A scalable redesign of a logistics mobile app
Role - Product Designer
Team - 5 engineers
Project Timeline - Design 2.5 weeks & Engineering 2 months
Research-led redesign for a scalable mobile app for growth in last-mile logistics
AxleHire's mission is to bring Amazon Prime level delivery services to all businesses. It is one of the few services out there that packages both the technical and logistical portions of an efficient delivery service.
Problem
Growing pains of scalability made the workflow of AxleHire’s driver app unsustainable. We needed a solution that allowed a change in our overall architecture while maintaining our success metrics of OTD% (on-time delivery %).
Users
Drivers responsible for delivering our client’s packages. Their motivation lies within completing as many jobs as possible so efficiency is key.
Goal
Design and implement a more efficient, sustainable workflow to reduce the amount of repetitive actions required by users, cut the amount of time required to complete a job, and maintain our 97% OTD%.
Discovery
Every month hundreds of drivers deliver tens of thousands of packages. This milestone was achieved due to the original “Driver App”. Our service is now attracting large enterprise clients who need a vastly different solution.
Satisfying this requirement would allow us to land new contracts with large enterprise clients and increase our revenue by XX%.
Introducing new concepts into the system
The biggest challenge was implementing a conceptual model that would allow multiple shipments per delivery while maintaining our existing shipping system.
Incorporating the concept of “parcels” into the system, we are able to deliver multiple packages to a single customer. Each shipment to a customer can hold multiple parcels.
4 taps for 1 shipment → 4 taps for 10+ parcels
This change allowed drivers to maintain efficiency while improving their overall performance.
Nesting work flows
To make drivers more efficient, I grouped parcels at a single location into a single flow.
One location would have multiple shipments which could potentially have multiple parcels.
This allows them to drop off one building’s worth of parcels in as little as 5 taps where the original workflow would have made a separate location for each shipment and each parcel which had the potential to be up to 15 taps.
There was also the added benefit of sharing delivery information.
Scalable accuracy
Clients sign on with AxleHire expecting a 99% delivery success rate. We implemented QR scanning of parcels to ensure that customers receive the correct package.
Building confidence
After major app changes, we saw a spike in drivers deviating from suggested routes — primarily completing deliveries out of order. Despite reassurances, skepticism around our routing persisted. To rebuild trust, I designed a view showing the direct impact of user actions.
This allowed drivers to see the real-time impact of their actions, which helped maintain trust in our software, preserve service quality, and ultimately protect our OTD%.
Design system
To ease the workflow transition for drivers, I preserved familiar components and rigorously tested each iteration for clarity. I also built a component library to ensure consistency throughout the app.
Outcomes
Post-implementation analytics showed great improvement. After a short adjustment period, our core metric, on-time delivery percentage (OTD%), not only held strong but even rose to 98%. The solution proved highly scalable, enabling us to effectively support a growing client base. As a result, AxleHire (now rebranded as Jitsu) has sustained an industry-leading >99% on-time delivery rate.