The Problem

Managing a fleet of vehicles means constantly balancing maintenance, cost, and operational reliability.

In many cases, vehicle data exists, but it’s scattered, difficult to interpret, or only becomes useful after something has already gone wrong.

This leads to reactive decision-making — where issues are addressed too late, resulting in breakdowns, delays, and unnecessary stress.

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, but to make quick, informed decisions based on system data. They need to understand what’s going wrong, how urgent it is, and what action to take — often under time pressure.

Without clear prioritization, even small issues can escalate into costly breakdowns.

The Solution

I designed Fleetora as a system that turns complex vehicle data into clear, actionable insights.

Instead of overwhelming users with raw metrics, the platform focuses on helping them quickly understand which vehicles are at risk and why.

The goal was to support proactive decision-making — allowing users to act early, rather than reacting to failures after they happen.

Key Concept - Risk Score

To simplify decision-making, I introduced a Fleet Risk Score that combines multiple vehicle metrics into a single, easy-to-understand value.

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

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

Dashboard

Provides a real-time overview of fleet health, highlighting high-risk vehicles and critical alerts.
Designed to help users quickly understand what requires immediate attention.

Check it out : https://tinyurl.com/fleetorasaas

Fleet List

Allows comparison across all vehicles using risk-based sorting and filtering.
Helps identify patterns and prioritize vehicles at scale.

Vehicle Details

Offers a detailed view of a vehicle’s condition through key health metrics and predictive insights.
Supports informed decision-making by explaining risk and recommended actions.

Alerts Log

Centralizes all active issues and organizes them by severity and status.
Enables users to track, review, and act on vehicle-related problems efficiently.

Service Planner

Visualizes upcoming maintenance tasks and service workload over time.
Helps users plan ahead and manage service capacity effectively.

Map

Displays vehicle locations with risk-based color indicators.
Provides geographic awareness to identify clusters of high-risk vehicles.

Reflection

This project helped me shift from designing individual screens to thinking in terms of systems and decision-making.

One of the biggest challenges was balancing complexity with clarity — deciding what information to show, what to simplify, and how to guide the user without overwhelming them.

It pushed me to focus less on visuals and more on how the product actually supports real-world workflows.

Overall, this project strengthened my ability to design data-heavy interfaces that feel clear, structured, and actionable.

From Structure to Interface

Early wireframes were used to define layout, hierarchy, and how information should be prioritized across the system.