To respect my non-disclosure agreement, certain details in this case study have been simplified, modified, or omitted. This case study focuses on my design process, problem-solving approach, decision-making, and contributions, while ensuring that no confidential product information is disclosed.

Optimizing Software & Production Visibility for Renault Fleet Tracker

Fleet Tracker is Renault’s proprietary B2B analytics platform transforms fragmented fleet data into real-time insights, helping Renault teams move from manual reporting to enabling proactive decisions and reducing reporting time by 85%.

Client:

Renault Group

Category:

B2B - Web Application

My Role:

Product Designer

Overview

Fleet Tracker is designed to manage large-scale vehicle deployments. While traditional telematics focus on GPS and fuel, Fleet Tracker manages the technical lifecycle of the vehicle.

This allows Renault teams to monitor production status and software version distribution across thousands of assets, transforming raw telemetry into actionable maintenance and deployment strategies.

Problem Statement

There was no unified system to track fleet production and software status. Fleet teams relied on fragmented data sources and manual workflows to answer operational questions, leading to delayed insights, increased risk of errors, and inefficient decision-making.

To answer a simple question: "How many vehicles in Zone A are running the outdated v2.4 software?" - managers had to:

Export raw CSVs from three different internal databases.
Manually pivot data in Excel to find correlations between production dates and software branches.
Cross-reference manual logs to identify which vehicles were "Pre-production" vs. "Live."

Users:

Fleet Operations Managers
Release Engineers
Maintenance Leads

The Business Risk

This manual process took 4–6 hours per report, leading to delayed software patches, mismatched hardware-software configurations, and increased downtime for critical fleet assets.

Research & Insights

I conducted a 2-week discovery phase involving stakeholder interviews and shadow sessions with the France and Indian based operations team.

I researched market analysis and the market leaders like Samsara, Geotab, and Verizon Connect dominate the logistics and driver-behavior space, they operate as "black boxes" regarding the OEM-specific technical stack.

They lack visibility into:

Production Stages: Identifying issues specific to pre-production prototypes vs. consumer-ready units.
Software Branching: Distinguishing between BVA firmware versions and infotainment OS branches.

Goal:

Position Fleet Tracker as the only tool providing OEM-level granularity, moving beyond simple GPS tracking into "Digital Twin" management.

Strategy & Approach

This problem wasn’t about lack of data, it was about lack of clear decisions. My approach focused on transforming Fleet Tracker from a data tool → decision-support system.

Transforming Data into Decisions:

Focused on highlighting deviations and risks, not just displaying data.
Designed for quick understanding, enabling users to identify issues in seconds.
Allowed users to filter, drill down, and investigate without breaking their flow.
Structured the experience from fleet-level insights → vehicle-level details.
Introduced clarity around data freshness and reliability.

Data Visualization Design:

Visualization over Tabulation: Used stacked bar and donut charts for quick, at-a-glance insights
Progressive Disclosure: From high-level overview → deeper drill-down without losing context
Live-Sync Interactions: Real-time filter updates to support seamless exploration

The Possible Solution

1. The Health Command Center

A high-density dashboard featuring two primary modules:

Production Lifecycle Tracker
Software Version Distribution

2. Dynamic "Global Search" Filtering

A persistent filter bar allows users to slice the entire dashboard by:

VIN Range / Batch ID
Geographical Zone
Hardware Revision

3. Deep Drill-Down Interactions

Clicking a "Critical Outdated" slice in a pie chart doesn't just zoom in, it generates a filtered Action List below the fold, pre-populated with the VINs, current location, and the last contact time for those specific vehicles.

4. Edge Case Handling

The "Data Gap": If a vehicle hasn't "phoned home" in 72 hours, it's flagged with a "Stale Data" icon rather than being omitted, preventing false positives in health reports.

Massive Scale: Used "Canvas" rendering for graphs to ensure smooth interactions even when visualizing 100,000+ data points.

Design Approach

I used an AI-augmented workflow to quickly explore dashboard layouts and data visualizations, then refined them based on product needs, usability, and stakeholder feedback. The final UI follows Renault’s design system for consistency and scalability.

Note:

These screens are recreated for this case study. Due to NDA constraints, the original production designs cannot be shared, but the structure and flows reflect the actual product experience.

Impact & Metrics

After testing the design for two month with a selected group of large-scale fleet teams, we observed:

85% Reduction in Reporting Time: Managers moved from 6 hours of manual work to under 45 minutes of dashboard-driven analysis.
40% Faster Issue Resolution: Release Engineers identified "stuck" software versions 2 days earlier on average.
Estimated operational savings through reduced unnecessary technician call-outs by accurately identifying software-fixable issues remotely.

Challenges & Resolutions

Challenge:

Large datasets made dashboards overwhelming and hard to scan.
Multiple filters created confusion and unexpected empty states.
Heavy data caused lag in interactions and visualizations.
Vehicle data from multiple sources often showed conflicting states, reducing trust in insights.
Data delays and offline vehicles made real-time insights unreliable.

Solutions:

Focused on key metrics and used progressive disclosure for deeper insights.
Made filters visible, reversible, and provided contextual feedback.
Used aggregation and optimized data loading strategies.
Aligned data states and introduced clear “last updated” indicators to improve transparency.
Designed data freshness cues and status indicators to communicate accuracy.

Key Learnings

B2B is about Efficiency, not Just Beauty: In an enterprise tool, a user's "Time on Task" should be low. The goal is to get them out of the app and back to managing their fleet.

Designing a dashboard requires deep knowledge of the data schema. If you don't understand the logic, you can't design a meaningful filter for it.

Conclusion:

By moving Renault from Data Collection to Insight Generation, the Fleet Health Dashboard didn't just improve a UI, it modernized a business process. It transformed Fleet Tracker from a simple tracking utility into a strategic asset for Renault’s B2B partners, ensuring that the next generation of software-defined vehicles is managed with precision.

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