Understanding existing drone solutions
Interviews surfaced weak visibility into performance metrics, making it difficult for personnel to judge efficiency and effectiveness.
Owigo
Drone Delivery Services
A drone delivery workflow concept focused on helping tech agents allocate parcels faster, track operations in real time, and work with more confidence in the field.
Owigo offers a software-assisted workflow for drone delivery personnel, helping teams coordinate routes, manage parcels, and stay ahead of operational changes.
Context
Owigo
Industry
Logistics
Tackling Operational Challenges
Owigo originated from the operational friction inside drone delivery workflows: route optimization, real-time tracking, and delivery coordination were all expensive to manage manually.
As drone delivery adoption grows, teams need a clearer system for assigning the right drone, monitoring live movement, and helping personnel respond faster in the field.
How Might We?
- Automate the allocation process for delivery requests while considering parcel weight, destination, and available drones.
- Create a real-time drone tracking system that shows estimated arrival times at delivery locations.
- Design a user-friendly way for personnel to monitor daily income, performance, and delivery progress.
Research
Interviews That Shaped Delivery Solutions
Primary research helped define the business context, clarify stakeholder expectations, and audit existing delivery tools. Those findings were synthesized into the insights that informed personas, flows, and feature direction.
Understanding the tech agents
Manual parcel management created unnecessary effort, delivery delays, and avoidable allocation errors.
Real-world immersion
Field context revealed how hard real-time tracking and communication can become during active drone deliveries.
Generating Diverse Solutions
Crazy 8s was used to quickly push beyond obvious answers and generate a broader range of concepts for parcel assignment, tracking, and task visibility.
The goal was not just cohesion in the workflow, but a better experience for both experienced personnel and newer team members learning the system.
Outcome
Concept to Beta
With a tight delivery schedule, the project moved quickly from concept exploration into high-fidelity screens, QA collaboration, and a beta-ready experience shaped by continuous stakeholder feedback.
Feature
Intuitive Dashboard
The dashboard surfaces daily, weekly, and monthly earnings, highlights bonuses and incentives, and keeps delivery personnel aware of completed, pending, and upcoming work.
Status changes and weather alerts are communicated instantly so teams can respond proactively instead of reactively.
Key Feature
Automated Allocation System
The allocation model prioritizes drones with the right payload capacity for each parcel while also factoring in availability and positioning, reducing unnecessary rework and improving dispatch efficiency.
Key Feature
Real-Time Tracking
Estimated arrival times adjust automatically based on delivery and weather conditions, giving personnel and customers a clearer sense of what to expect at every stage.
Timely status alerts support safer, more predictable delivery operations in the field.
Evaluation
Acquire. Retain. Thrive.
Owigo uses a parcel-based allocation model to help personnel select the most suitable drone based on weight, dimensions, and operational constraints, making assignment faster and more dependable.
Reflections
Learning Through Real-World Context
This project marked the culmination of an HCI learning journey and became a strong exercise in translating theory into a realistic operational product.
Real-world immersion sharpened feasibility
Immersion in the actual delivery environment exposed constraints that purely theoretical workflows would miss, helping the solution stay grounded and implementable.
A broader user range improved the design
Evaluating with a varied mix of personnel kept the product from overfitting to experienced users only and made the workflow more useful for interns and newer tech agents too.
Next Step
With more time, the next move would be deeper ride-along usability studies with personnel across experience levels, especially in live field conditions where weather, terrain, and device handling can reshape the experience.
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