It’s no longer fastest fingers first. With a new clustering algorithm, uParcel, an on demand 24/7 courier service in Singapore, has found a way to allocate jobs more fairly and efficiently to drivers while ensuring faster deliveries for customers.
uParcel is the largest home-grown same-day delivery company with an on-demand platform that supports a decentralised distributed model. This means that the company delivers packages point to point without consolidating them at warehouses. Instead, the uParcel mobile app matches delivery bookings to their network of crowdsourced drivers in real time, providing fast and flexible delivery options for the growing ecommerce sector.
Without the use of artificial intelligence (AI), the process for this involves posting the delivery jobs online. The drivers then choose the ones that they want to take on.
The disadvantage of this arrangement is that drivers have to manually search through the list of delivery orders on their app, and those with the fastest fingers will clinch the coveted jobs. This offers a less-than-ideal user experience and also means that multiple drivers could be taking different jobs with similar pickup and delivery points. The system was thus inefficient, with each driver operating way below his or her optimal capacity.
To boost driver efficiency, uParcel wanted to find a way to bundle jobs so that drivers can take on more jobs with similar pickup and delivery points and maximise their carrying capacity. But with its internal engineering team already stretched supporting its daily operations, it needed external help.
uParcel reached out to AI Singapore (AISG) to find out how it could tap on AI to achieve its goal. Working with the company in just 20-man days, the AISG team developed a clustering algorithm that groups pickup and destination points based on their proximity to each other. The algorithm is applied on a set of orders that contains the time and location of each pickup or delivery.
The optimised batching of pickups based on location has improved driver productivity by up to 20 percent.
Previously, there can be multiple collection points for drivers but they will have to manually consider if they are near each other and search through the jobs. With the new batching algorithm, it presents to them an optimally grouped batch of jobs that saves drivers precious time in considering the options. Multiple collection points within a 2km radius can be batched, enabling drivers to complete more jobs in a single trip. This increases their efficiency and income and at the same time, customer service receives a boost with faster pickup and delivery.
The AI solution, which is currently in beta testing stage, is set for a full launch in June.
“The collaboration with AISG has enabled us to turn data into actionable insights in record time,” said William.