#product
#web
#Figma
2025
Timeline
Product Designer
Role
Fleet managers struggled with:
• Fragmented tools forced ops to pivot between maps, tickets, and spreadsheets, slowing time‑critical responses
• Safety incidents lacked standard triage, causing inconsistent follow‑through and reporting gaps.
• Data density on city‑scale maps made it difficult to see what mattered now versus what could wait
The goal was to design a dashboard that not only tracks violations but also predicts risk levels and recommends actions.
I applied a human-centered design process to ensure the dashboard was not only data-rich but also actionable using the following:
A. Industry Analysis: Explored logistics and transportation safety trends, highlighting the increasing regulatory pressure and cost of non-compliance.
B. Competitive Benchmarking: Compared existing fleet management platforms, revealing dashboards that were often cluttered, non-intuitive, and reactive rather than predictive.
C. System & Data Review: Mapped violation logs, telematics data, and reports to understand patterns in driver behavior and recurring safety issues.
D. Stakeholder Priorities: Worked closely with internal business teams to define goals: improve safety, reduce violations, and provide predictive risk alerts.
E. Heuristic Evaluation: Reviewed current dashboard practices and pinpointed key usability pain points such as poor hierarchy, lack of data clarity, and weak visualization methods.
Understanding users
A. Fleet Manager: Needs to monitor risk, ensure compliance, and reduce costs.
B. Operations staff: Requires quick access to alerts and driver compliance status.
C. Executives: Care about fleet-wide trends, KPIs, and cost reduction.
Core Problem Identified
Fleet managers were overwhelmed by raw data but lacked the tools to translate it into clear, future-focused insights. The system needed to evolve from being reactive to predictive.
To balance monitoring and prediction, I structured the dashboard into two layers:
A. Monitoring Layer (Now)
• Key KPIs: Violations, Trips, Harsh Events, Fuel Efficiency
• Trend charts (incline/decline visualizations)
• Driver ranking list
B. Predictive Layer (Next 30 Days)
• Risk Level: Low / Medium / High (color-coded)
• AI-driven trend projection chart (expected violations)
• Recommended action (e.g., “Safety Coaching” or “Shift Adjustment”)
Design Goals
The insights shaped three guiding principles:
Solution Highlights
The resulting design introduced several innovations:
Impact
By transforming abstract data into intuitive, predictive insights, DriveIQ empowers organizations to:
Figma
Miro
Reflection
This project reinforced how UX + AI can unlock new value. Designing not just for monitoring, but for prediction and prevention, shifts the dashboard from a passive reporting tool into an active decision-support system.
