Maptimization Details
Contents
Motivation and Problem Discovery
Motivation
With families in Singapore moving towards modern times where both parents or couple work full-time, domestic household chores are performed much less due to time-constraints. Jelivery Service(JS) Pte Ltd aims to alleviate this issue by providing convenient laundry washing services.
Problem
Currently, Jelivery Services operations function on delivering laundry to each customer's doorsteps regardless of distribution demand nor distance. This creates an issue where deliveries are sometimes time-inefficient and very fuel-consuming for its employees. This results a high operational cost on the company's expenses, making their profit margin very thin.
Proposed Solution
Our group, Maptimization, aims to optimize Jelivery Service's business process by streamlining it via the creation of single-best-point(s) for laundry collections/returns based on the layout of the area and zone. This would increase efficiency and ultimately improve customer satisfaction by providing the best convenience to them possible. Through the use of K-means clustering and sufficient data points, we would be able to create an optimization model.
Project Sketch
First sketch
Our first sketch sets a direction of what we would like users to be able to see and utilize in our final application. As seen above, we would like to provide three features to our users
1. Filtering by Region(North, South, East, West): This would allow users of different regions to only select laundry collection points near their regions. This is to facilitate convenience and disallow "noise(undesired)" collection points from showing up in their interface
2. Filter by Distance: This means that each collection point can only go up to a certain range from them(e.g. 1KM away from them). The application would recommend how many collection points are within the selected range
3. Filter by K-Clusters: This would allow users to know how many collection points are clustering together, as each collection point is catered to 10 household collection point. If a cluster is more dense, he will more likely be able to collect his laundry easier.
Final sketch
Our final sketch shows an in-depth view of what we would expect the application to look and feel like. Through the selection of options on the top right of our application, users will be able to select Jelivery Service laundry collection points to their own convenience.
For illustration purposes, we have shown that a user have selected the "North" Region and a K-Cluster of 3. The application shows that 3 K-Clusters are shown to the user. The user will then be able to make their way down to whichever is the nearest collection point or whichever is the densest cluster to be served quicker.
Technologies used
Technologies:
- R Shiny
- R Studio
- R Language
- OMS
- Excel
Challenges & Limitations
Data
- There was lack of sufficient data available for Toa Payoh
- Datasets found are often incomplete or had missing fields
Data cleaning and transformation
- Addresses found often had invalid postal codes or unknown postal codes, which required research to amend it
- Data transformation to respective regions(North/South/East/West) was resource-draining and tedious
Novel technologies
- Steep learning curve
- A lot of consultations were needed with Prof Kam
- Self-learning
