Difference between revisions of "ParcFinder Proposal"

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===Application Architecture ===
 
===Application Architecture ===
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===Application Overview ===
 
===Application Overview ===

Revision as of 04:18, 11 April 2018

PARCFINDER logo .png

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PROJECT DETAILS

POSTER

APPLICATION

RESEARCH PAPER


Project Background

Customer experience journey
Flowchart of Traditional SingPost Delivery Service in E-Commerce

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 2017, 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.



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

Customer experience journey
POPStation, a 24/7 self-collection point from SingPost

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:

  • The number of residential locations that each self-collection point caters to within a specified buffer distance
  • The accessibility scores of the respective self-collection points
  • The heatmaps to visualise hotspots of self-collection points and determining areas that are underserved
  • Approach - GIS and Accessibility Models Used

    ParcFinder is an application that uses various GIS and Accessibility Models and give the user different levels of understanding the geographical accessibility of the self-collection points. These models are as follows, based on the level of analysis and insights each method brings:

    Level 1: Catchment Area Buffer Analysis

    Level 2: Hansen Potential Accessibility Model

    Level 3: Kernel Density Estimation

    Level 4: Two-Step Floating Catchment Area Method (For Future Works)

    Data Source

    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

    https://www.singpost.com/list-of-post-offices

    3

    SingPost POPStation

    Unformatted

    https://www.mypopstation.com/locations

    4

    EzBuy

    Unformatted

    https://ezbuy.sg/Help/QuickGuide#Delivery

    6

    Residential Location

    csv

    Public Housing: https://www.ema.gov.sg/statistic.aspx?sta_sid=20150617kEhn53Jk6sDQ
    Private Housing: https://www.ema.gov.sg/statistic.aspx?sta_sid=20150209DnSuIwVsNHBY

    Project Proposal

    Project Milestones


    ParcFinder Milestones.png


    Project Storyboard


    Storyboard ParcFinder .png


    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
    Hansen Analysis: Zhi Hui
    KDE: Aaron and Shu Yan
    Upload Dataset: 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 In Progress
    19 Townhall Poster Presentation @ SLA All 14 11 Apr In Progress
    20 Finalizing Wiki & Research Paper All 14 12 Apr - 15 Apr In Progress


    Case Study


    Case Study 1: Singapore Post POPStation


    Case Study 2: EzBuy, bluPort and NinjaCollect



    ParcFinder Application

    Application Architecture

    System .jpg

    Application Overview


    Application Guide


    Results and Findings



    Challenges and Limitations

    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


    Future Work


    Meet the ParcFinder Team

    Aaron Ching Kwun Hin
    Chien Shu Yan
    Lee Zhi Hui


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