Difference between revisions of "Hiryuu Methodology"
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==<div style="background: #95A5A6; line-height: 0.3em; font-family:Roboto; border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#ffffff"><strong>Introduction</strong></font></div></div>== | ==<div style="background: #95A5A6; line-height: 0.3em; font-family:Roboto; border-left: #6C7A89 solid 15px;"><div style="border-left: #FFFFFF solid 5px; padding:15px;font-size:15px;"><font color= "#ffffff"><strong>Introduction</strong></font></div></div>== | ||
| − | <p>The main aim of this practicum is to give our sponsor an insight into the delivery patterns in the different countries managed, focusing on Australia and Japan as these 2 countries have posed the most problems. To do so we will first analyse the trends from 3 months worth of data using | + | <p>The main aim of this practicum is to give our sponsor an insight into the delivery patterns in the different countries managed, focusing on Australia and Japan as these 2 countries have posed the most problems. To do so we will first analyse the trends from 3 months worth of data using 3 main techniques, Exploratory, Time Series, and Geospatial. </p> |
<p>With these analysis done, we hope to give our sponsors a clearer picture as to the reasons of failed deliveries so that it will aid the company in avoiding similar pitfalls in the future. </p> | <p>With these analysis done, we hope to give our sponsors a clearer picture as to the reasons of failed deliveries so that it will aid the company in avoiding similar pitfalls in the future. </p> | ||
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# Understanding the patterns and trends across shipment routes in different countries | # Understanding the patterns and trends across shipment routes in different countries | ||
# Identify patterns such as the locations and timing for shipments with frequent issues | # Identify patterns such as the locations and timing for shipments with frequent issues | ||
| − | |||
# Conducting time series analysis to determine the presence of seasonality in shipments | # Conducting time series analysis to determine the presence of seasonality in shipments | ||
| − | # | + | # Build a dashboard for single view of all data statistics for a particular country that can measure KPI easily. The sponsors we’re working with are focused on the marketing simplicity and efficiency only. The functions we hope to show includes the following: |
<br>Design Specification | <br>Design Specification | ||
* Showing data records of parcels picked up but not replied | * Showing data records of parcels picked up but not replied | ||
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* View failed deliveries at a single glance and detailed breakdown at a single click, including track by reference number for both inbound and outbound | * View failed deliveries at a single glance and detailed breakdown at a single click, including track by reference number for both inbound and outbound | ||
* Peak of the failure points when time series analysis | * Peak of the failure points when time series analysis | ||
| − | * Simple to understand bar charts and histograms | + | * Simple to understand bar charts and histograms that represents KPI |
| − | |||
<br>Multiple iterations of the dashboard will be conducted to increase the usability for our sponsor. We will conduct frequent feedbacks with our supervisor and sponsor to ensure that the dashboard is equipped with the data statistics and KPI most readily useful for the decision making. </p> | <br>Multiple iterations of the dashboard will be conducted to increase the usability for our sponsor. We will conduct frequent feedbacks with our supervisor and sponsor to ensure that the dashboard is equipped with the data statistics and KPI most readily useful for the decision making. </p> | ||
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<p><h3>2. Geospatial Analysis</h3></p> | <p><h3>2. Geospatial Analysis</h3></p> | ||
| − | + | To align with the sponsor's requirements, we will do a choropleth mapping for both inbound and outbound shipments in both countries, Australia and Japan. The choropleth mapping will reflect the percentage of passes in each city. Each city on the map will also display further information such as: | |
| − | + | * The TOTAL number of shipments to that city | |
| − | * | + | * Number of shipments that PASSED |
| − | * | + | * Number of shipments that FAILED |
| − | * | + | * City and State information |
| − | * | ||
| − | <p><h3>3 | + | <p><h3>3. Time Series Analysis</h3></p> |
| − | |||
| − | |||
| − | |||
As the data could be organised by the date, a time series analysis could be conducted. The time series analysis would be broken down into time periods of weeks and month to analyse and identify patterns and trends in the shipment and customer data. | As the data could be organised by the date, a time series analysis could be conducted. The time series analysis would be broken down into time periods of weeks and month to analyse and identify patterns and trends in the shipment and customer data. | ||
<p>We will also attempt to determine if there are seasonality trends in shipment patterns across different countries for different shipments.</p> | <p>We will also attempt to determine if there are seasonality trends in shipment patterns across different countries for different shipments.</p> | ||
<!--------------- Body End ----------------------> | <!--------------- Body End ----------------------> | ||
Revision as of 18:36, 22 April 2017
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| Background | Data | Methodology |
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Contents
Introduction
Introduction
The main aim of this practicum is to give our sponsor an insight into the delivery patterns in the different countries managed, focusing on Australia and Japan as these 2 countries have posed the most problems. To do so we will first analyse the trends from 3 months worth of data using 3 main techniques, Exploratory, Time Series, and Geospatial.
With these analysis done, we hope to give our sponsors a clearer picture as to the reasons of failed deliveries so that it will aid the company in avoiding similar pitfalls in the future.
Objectives
Objectives
There are 5 main objectives we aim to achieve:
- Understanding the patterns and trends across shipment routes in different countries
- Identify patterns such as the locations and timing for shipments with frequent issues
- Conducting time series analysis to determine the presence of seasonality in shipments
- Build a dashboard for single view of all data statistics for a particular country that can measure KPI easily. The sponsors we’re working with are focused on the marketing simplicity and efficiency only. The functions we hope to show includes the following:
Design Specification
- Showing data records of parcels picked up but not replied
- Show visual summary of shipments and current status
- View failed deliveries at a single glance and detailed breakdown at a single click, including track by reference number for both inbound and outbound
- Peak of the failure points when time series analysis
- Simple to understand bar charts and histograms that represents KPI
Multiple iterations of the dashboard will be conducted to increase the usability for our sponsor. We will conduct frequent feedbacks with our supervisor and sponsor to ensure that the dashboard is equipped with the data statistics and KPI most readily useful for the decision making.
Analysis
Analysis
1. Exploratory Analysis
An exploratory analysis will be conducted first to analyse the shipping behaviour of different customers in different countries.
- Determine the average turnaround time from the first to the last stage.
- Determine the average turnaround time for the statuses closure
- Identify patterns between destinations and shipment issues.
- Identify types of shipments with frequent shipment issues.
2. Geospatial Analysis
To align with the sponsor's requirements, we will do a choropleth mapping for both inbound and outbound shipments in both countries, Australia and Japan. The choropleth mapping will reflect the percentage of passes in each city. Each city on the map will also display further information such as:
- The TOTAL number of shipments to that city
- Number of shipments that PASSED
- Number of shipments that FAILED
- City and State information
3. Time Series Analysis
As the data could be organised by the date, a time series analysis could be conducted. The time series analysis would be broken down into time periods of weeks and month to analyse and identify patterns and trends in the shipment and customer data.
We will also attempt to determine if there are seasonality trends in shipment patterns across different countries for different shipments.
