Hiryuu Analysis
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| Data Preparation | Analysis |
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Contents
Exploratory
1. TAT across different countries
Although there are some datasets where the 90 percentile of the TAT is less than 3 days, there are some data sets where there were a huge proportion of failures.
One example is the dataset below which has a high value of 13 days for its TAT at the 90th percentile. This is an alarming number and should be flagged out for further in-depth analyis on the factors for failure.
2. Ending day of shipments
We have observed similar trends across various datasets in the failure rates for shipments ending on Monday and Tuesday.
An example of the distribution is shown below:
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For OB data, the reason for the higher failure rate might be the inavailability of customers over the weekends.
However, for IB data, there is no conclusive reason as of now, and we will be clarifying with our sponsors.
Dashboard Design
In this section, we will explain our application design of the dashboard we have created to best fit the shipment data given to us by our sponsor. In this project, we have used R Shiny to create a dashboard to integrate the different insights we have gathered.
R Shiny is a web application framework for R, which allows us to turn our analyses done in R into interactive web applications that can be hosted on a server for easy access by our sponsors. Our choice of using R Shiny is because of its ease of use, and flexibility in integrating different types of charts, as well as it being open-sourced and free. Compared to other commercial platforms available, R Shiny would serve to be a more sustainable platform for our sponsors to use for that it is free and that no web development skills are required, making it easier for them to make changes to fit their situation. An interactive application would best fit the needs of the sponsors, for the easy usage with controls fit to their specifications would suit the needs of the sales team.
The full dashboard consists of the main body and the sidebar. The sidebar consists of filters and the navigation tabs for the main body. The main body displays the different data visualizations available, such as the graphs, the data tables and the geospatial map. We will explain the 4 main parts to our dashboard below, namely, the Filters, Summary and Graphs, Data table and Geospatial Map.
Filters
To capture the flexibility in determining the start and ending points of a shipment, filters in the form of a check box have been created to allow our sponsors to set the start and end statuses according to their specifications to be taken into the calculation for the turnaround time. Additionally, an Inbound/Outbound (IB/OB) filter has been created to allow our sponsors to easily filter to those categories of shipments so that they can understand the situation of the shipments respectively.
Graphs
Summary - Understanding of the Overall Situation
On the summary tab, 2 graphs are created to show an overview of the statuses of all the shipments for each country. These graphs have been filtered to either Inbound or Outbound shipments for clearer understanding of the shipment progress.
Figure 8 shows the distribution of all the shipments with regards to its status being “Pass”, “Fail” or “Incomplete”. The term “Pass” is referred to as having a turnaround time lesser than or equal to the stated SLA in our project. On the other hand, “Fail” would refer to having a turnaround time exceeding the SLA, and “Incomplete” refers to a shipment having a starting point but without any ending points hence cannot have its turnaround time calculated. This graph would allow our sponsors to easily understand the overall situation in a particular country, and to easily delve into shipments which have failed or are incomplete.
Figure 9 shows the percentage of shipments which have “Passed” for each working week. The x-axis shows the starting date of each week included in the data. The SLA threshold level is taken from an input, which allows our sponsors to tweak and adjust to the value desired instead of taking a static value. This graph thus gives a time series breakdown of the situation in each country.
- Deeper Understanding of the Situation
The second tab of graphs provide a deeper understanding of the shipments in a specific country in providing more details on the shipment distribution performance.
Geospatial
Choropleth Plot
We performed a simple choropleth map based on the percentage rate of passes for both inbound and outbound shipments across both countries. Doing so allows us the user to easily point out problem areas and find out specifically the number of failed or successful shipments.
Neighbouring Polygons Patterns
We observed that when a city had a high Percentage Pass rate of shipments, the neighbouring cities around it tended to have a higher Percentage Passing rate as well. To investisgate this further and determine if there indeed is a spatial correlation between patterns, we utilised Moran I. This spatial measure was more prominent in Australia and the results showed that there indeed was a spatial correlation between cities and their Percentage Passes as the Mora I statistic is above 0 (0.209107260475405).
Possible explanations for the spatial correlation could be the transport mode, or the couriers assigned to handle these areas.
We decided to investigate for Japan's side as well. However, the limited time span of the data (3 months) ended up with all of Japan having a 100% passing rate. So the Moran I statistic returned a null value.
