Difference between revisions of "ANLY482 AY2017-18 T2 Group 22"

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DHL is one of the largest logistics and transportation companies in the world, providing international express, freight transportation, e-commerce and supply chain management services to over 220 countries and territories worldwide, and employing approximately 510,000 employees. In the fiscal year of 2016, DHL’s revenue was over 57 billion euros. A key part of DHL’s business relies on the success of its freight transportation, delivering shipment via various modes of transportation through the air, ocean and road.
 
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This project aims to study into a critical factor behind DHL’s success as a freight transportation service provider: the price of its corporate shipping contracts. Contract bidding refers to the process of DHL submitting a tender to secure a corporate contract in order to provide its freight services to a company. To secure its shipping contracts, DHL has to go through several bidding rounds with competitors, which include major players such as UPS, Fedex and other companies in the same region. By discovering insights into the factors affecting the bidding price for its shipping contracts, this better allows DHL to understand the thought process and motivation behind the bidding results of its customers, across various industries as well as different countries, and potentially better prepares them for negotiation with their customers for future contract bidding rounds by offering better terms which are of greater concern specifically for different types of customers, thus enabling them to have a higher chance of earning contracts and reaping more profits.
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Kiva is an online crowdfunding platform which extends financial services in the form of loans to the poor and financially excluded people around the world, who are otherwise unable to raise funds from financial institutions and banks given their financial capacity and background. Since its inception, Kiva lenders have provided over $1 billion USD in loans to over 2 million people. This project provides a journey of starts with an overview of the business and the motivations and activities for people in Philippines taking up these loans, followed by kernel density analysis to observe how the intensity of spatial point patterns differ across the different islands and provinces, and lastly the implementation of Exploratory Spatial Data Analysis using measures of spatial autocorrelation and Local Indicators of Spatial Association (LISA) to gain insights into the impact of neighbouring areas and presence of clusters, by using Queen’s case contiguity-based weights, and 2 distance-based weighting methods, namely the K-Nearest Neighbour and the Inverse Distance Weighting.
 
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Latest revision as of 16:37, 15 April 2018



Home

 

Project Overview

 

Project Findings

 

Project Management

 

Documentation

 

About Us

 

ANLY482 Main Page


Project Description
Kivalogo.png

Kiva is an online crowdfunding platform which extends financial services in the form of loans to the poor and financially excluded people around the world, who are otherwise unable to raise funds from financial institutions and banks given their financial capacity and background. Since its inception, Kiva lenders have provided over $1 billion USD in loans to over 2 million people. This project provides a journey of starts with an overview of the business and the motivations and activities for people in Philippines taking up these loans, followed by kernel density analysis to observe how the intensity of spatial point patterns differ across the different islands and provinces, and lastly the implementation of Exploratory Spatial Data Analysis using measures of spatial autocorrelation and Local Indicators of Spatial Association (LISA) to gain insights into the impact of neighbouring areas and presence of clusters, by using Queen’s case contiguity-based weights, and 2 distance-based weighting methods, namely the K-Nearest Neighbour and the Inverse Distance Weighting.

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