ANLY482 AY2017-18T2 Group27 : Project Findings
| Interim | Final |
Contents
8.0 Project Findings
Company X required 3 modules for their data visualisation: 1. Rate per Kg (RPK) 2. Density 3. Ship to Profile (STP)
8.1 RPK
EDA was performed in the following variables of RPK's module: Chargeable weight (WGT), Freight Charges (WTCHG) and Rate per Kg (RPK). Variable RPK is tabulated by dividing WTCHG by WGT.
WGT
We first looked at the variable chargeable weight generally. Chargeable weight (WGT) has a wide range of values from 0.1kg to 270000kg. Despite bringing this up, it was flagged as normal data by Company X. The wide range of data can possibly be attributed to the size of the shipment or contractual terms. Blank WGT entries were deemed as incorrect entry by ground staff or cancelled entries.
Subsequently, we looked at WGT across the GE BUs.
The large range of data is also seen across the regular BUs (with contract) and other non regular BUs (without contract).
In general, the shortest interval for 50% of the data is ends at the median or below the median. Moreover, the histogram is skewed to the right. This can be due to the few large outliers, visible in our box chart graphs below. The similar distribution pattern, across all BUs may suggest that there are huge shipments made by all BUs sometimes.
These huge range in chargeable weight of shipments may be due the nature of goods delivered. These BUs are from the manufacturing industry. Parts shipped may sometimes be as light as a small shipment of long term employment certificate or as heavy as a stack of legal documents, in the case of Corporate.
However, the chargeable weight for shipments by non-regular BUs seem to have a larger range of outliers than regular BUs. As non-regular shipment is generally costlier, this presents an opportunity for Company X's team to engage these non-regular BUs for a contract. This may interest the BUs as it is a cost-saving opportunity for them too.
WTCHG
WTCHG, total air freight for the shipment, appears to follow a similar distribution to WGT, where both regular and non-regular BUs’ mean is higher than its median. This means that their histogram is skewed to the right. This is as expected as air freight shares a positive relationship with chargeable weight.
Interesting to note, in WTCHG, Aviation is more than non-regular BUs, the opposite of the trend in WGT. This may be due to extra shipment during peak seasons or extra urgent shipment requested by Aviation.
RPK
In the overall summary statistic of RPK, those in the 0.05% percentile are between $404.33735/kg and $21986.66/kg.
Only 3 of the 180 transactions are dangerous goods. These dangerous goods are requested by Aviation, specifically Aircraft Engines. As they are from Aircraft Engines, these dangerous goods may be flammable. Their unusually high RPK can be attributed to the additional service or special aircraft provided by Company X to ship the dangerous goods.
The other 177 shipments are non dangerous goods. More investigation for details have to be conducted to reason this occurrence.
8.2 Density
EDA was performed in the following variables of Density's module: Volume of goods shipped in each shipment (VOL)
VOL
The box plot (not shown due to data privacy) reflects a rather distributed and varied field for Aviation customer as compared to the other customers. This is further supported when a closer look at the average volume being shipped is tabulated.
There is also a possibility of an outlier for this customer. A closer look at this (analysis on the proportion of customers) graph indicates that the dataset contains large transactional data relating to Aviation and Healthcare. Healthcare, when compared against the its respective box plot indicates a smaller box plot in comparison with Aviation. This shows that GE Healthcare does not ship heavy density shipments as compared to GE Aviation
To supplement this, we also considered the average volume by customer. From a bar chart of average volume (not shown due to data privacy), we note that there is a lot of higher range volumes which affects the average volume and skew per customer.
8.3 STP
EDA was performed in the following variables of STP's module: Chargeable weight (WGT), Freight Charges (WTCHG) and Rate per Kg (RPK).
BU Contracts
Firstly, we started by comparing the actual shipped weight against the total annual projected weight for each BU.
In general, we see that Healthcare has the highest contractual amount awarded and also, the highest actual amount shipped. It was not able to match up to its projected total amount.
On the other hand, Aviation, Energy Management, and Oil and Gas actual shipped amount exceeded the projected amount.
Considering these insights, the Company X can consider zooming in into these business units to further investigate which country and station lanes are causing these discrepancies.
Region Lanes
Another broad category to look at would be Region Lanes. Region Lanes track the origin region and the destination region. There are 3 main regions – ASPA, EMEA and AMER.
ASPA stands for Asia-Pacific, EMEA represents the European region while AMER represents the American continent.
Figure 15 highlights the actual shipped amount versus the Projected amount for each region lane. Here, we see that for Region Lane Pairs AMER > ASPA, ASPA >AMER, ASPA > ASPA, the actual amount shipped never matches up to the projected amount.
However, in the case of ASPA > EMEA, the actual amount shipped almost matched the projected amount. Additionally, for the region pair EMEA > ASPA, the actual amount shipped outweighed the projected amount.
Region Lanes and BU
Regions Lanes and Business Units are 2 broad categories. By combining these 2 filters, we will be able to gain more actionable insights as compared to looking at each aspect individually.
By using these 2 aspects as legends, the possible combinations have increase as each BU is paired with a Region Lane.
More details will be in our report due to data privacy concerns.
