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Revision as of 23:43, 15 April 2018
Contents
Project Background
Issues and Problems
With the expansion of E-Commerce in Singapore, there is a growing demand for the provision of effective logistical services to facilitate the delivery and receiving of goods and services to consumers.
As we analysed the entire customer experience journey in the e-commerce industry, we realised that there is a gap in the service delivery process in the event of a missed delivery. A study by the NUS Logistics Institute - Asia Pacific shows that as of 2016, the delivery failure rates in Singapore hover at more than 15%.
Traditionally, customers who missed a delivery from their logistics providers are redirected either to the post office to collect their parcels. They could also be required to make a call to their logistics providers and rescheduling for a redelivery, and at times required to pay an additional fee for the services. As for the logistics providers, carrying out redeliveries incurs additional operational costs in terms of man-hours and resources.
In our opinion, the last-mile delivery process should be improved such that consumers are not required to go through the additional hassle and incurring additional costs of collecting their missed parcels, and logistics providers can ensure that their customers get their parcels without any additional incurring of operational costs in carrying out redeliveries.
Motivation
Project Aim
Our team has explored the Self-Collection Points as a viable solution in addressing the issue of missed deliveries. We recognise the importance for Logistic Companies to be able to determine the location of their self-collection points, in order to maximise coverage as well as improving their last-mile delivery service experience for their customers.
Proposed Solution
Through the conceptualisation of our application - ParcFinder, we provide users with the tools to visualise the geographical accessibility and generate spatial analysis reports of their existing self-collection points. We hope to provide the necessary insights for our users in their decision-making process in the location of their self-collection points.
Our project will provide an application that will present to users the following insights and analyses:
Approach - GIS and Accessibility Models Used
Overview
ParcFinder is an application that uses various Accessibility Models and give the user different levels of understanding the geographical accessibility of the self-collection points. All the models are executed and have the following assumptions in common:
Level 1: Catchment Area Buffer Analysis
Objective of Analysis: To display and capture the number of residential locations that each point caters to, based on the specified buffer distance.
A Catchment Area Buffer of a self-collection point is defined as the vicinity of the point and its surroundings. It can be viewed as the base of which the residential locations have accessibility to. There are two types of catchment area buffers - Circular Area and Service Area. In the context of ParcFinder, it uses the circular buffer analysis of the Catchment Area Buffer Analysis.
There are two phases of the circular catchment area buffer analysis. Firstly, we set the radius of the buffer distance to determine the geographical catchment area of the self-collection point. The size of the buffer distance is dependent on the willingness to walk criteria of a consumer to the respective self-collection point in their catchment area. Secondly, we determine the number of residential locations that fall within the specified buffer distance, to determine the demand catered by each self-collection point.
The Catchment Area Buffer analysis is executed on the assumption that the self-collection service area is circular, and the collection point is centered on the residential locations.
Level 2: Hansen Potential Accessibility Model
Objective of Analysis: Each SCP is given an accessibility score to see how well it performs regarding its accessibility to the residential location.
Level 3: Kernel Density Estimation
Objective of Analysis: Looks at the macro level the areas in which the existing SCPs clusters and disperses to determine which areas are adequately served or underserved.
Project Proposal
Project Milestones
Project Storyboard
Project Task Allocation
S/N | Task | Done by | Week | Dates | Status |
1 | Topic Brainstorming | All | 2 & 3 | 15 Jan - 26 Jan | Completed ✔ |
2 | Drafting and refinement of Project Proposal | All | 2 & 3 | 15 Jan - 26 Jan | Completed ✔ |
3 | Consultation with Prof Kam for Feedback on Proposal | All | 3 | 22 Jan - 26 Jan | Completed ✔ |
4 | Finalisation of Project Topic and Focus | All | 3 | 26 Jan - 28 Jan | Completed ✔ |
5 | Compilation and Cleaning of Datasets (SingPost Post Office, POPStation, EzBuy) | Shu Yan & Zhi Hui | 4 - 6 | 29 Jan -18 Feb | Completed ✔ |
6 | Creation of Wiki Page | Aaron & Shu Yan | 5 - 6 | 5 Feb - 14 Feb | Completed ✔ |
7 | Generation of Storyboard | Aaron | 5 | 5 Feb - 9 Feb | Completed ✔ |
8 | Inddependent learning of R and R Shiny on DataCamp | All | 6 - 9 | 12 Feb - 9 March | Completed ✔ |
9 | Research on Tools for Data Conversion | Shu Yan & Zhi Hui | 6 - 7 | 12 Feb - 19 Feb | Completed ✔ |
10 | Wiki Content Update | Aaron | 7 | 19 Feb - 23 Feb | Completed ✔ |
11 | Preparation for Interim Presentation | Aaron and Shu Yan | 7 - 8 | 19 Feb - 2 Mar | Completed ✔ |
12 | Consolidation and complete conversion of data into SHP File | Shu Yan and Zhi Hui | 8 | 26 Feb - 4 March | Completed ✔ |
13 | Interim Presentation with Prof Kam | All | 9 | 6 Mar | Completed ✔ |
14 | Map and Interface Development Buffer Analysis: Shu Yan and Zhi Hui Hansen Potential Analysis: Zhi Hui Kernal Density Estimation: Shu Yan User Interface (Web Layout): Shu Yan User Interface (Description) : Aaron |
All | 9-13 | 5 Mar - 6 Apr | Completed ✔ |
15 | Debugging and Analysis of Results | All | 10-14 | 12 Mar - 10 Apr | Completed ✔ |
16 | Creating and Submission of Townhall Poster | Aaron | 13 | 2 Apr - 6 Apr Submission: 9 Apr |
Completed ✔ |
17 | Uploading of App on Shinyapps.io | Zhi Hui | 13 - 14 | 2 Apr - 15 Apr | Completed ✔ |
18 | Updating of Project Wiki Page | Aaron | 13 - 14 | 2 Apr - 15 Apr | Completed ✔ |
19 | Townhall Poster Presentation @ SLA | All | 14 | 11 Apr | Completed ✔ |
20 | Finalizing Wiki & Research Paper | All | 14 | 12 Apr - 15 Apr | Completed ✔ |
Data Sources
S/N |
Title |
Format |
Website Link / Sources |
1 |
Master Plan 2014 Planning Area |
SHP |
https://data.gov.sg/dataset/master-plan-2014-planning-area-boundary-web |
2 |
SingPost Post Office |
Unformatted |
|
3 |
SingPost POPStation |
Unformatted |
|
4 |
EzBuy |
Unformatted |
|
6 |
Residential Location |
csv |
Public Housing: https://www.ema.gov.sg/statistic.aspx?sta_sid=20150617kEhn53Jk6sDQ |
Case Study
We have selected the following Self-Collection models in generating our analysis from ParcFinder.
Case Study 1: Singapore Post POPStation
Case Study 2: EzBuy
ParcFinder Application
Results and Findings
Singapore Post
From the three levels of analyses, we can derive that there is much room for improvement for SingPost to improve the geographical accessibility of its post offices to residential locations. Coupled with the visualisation results from the KDE analysis, the areas in which SingPost should look into expanding the number of SingPost Post office should primarily be either in the West Area (Jurong East, Jurong West, Bukit Batok), and in the North Area (Woodlands, Yishun, Sembawang).
However, we also note that the cost of setting up and operating a new post office is high for SingPost. Therefore with this analysis, it supports the scope of our project, that to extend its last-mile delivery service efficiency, SingPost should look more into the expansion of its SCPs – POPStation, to enhance the overall accessibility of its services to consumers.
Singapore Post POPStation
From the three levels of analyses, we can derive that the extension of SingPost last mile delivery solution through POPStation has supported the improvement in the geographical accessibility to residential locations. Nevertheless, with the visualisation results both from the HPA and KDE Analyses, there are certainly areas of improvement regarding improving the accessibility scores of POPStations overall, especially those in the Western and Northern Regions of Singapore.
The Catchment area buffer analysis and the KDE analysis both suggest that there is currently an extensive coverage of POPStations SCPs in Singapore. However, as seen in the HPA Analysis in Figure 6, there is still the low accessibility scores of POPStations overall. One of the possible insights would be due to the insufficient coverage of transportation networks in these areas, which may have affected the overall performance of these POPStations. Hence, what SingPost can do regarding improving the accessibility scores of POPStations, would be to build more POPStations SCPs in these areas, preferably in locations that are nearer to the residential locations such as neighbourhood shopping centres and transport terminals. POPStations can also be opened in areas within private estates, to improve the overall accessibility coverage especially to private residential locations.
POPStations as an SCP is situated mostly in the central district, pertinently in the CBD areas, and in the center of various towns. One explanation for this could be that SingPost takes into consideration of the travel patterns of its consumers who utilise their service, where consumers would be travelling from their areas of work or school, rather than from their areas of residence.
EzBuy
From the three levels of analyses, we can derive that the EzBuy CDPs have the best performance between both SingPost Post Offices and POPStations in terms of its geographical accessibility to residential locations. With the extensive coverage that EzBuy provides, especially in the heartlands, it demonstrates the effectiveness of adopting CDPs in the Self-Collection model to improve efficiency in the last-mile delivery service. These findings are supported by all 3 levels of the analyses generated. With the HPA Analysis showing EzBuy Overall Accessibility performing better than average (Figure 11) and the probability density clusters from the KDE Analysis (Figure 12) showing a proportionate distribution of CDPs in Singapore. An area of improvement would be for EzBuy to extend its CDPs to the private estates like Bukit Timah, as these areas are where the accessibility scores and buffer distance coverage fare the lowest.
Challenges and Limitations
Challenges
Data Cleaning and Transformation
- Data retrieved not available in KML/SHP/XML format
- Team effort to convert to SHP format
- Documentation to keep track of changes.
- Data (longitude and latitude) not provided
- Write script to transform postal codes
- For postal codes that wasn't able to transform using the script, manually retrieve longitude and latitude from Google Map
No Prior Experience in Geospatial and R Programming
- Team effort to learn from Prof Kan and attend online classes from DataCamp
- Complete the hands on practice to familiar ourselves in the R environment
- Read relevant packages' documentations
Limitations
Due to the scope of the project, our team decided to focus on residential areas and did not include the rest, such as business offices/buildings. Distances were measured in straight line distance for Buffer Analysis and Hansen Potential Estimation.
Future Work
- Two-step floating catchment area method (2SFCAM)
- The 2SFCAM measure the spatial accessibility to each self-collection point, by calculating the ratio of the self-collection point to the residential population in that specified area,
- Upload dataset (SHP Files, XML format)
- Allow users to upload their data into the web application and perform the three layers of analysis provided
- Providing analyses of the geographical accessibility of existing datasets to different locations asides residential locations such as Office Locations, school locations etc.
- This will enable users to be able to interact with variuos datasets and obtain different analyses that suits to their needs.
- Slider for KDE Function
- Allow users to adjust the bandwidth for their analysis
- Select Color Scheme
- Allow users to change the color for HPA and KDE for comfortable viewing purpose
Meet the ParcFinder Team
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