The Problem

Managing a fleet means constantly balancing maintenance schedules, operational demands, and cost pressures.

Vehicle data often exists — but it's scattered, hard to interpret, and only surfaces when something has already gone wrong.

This forces fleet managers into a reactive mode: issues are caught too late, leading to unplanned downtime, operational disruptions, and avoidable costs. I wanted to explore how a system could surface the right information at the right time — helping users understand risk early and act before problems escalate.

The User

The primary user is a Fleet Operations Manager responsible for monitoring around 100–150 vehicles across multiple regions.

Their job isn't to repair vehicles — it's to make quick, informed decisions based on system data. They need to know what's going wrong, how urgent it is, and what action to take, often under time pressure.

They need a system that thinks in terms of priority and urgency — not just raw data.

The Solution

Fleetora translates complex vehicle telemetry into a clear hierarchy of risk — so managers always know which vehicle needs attention, why, and what to do next.

Rather than overwhelming users with raw metrics, the platform focuses on surfacing what matters most. The goal was to support proactive decision-making: helping users act early, rather than react to failures after they happen.

Key Concept - Risk Score

To simplify decision-making, I introduced a Fleet Risk Score — a single, actionable number that combines multiple vehicle metrics into one easy-to-read value.

This allows users to quickly prioritize vehicles without needing to interpret each individual metric.

Instead of asking "What does all this data mean?", the system answers "Which vehicle needs attention first?"

Dashboard

A real-time overview of fleet health, surfacing high-risk vehicles and critical alerts. At a glance, users know exactly where to focus.

Explore the live prototype here

Fleet List

Risk-based sorting and filtering across all vehicles, making it easy to identify patterns and prioritize at scale.

Vehicle Details

A deep-dive view into a single vehicle's condition — key health metrics, predictive insights, and recommended actions explained in context.

Alerts Log

All active issues, organized by severity and status — so users can track, triage, and act without losing anything.

Service Planner

Upcoming maintenance visualized over time, helping users plan ahead and manage service capacity before it becomes a bottleneck.

Map

Vehicle locations marked with risk-based color indicators — providing geographic awareness of where high-risk clusters are forming.

Reflection

Fleetora pushed me to think beyond individual screens and design a coherent decision-making system.

The central challenge was information hierarchy — knowing what to surface, what to abstract away, and how to guide users without overwhelming them. Working with data-dense interfaces meant constantly asking: does this add clarity, or just complexity?

This project deepened my approach to designing for real-world workflows. It's not enough for an interface to look structured — it has to help users think more clearly under pressure.

From Structure to Interface

Early wireframes were used to define layout, information hierarchy, and how data should be prioritized across the system — before any visual decisions were made.

User Journey

This journey maps how a fleet operations manager identifies high-risk vehicles and takes action to prevent potential failures — from the first alert to a scheduled service.

User Flow

This flow illustrates how Fleetora guides users from risk detection to maintenance scheduling through a structured, low-friction process.