Difference between revisions of "Improved Decisions for Ocean FreightsAnalysis"
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|<br><center>[[File:RemovedAnomalies.png|800px]]</center> | |<br><center>[[File:RemovedAnomalies.png|800px]]</center> | ||
− | For the top graph, some anomalies in the data are revealed and also implies wrong data around the month of June in 2013 and 2014 where percentage reached 400%, 3 times more than the limit of percentage utilization. | + | For the top graph, some anomalies in the data are revealed and also implies wrong data around the month of June in 2013 and 2014 where percentage reached 400%, 3 times more than the limit of percentage utilization. During interims, to remove that dataset, we put a constraint on the axis such that only percentages between 0% and 100%, as can be seen in the bottom graph. For finals, we do not remove any datasets. Instead, we replace the anomalous data with the average percentage utilization of the whole dataset, so that we can keep the row of data. |
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[[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Carbon Emissions</strong></span>]] | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Carbon Emissions</strong></span>]] | ||
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+ | |<br><center>[[File:Carbon1.PNG|800px]]</center> | ||
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+ | In this treemap, the larger boxes indicate higher total carbon emissions. There are different boxes for the same company for emissions in different years. | ||
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+ | <br><center>[[File:Carbon2.PNG|600px]]</center> | ||
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+ | Over here, we plot the carbon emissions against the number of transactions. In addition, there is a straight line that represents the function y=f(x) where y is the average amount of carbon emitted for the selected industry for x number of transactions. In this visualization, users can clearly see that companies AUTO-1 and AUTO-4 are polluting higher than the average, regardless of the number of transactions they have while companies DAIMLR and GOODYR are polluting less than average. | ||
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{| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | {| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | ||
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[[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Trade Lanes Profiling</strong></span>]] | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Trade Lanes Profiling</strong></span>]] | ||
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+ | |<br><center>[[File:Tradelanes.PNG|800px]]</center> | ||
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+ | In Trade Lanes Profiling, the ports, trading lanes and regions become the main focus of analysis. In this visualization, the larger the bubble, the more times that port has records of low percentage utilization of FCL containers. For chosen trade regions, the charts below are used to display information of the trade regions in terms of average percentage utilization at a very high level. The overall average utilization is shown on the top left and the time series is shown on the top right. We then provide another level of breakdown for the trade regions with a drill down by Industries in that region and the companies within those industries. This added layer again gives another angle to Customer Profiling as well, by identifying customers through the trade lanes they employ. | ||
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{| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | {| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | ||
| style="background-color:#006600; ; color:#ffffff; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff; width:50%; " | | | style="background-color:#006600; ; color:#ffffff; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff; width:50%; " | | ||
− | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong> | + | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Percent Groups</strong></span>]] |
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+ | |<br><center>[[File:Percentgroups.PNG|800px]]</center> | ||
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+ | The charts here separate the data into quartiles for more in-depth analysis. For example, the stacked bar charts give more information that can otherwise be hard to read from the cumulative graph in the customer profiling dashboard. | ||
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[[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Boxplots</strong></span>]] | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Boxplots</strong></span>]] | ||
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+ | |<br><center>[[File:Boxplots.PNG|800px]]</center> | ||
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+ | We also provide box plots to complement the cumulative distribution graphs so that the distribution can be better understood. | ||
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{| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | {| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | ||
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[[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Cost Treemap</strong></span>]] | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>Cost Treemap</strong></span>]] | ||
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+ | |<br><center>[[File:Boxplots.PNG|800px]]</center> | ||
+ | |||
+ | We also provide box plots to complement the cumulative distribution graphs so that the distribution can be better understood. | ||
+ | |||
|} | |} | ||
+ | |||
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{| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | {| style="background-color:#ffffff; width:80%; font-family:Century Gothic; font-size:15px; margin: 3px auto 0 auto;" | | ||
| style="background-color:#006600; ; color:#ffffff; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff; width:50%; " | | | style="background-color:#006600; ; color:#ffffff; text-align: center; border-top:solid #ffffff; border-bottom:solid #ffffff; width:50%; " | | ||
− | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>FCL/LCL Cost | + | [[Improved_Decisions_for_Ocean_Freights|<span style="color:#ffffff"><strong>FCL/LCL Cost Comparison</strong></span>]] |
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+ | |<br><center>[[File:Boxplots.PNG|800px]]</center> | ||
+ | |||
+ | We also provide box plots to complement the cumulative distribution graphs so that the distribution can be better understood. | ||
+ | |||
|} | |} |
Revision as of 14:39, 13 November 2015
No clear relationship between shipper country and utilization rate |
![]() We see from the graph on the left that shipments originating from Chile, Singapore and the United States have the lowest utilization rates. However, we see from the graph on the right that shipments originating from Fiji, Italy and Estonia have the lowest utilization rates. As such, we are unable to see a clear relationship between the shipper country and utilization rates. |
No clear relationship between consignee country and utilization rate |
![]() With the same X-axis and Y-axis, that is Consignee Country and Average Percentage Utilization, we realise that are no similar trends between the 2 graphs of different industries (auto industry and engineering industry).
|
Underutilization could possibly be due to danger of goods involved |
![]() We attempted to analyse if the danger level of the goods affected the choice of container.
We can see from the graph above that 5 out of 6 industries underutilize the containers when dangerous goods are involved. However, we also realized that there are only 142 records of dangerous goods available, as compared to 82,649 records of non-dangerous goods. Due to the vast difference in numbers, we are not able to say with certainty that the danger level of the goods affects the ultimate container choice. |
Breakdown of average utilization by Industries for FCL and LCL |
![]()
Out of the 6 industries, it becomes apparent that, in the order of lowest utilization of FCL are:
As such, we would suggest focusing on Engineering companies first. |
We also provide box plots to complement the cumulative distribution graphs so that the distribution can be better understood.
|
We also provide box plots to complement the cumulative distribution graphs so that the distribution can be better understood.
|
We also provide box plots to complement the cumulative distribution graphs so that the distribution can be better understood.
|