Hiryuu Methodology

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Background		Data		Methodology

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 4 main techniques, Exploratory, Clustering, 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

There are 5 main objectives we aim to achieve:

1. Understanding the patterns and trends across shipment routes in different countries
2. Identify patterns such as the locations and timing for shipments with frequent issues
3. Identify possible clusters based on types of shipments or customers to easily classify shipments
4. Conducting time series analysis to determine the presence of seasonality in shipments
5. Building a dashboard for single view of all data statistics and to understand 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
• Testing Iteration of Application

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

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

Shipping patterns and behaviour can be identified using geospatial analysis. The analysis will be narrowed down to the country, state/city and postal code. We will seek to answer the following questions:

• Where different customers lie on the map and hopefully identify the more popular areas and their reasons
• How different locations and proximity to the warehouses can affect shipment time and procedures.
• Identify and flag out destinations with high probability of shipment issues.
• Track different shipping routes from the start to the final to determine the average time required.
• Track different shipment status gap to determine partner’s performance in data provision/updates

3. Clustering

We plan to cluster our data based on type of customer, shipping history, activity level and any other potential classifications which we may identify in the future. Each customer/vendor will then be assigned a cluster number.

4. 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.