses on creating solutions that help companies transform their supply chains, becoming greener, more resource efficient and sustainable.<br/><br/>
-We will be embarking on gaining further insights on GTL’s sustainability heat map project, which is a group of heat maps with global profiling of areas and trends on sustainability related jobs as well as sustainability related topics on Twitter. <br/>
 [[Image:Motivation SCM.png|Center|800px]]
 ==<div style="background: #ffffff; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px; text-transform:lowercase;letter-spacing:-0.1em;font-size:24px"><font color=#3d3d3d> </font></div>==
-Due to the increasing gap between companies’ sustainability initiatives and the workforce involvement, more efforts are placed in integrating sustainability concepts into business models and jobs so as to create a greater impact on the environment. <br/><br/>
+The shipping and freight industry is a highly dynamic market. Shippers and shipping lines are constantly looking towards designing better shipping and distribution networks to improve efficiency and vessel economics (Notteboom, 2012). At present, many FCL containers are not fully maximised. Therefore, the scope of this project is to look into improving the fill rate efficiency for different trade lanes and geographical location.
-Through this project, we are interested to understand the elements powering the creation of sustainability jobs, be it government policies, workforce preference or the pervasive impact of social media. <br/>
 ==<div style="background: #ffffff; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px; text-transform:lowercase;letter-spacing:-0.1em;font-size:24px"><font color=#3d3d3d>Project Objectives</font></div>==
-The main objective of our project is to analyze crawled real-time tweets as well as job information related to sustainability and environment at a deeper level so as to gain more insights, relationships and trends. We will also be exploring various environmental and socio-economic indicators data justified by the United Nations (UN). Through multiple analytical methodologies, we hope to answer the below project objectives. <br/><br/>
+</div>
+The dataset for this project contains the shipment data on DHL shipments for an IT company in year 2012 and 2013. This comprehensive set of data contains information on both operational and financial transactions. As this project focuses on the aspect of supply chain, the financial data will thus be ignored.
+Our objectives for this project is to, <br/><br/>
+'''•	Identify the various individual factors that affect the fill rate efficiency of containerisation <br/>'''
+'''•	Analyse patterns and determine the relationship for these various factors <br/><br/>'''
-*From the crawled real-time tweets from Twitter, explore the relationship between highly related topics and impact generated by posts that are labeled with the hash tag “#sustainability”.
+Factors that will be looked into are: <br/>
-**Which topics has the highest frequency of being mentioned with “#sustainability”?
+.	Origin - port where goods are loaded <br/>
-**Define the type and genre of users posting tweets related to sustainability (based on level of popularity and influence of the users)
+.	Destination - port where goods are unloaded <br/>
-**Are companies generating tweets related to sustainability? Differentiate the topics driven by social networks and commercial companies.
+.	Location - geographical areas  <br/>
-**Are sustainability hash tags posted by users highly related to each other? Are these tweets relationship-directed or non-directed? Are there any cascade effects on the sustainability tweets?
+.	Carriers - companies that transport goods from origin to destination  <br/>
-<br/><br/>
+.	Size of containers <br/>
-*To determine the correlation relationship between real-time tweets and job information related to sustainability and environment.
+.	Different time periods <br/>
-**What are the impacts of this relationship?
+.	Trade Lanes <br/>
-**Are there more jobs created in the respective categories due to this correlation
+.	Volume (m3) <br/>
-<br/><br/>
+.	Gross Weight <br/>
-*Based on the crawled data on sustainability jobs, is there an association of policies (e.g. country policies, governmental policies) to sustainability jobs?
-**What are the effects on the creation of sustainability jobs due to government policies such as sustainable development and resource efficiency?
-**Are nations’ environmental watchdogs contributing to the spike in sustainability jobs in the recent years?
-<br/><br/>
-*To find out whether other environmental and socio-economic indicators have a form of impact sustainability jobs based on locations such as Europe, Asia and America.
-**Identify the effects of various macro-economic metrics and UN environmental indicators to determine whether there is an existing trend with sustainability jobs in the various regions.
-</div>

Difference between revisions of "ANLY482 Sustainability Jobs Project Overview"

Latest revision as of 00:13, 24 April 2015

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