Case Study: "SwiftPath: The Mastermind of Efficient Deliveries"
Introduction
TheLorry is a leading technology-empowered logistics platform in South-East Asia offering a comprehensive and integrated marketplace model, they have several services, and one of it is supply chain solutions where they provide moving service from e-commerce stores to customers. With number of parcels to deliver each days, the company need a solution to seamlessly deliver parcel in a shorter distance and shorter time.
My Role
I oversaw designing, product and data analysis and planning. Working together with my product manager(Shindy), we planned a seamless user flow for mover to easily navigate with the route optimization. While data engineer worked on the geolocation datat collection, I worked with the frontend developer to present a better UI for the user to have a look and interact with.
Problem Statement
The delivery process for movers in TheLorry Online courier service app lacks an efficient and optimized route planning system. This results in suboptimal delivery routes, increased travel time and distance, and a potential decrease in overall customer satisfaction. Movers face challenges in manually determining the most efficient routes to deliver items, leading to inefficiencies, higher costs, and potential delays.
Goal
To empower movers to deliver items more effectively and efficiently by optimizing their delivery routes. By leveraging advanced algorithms and real-time data, the feature aims to streamline the delivery process, reduce time and cost, and enhance overall customer satisfaction.
Data Analysis
We analyse 50 drivers and took 5 drivers to come out a data. From the data below, we found out parcels to be delivered by mover in a day is not enough as they have to travel long distance, and from one point to one point, it took them longer time
User Flow
User Interface Design
As Courier App has been here in a while, as I venture into the User Interface after user flow, by adding a few button that shown easily in front of user and convenient to the fingertip, we added “Select End Point Address” so that user can choose an area that is nearest to their home by the end of their work day.
Key Component
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Optimize delivery routes: Develop an algorithm that analyzes multiple variables, including item destinations, traffic conditions, distance, and delivery time windows, to generate the most efficient delivery routes. Enable movers to easily access and follow these optimized routes within the app.
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Reduce travel time and distance: Minimize the total travel time and distance required to deliver all items in a given route. Consider factors such as traffic patterns, road conditions, and historical delivery data to ensure the generated routes are optimized for speed and efficiency.
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Consider delivery constraints: Incorporate business rules and constraints specific to the courier service, such as customer preferences, specific delivery time windows, and any special instructions for certain items. Ensure that the generated routes adhere to these constraints while still maximizing efficiency.
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Provide real-time updates: Enable movers to receive real-time updates and notifications about changes or updates to their optimized routes. This includes information about new delivery requests, traffic congestion, or any other factors that may impact the route plan.
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Enhance navigation support: Integrate the feature with reliable navigation services or maps to provide turn-by-turn directions and visual guidance for movers. This helps ensure they stay on track while following the optimized routes, reducing the chances of getting lost or making unnecessary detours.
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Enable flexibility and adjustments: Allow movers to make adjustments to the route plan as needed, such as adding or removing delivery stops, reordering stops based on urgency or proximity, or accommodating unforeseen circumstances. Provide an intuitive interface that enables easy modification of the optimized route.
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Track performance and metrics: Implement tracking and reporting capabilities to capture and analyze data related to route optimization. This includes metrics such as delivery time, distance traveled, fuel consumption, and cost savings. Use this data to evaluate the effectiveness of the feature and identify opportunities for further improvements.
Conclusion
In conclusion, embarking on this project has been an enlightening experience for me, as it has provided valuable insights into the user journey during delivery and the significance of geo-location. I am filled with satisfaction knowing that I have been able to contribute to the optimization of driver experiences by reducing travel time and fuel consumption, ultimately enabling them to complete their tasks more efficiently. This project has not only expanded my knowledge and understanding but has also allowed me to make a tangible impact within the realm of delivery logistics. Moving forward, I am excited to apply the lessons learned from this project to future endeavors, continuously striving to enhance processes and positively influence the experiences of both users and service providers.