CASE STUDY
Designing an automotive quality dashboard that helps teams move from fleet-wide signals to the issues with the highest operational and financial impact.
01
CONTEXT
Automotive quality teams work across different signals: monitored vehicles, connected coverage, repair orders, warranty claims, diagnostic trouble codes, recurring topics, and emerging issues.
Each signal tells part of the story, but they do not carry the same business impact or urgency.
The dashboard needed to bring those signals into one operational view while making it clear which issues deserve attention first, what their potential impact is, and which patterns may be starting to emerge.
The challenge was not simply displaying vehicle-quality data, but turning it into a clear order of attention.
02
THE CHALLENGE
A frequently reported problem may affect many vehicles but have relatively limited impact.
Another issue may appear less often while carrying a higher predicted cost or broader predicted impact.
At the same time, quality teams need enough context to understand whether an issue is isolated, widespread, model-specific, or beginning to trend.
The dashboard therefore needed to answer several questions:
What is the scale of the monitored fleet?
Which known issues deserve attention first?
Where are repair orders and warranty claims concentrated?
Which diagnostic patterns appear across models?
Which signals are beginning to trend?
The design problem became one of turning a dense quality dataset into a clear hierarchy of attention.
03
RESEARCH
The research focused on content definition and information hierarchy.
01
What information is essential for the quality team?
Which metrics and signals need to remain visible in the main operational view?
02
How should quality issues be prioritized?
Which signals help distinguish a high-volume issue from one with meaningful operational or financial impact?
03
What supporting context explains the priority?
Which fleet, claims, diagnostic, and trend signals help determine whether an issue is widespread, model-specific, or emerging?
01
I worked with two Product Managers to define the information that needed to appear in the dashboard and clarify the business meaning behind the different quality metrics.
02
I spoke with an OEM Quality Engineer to understand how vehicle-quality information is evaluated in practice.
The conversation focused on what needs to be visible first, which information is secondary, and how the dashboard hierarchy should support prioritization.
01
Fleet size and connected coverage provide the context for interpreting the quality signals that follow.
02
Frequency matters, but predicted impact and cost help identify where attention is more valuable.
03
Known issues require prioritization, while rising patterns help expose what may become important next.
04
Repair orders and warranty claims become more useful when organized by vehicle system and time.
05
A DTC count becomes more meaningful when users can also see how it is distributed across vehicle models.
RESEARCH DIRECTION
FROM
A collection of quality metrics
TO
Scope → Priority → Operational context → Emerging signals
04
KEY PRODUCT DECISIONS
DECISION 01
PROBLEM
Quality metrics are difficult to interpret without knowing the size and coverage of the vehicle population behind them.
DECISION
Start with a compact overview of:
RATIONALE
The first layer gives users immediate context for both the monitored environment and the current quality exposure.
DECISION 02
PROBLEM
Claim count indicates how often an issue appears, but it does not necessarily indicate how important it is.
DECISION
Use a Top quality issues view that combines:
Rank the issues by predicted cost and classify their impact as:
Widespread · Significant · Moderate
RATIONALE
This lets users compare frequency with expected impact instead of treating the most common issue as automatically the most important.
DECISION 03
PROBLEM
Repair orders and warranty claims provide important context, but they answer a different question from the prioritized issue list.
DECISION
Give operational activity its own layer:
Repair orders grouped by vehicle system
and
Warranty claims grouped over time.
Keep severity visible through consistent stacked color coding.
RATIONALE
This shows where service activity is concentrated without allowing raw volume to compete with the primary prioritization model.
DECISION 04
PROBLEM
Current high-impact issues do not reveal everything the quality team needs to watch.
New problems can first appear as diagnostic patterns, rising claim counts, or repeated topics.
DECISION
Combine three complementary views:
DTCs show diagnostic distribution across models.
The watch list highlights signals whose claim counts are increasing.
Trending topics surface recurring quality themes across claims.
RATIONALE
Together, these views extend the dashboard beyond known problems and expose patterns that may require attention next.
05
THE EXPERIENCE
01ASSESS
The user begins with monitored and connected vehicle coverage, the number of priority issues, and their combined predicted cost.
This establishes the scale of the environment and the current quality exposure.
02PRIORITIZE
Top quality issues combines claim frequency, predicted impact, and predicted cost so attention can move beyond volume alone.
03CONTEXTUALIZE
Repair orders and warranty claims show how operational volume is distributed across vehicle systems and over time.
04DETECT
DTC distribution, rising watch-list signals, and trending topics expose patterns that may not yet appear among the highest-cost issues.
The Vehicle Quality Dashboard brings fleet coverage, quality issues, service activity, diagnostics, and emerging trends into one operational hierarchy.
It moves from understanding the monitored population, to prioritizing high-impact issues, to explaining the activity behind them, and finally to exposing signals that may require attention next.
Understand the fleet.
Prioritize the impact.
Watch what is emerging.