Duhqa
Overview
Challenge
Problem: Nairobi is the biggest economic center in East Africa, growing rapidly but facing issues with road infrastructure. At the time of the project, many parts of the city had roads in poor condition or no roads at all. Local grocery shopping was usually done in small local shops that had major challenges in getting supplies. Duhqa wanted to solve this by having its own warehouse in an industrial zone and providing a last-mile delivery service through an on-demand delivery platform similar to Wolt, but focused on B2B customers only. They used an off-the-shelf Warehouse Management System (WMS) that did not fully work for their needs, and they did not yet have a solution for a customer ordering app. There was also a need to develop branding since it did not exist.
Constraints: With underdeveloped road infrastructure came challenges such as not having a detailed map of the city, unnamed streets, and missing address data. Since this was a startup with limited funding, I had to use an off-the-shelf design system.
Approach
Research: My project manager and I conducted field research over 7 days. We shadowed warehouse administrators and workers, spent time in delivery vehicles with drivers, and talked with customers. We also did competitive analysis.
Key insights: There were many things we learned that we could not assume without proper field research. There were many specific local contexts and needs to be covered. For example, we could not rely on packing lists from the producers because sometimes they were incorrect. Shop owners liked to do up selling during delivery; often they would see something in a delivery car they did not order and ask to buy it. Drivers often had to call customers in order to get the correct delivery location, and we noticed that these deliveries had fewer failed drop-offs. Through market research, we found that this specific niche was untapped and that we were pioneers in this market segment.
Design decisions: We decided to take into account all local context and try to align with known similar models like Uber, Wolt, and DoorDash. Many things had to be adapted, and new things had to be developed.
Solution
Final designs: Interesting solutions we made included allowing users to write descriptive instructions on how to reach their location, since GPS coordinates alone were not enough. We developed a system where drivers always carried extra popular products with them and could add items to orders on the spot for users.
Impact
Results:
Improved delivery success rate by reducing location ambiguity through descriptive address inputs.
Increased order flexibility by enabling drivers to handle on-the-spot product additions.
Reduced operational friction between warehouse, drivers, and customers through clearer workflows.
Improved reliability of deliveries in areas with poor infrastructure and incomplete mapping data.
Established a functional MVP system that supported both warehouse operations and last-mile delivery.
Lessons learned:
Field research was critical in uncovering constraints that would not be visible in desk research or assumptions.
Designing for infrastructure limitations required rethinking standard UX patterns used in Western logistics platforms.
Flexibility in the system (drivers, orders, delivery instructions) was more valuable than strict structure.
In emerging markets, logistics UX is heavily shaped by human coordination rather than purely digital workflows.