06 · CASE STUDY · RENAULT

Predictive Vehicle Quality& Warranty Analytics.

Turning fragmented vehicle, incident and diagnostic data into governed defect intelligence for earlier risk detection and warranty decision support.

Mandate: lead the end-to-end Data / Product lifecycle for a predictive analytics application combining data modelling, governance, forecasting, operational BI and user workflows.

AutomotiveVehicle QualityData ProductProgram ManagementProject Management

Data ManagementData PipelineData GovernanceBusiness Intelligence

Predictive AnalyticsRisk ManagementAI / Machine LearningWarranty RiskISO/IEC 27001

Explore the architecture ↓View the user journey

Predictive vehicle-quality flow from governed data to warranty decisions

ROLE / MANDATEData Analytics Engineer · Product Owner · Project Manager

End-to-end contribution across data, product, analytics, application design and operational delivery.

SituationFragmented vehicle-quality evidence

Vehicle definitions, customer symptoms, diagnostic files, ECU context and corrective actions lived across heterogeneous systems.

Product directionPredictive quality intelligence

A governed analytical product designed to connect historical evidence with a forward-looking nine-month risk horizon.

Decision outcomeEarlier visibility. Better prioritisation.

Quality and warranty teams gained a structured path from fleet signals to defect investigation and corrective action.

01 · VEHICLE QUALITY CHALLENGE

Make fragmented field evidence decision-ready.

The challenge was not to produce another dashboard. It was to create a trustworthy analytical chain connecting the active fleet, customer symptoms, diagnostic evidence and corrective-action history.

From retrospective reporting to earlier quality-risk anticipation.

Vehicle-quality analysis depended on poorly structured, incomplete and redundant data across multiple operational systems. Source structures varied, documentation was inconsistent and relationships between a vehicle population, a customer symptom, a diagnostic code and a corrective action were difficult to trace.

The mandate combined data-product design and delivery: structure the domain model, establish governed definitions, integrate cross-source evidence, formalise analytical treatments and translate the result into a usable operational application.

Data complexity

Heterogeneous source structures

Vehicle, incident, diagnostic and engineering evidence required common keys, controlled mappings and consistent time logic.

Analytical complexity

Signal → contributor → defect

Users needed to move from a fleet-level symptom rate to product contributors, diagnostic codes and underlying cases.

Operational complexity

Insight had to lead to action

Forecasts and Pareto views had to support investigation, prioritisation, evidence export and corrective-action follow-up.

02 · PRODUCT VISION

One governed path from vehicle data to quality decisions.

The product vision connected four layers that had previously been treated separately: operational evidence, trusted integration, predictive analytics and decision workflows.

01Evidence

Vehicle & quality data

Fleet configuration, customer symptoms, diagnostic files, ECU context and corrective-action history.

02Foundation

Governed data model

Controlled perimeters, groupings, source relationships, calculation rules, traceability and shared definitions.

03Intelligence

Predictive analytics

Symptom and defect rates, contribution analysis, Pareto prioritisation and forward-looking risk signals.

04Action

Operational decisions

Drill-down investigation, defect alerts, corrective-action review and quality / warranty prioritisation.

03 · DATA ARCHITECTURE

A two-level story: executive clarity with technical depth.

The public portfolio view communicates the operating logic in five stages. The detailed technical reconstruction remains available as supporting evidence without exposing confidential source-system mappings.

Vehicle Quality Intelligence Architecture — portfolio overview — **Vehicle Quality Intelligence Architecture**Operational vehicle evidence moves through controlled integration and a trusted foundation into analytics, machine learning and business decisions.
Vehicle dataData integrationTrusted foundationAnalytics & MLBusiness decisions

04 · GOVERNED DATA PIPELINE

Traceable data. Predictive intelligence. Continuous improvement.

The governed pipeline connected operational evidence to controlled ingestion, a trusted data foundation, analytical processing and decision delivery—while preserving a continuous quality-feedback loop.

**Governed Vehicle Quality Data Pipeline**Operational evidence moves through controlled ingestion, trusted data,
processing and predictive analytics into quality, warranty and corrective-action decisions.
Operational evidenceControlled ingestionTrusted foundationProcessing & featuresAnalytics & MLDecision deliveryContinuous feedback

05 · OPERATING MODEL

Govern strategically. Deliver iteratively. Learn continuously.

The delivery model combined PMO steering, portfolio priorities and quality guardrails with integrated product leadership and a disciplined Scrum delivery cadence.

Hybrid PMO Governance and Scrum Framework — **Hybrid PMO Governance & Scrum Framework**Executive steering and decision guardrails governed the programme above, while integrated product leadership connected evidence, risks and learning to iterative Scrum delivery below.
Executive steeringPortfolio & budgetRisk & qualityProduct leadership
Scrum cadenceContinuous learning

06 · DATA MODEL & GOVERNANCE

Govern the meaning before scaling the analytics.

A credible forecast required more than ingestion. It required shared definitions for fleet perimeters, symptom groupings, product contributors, diagnostic evidence and measurement dates.

Data-product architecture,
not dashboard assembly.

The work established controlled entities and relationships across vehicle populations, incidents, symptoms, diagnostic files, ECU attributes, defects, analytical treatments and corrective-action references. That semantic foundation made every downstream rate, Pareto and forecast interpretable.

ScopeFleet & perimeter definitions

Consistent populations, countries, products and measurement periods.

SemanticsSymptoms & diagnostics

Controlled grouping logic and traceable relationships.

QualityRules & reconciliation

Normalisation, completeness checks and governed calculations.

AccessRoles & controlled use

Enterprise access and fit-for-purpose operational views.

07 · PREDICTIVE ANALYTICS

Turn historical quality evidence into a forward-looking risk signal.

The analytical layer combined interpretable quality metrics with contribution analysis and a nine-month forecasting horizon—giving operational users a defensible route from trend detection to investigation.

Monitor symptom rates

Track quality signals across selected fleets and perimeters, compare current performance with target or commitment levels and identify meaningful deviations.

Prioritise contributors

Use product-contribution and diagnostic-code Pareto analysis to focus investigation on the components and defects explaining the largest share of exposure.

Forecast the risk horizon

Extend monthly rate and product-mix evidence into a nine-month outlook designed to anticipate potential alerts and support earlier quality and warranty decisions.

08 · OPERATIONAL USER JOURNEY

From fleet selection to supporting evidence in six controlled steps.

The product experience translated complex analytical treatments into a progressive drill-down journey. Public labels and data have been anonymised while preserving the interaction logic.

Public vehicle-quality analytics user journey — **Vehicle Quality Analytics — Public User Journey**Select scope, monitor the symptom rate, identify contributors, prioritise diagnostic codes, review the trend and export supporting evidence.
Fleet scopeSymptom trendDiagnostic Pareto
Rate evolutionEvidence export

09 · CORRECTIVE-ACTION LOOP

Close the loop between detection, evidence and action.

Quality intelligence created value only when the analytical signal could be investigated, linked to engineering evidence and compared before and after corrective action.

01Detect

Identify an abnormal fleet or symptom-rate signal.

02Prioritise

Rank product contributors and diagnostic codes.

03Investigate

Review trends and supporting diagnostic cases.

04Act

Connect quality evidence to corrective-action decisions.

05Measure

Compare the rate before and after the action frontier.

10 · EXECUTION EVIDENCE

Documented product logic from data model to user workflow.

The case is supported by requirements, workshop material, conceptual modelling and operational navigation artefacts. Confidential implementation detail and unsupported quantified claims remain excluded.

Product

Functional scope & workflows

Defined perimeters, roles, questions, drill-down sequences, comments, exports and decision interactions.

Data

Conceptual model & relationships

Structured vehicle, symptom, diagnostic, ECU, treatment and corrective-action entities.

Analytics

Rates · Pareto · forecast

Specified calculation logic, contributor analysis, diagnostic-code prioritisation and nine-month projections.

Delivery

Operational application design

Translated analytical logic into an enterprise workflow for quality and warranty decision support.

Disclosure rule: selected vehicle-quality, diagnostic, warranty and internal architecture details are withheld due to NDA, safety and enterprise confidentiality. Visuals are public conceptual reconstructions using anonymised content.

11 · OUTCOME & LEADERSHIP VALUE

Earlier visibility. Evidence-led prioritisation. Stronger warranty decisions.

The programme connected governed data foundations, predictive analytics and an operational business application to support quality and warranty decisions. Reported outcomes combine cumulative cost reductions, data accuracy and KPI delivery with stronger control of major vehicle-defect risks.

Programme costsReduced

Contribution to cumulative cost reductions across the programme.

KPI delivery800+

KPIs delivered for operational analysis and decision support.

Data accuracy90%

Data accuracy achieved within the assessed scope.

Visibility

Earlier view of quality exposure

Fleet-level signals and future outlooks made emerging quality risks easier to identify and discuss.

Focus

Evidence-led defect prioritisation

Pareto analysis and drill-down paths concentrated attention on the most material contributors.

Decisions

Operational quality intelligence

Users could move from aggregate rates to diagnostic evidence and corrective-action context.

Planning

Warranty & parts decision support

The nine-month horizon strengthened the basis for proactive quality, warranty and parts-planning decisions.

OUTCOME PERSPECTIVE

The business value of predictive quality intelligence.

Four business focus areas, with business, technology and behavioural outcomes from the Renault predictive vehicle-quality case.
Business focusThe strategic objective	Business outcomesValue enabled	Technology outcomesCapabilities structured	Behavioural outcomesWorking practices enabled
Commercial GrowthIndirect contribution	Supported proactive warranty and parts planning through better anticipation of vehicle-quality exposure.	Structured nine-month projections using symptom-rate and product-mix evidence.	Aligned quality and warranty stakeholders around a shared risk horizon and prioritisation basis.
Operational Efficiency	Contributed to cumulative programme cost reductions, with a structured workflow for defect investigation and corrective-action follow-up.	Delivered 800+ KPIs, a new database, governed data pipelines, a business application and a predictive BI/ML platform.	Enabled teams to apply shared definitions and consistent analytical criteria when prioritising investigations.
Customer Value	Reported improvements in customer and business-user satisfaction, alongside stronger support for vehicle-quality decisions.	Connected fleet symptoms, diagnostic evidence, trends and corrective-action context through operational analytics and drill-down workflows.	Supported more informed investigations and coordination between business users, quality teams and technical stakeholders.
Risk Management	Strengthened control of major vehicle-defect risks. Supported GDPR readiness and information-security requirements aligned with ISO/IEC 27001 within the project scope.	Achieved 90% data accuracy within the assessed scope. Established data-quality controls, customer-data anonymisation, reconciliation, traceability and controlled enterprise access.	Supported decisions grounded in shared definitions, interpretable evidence and comparisons before and after corrective action.

Outcome basis: programme-level results reported by the programme lead. Cost reductions reflect collective programme performance; 90% refers to data accuracy within the assessed scope. Customer and business-user satisfaction improvements are qualitative.

ROI — Return on Investment: the cumulative cost reductions provide a financial benefit for assessing returns. A financial ROI requires attributable benefits, investment, operating costs and the period considered.

Value framing: commercial contribution is indirect, through quality, warranty and parts-planning decisions. GDPR readiness reflects preparation through customer-data anonymisation; ISO/IEC 27001 alignment concerns information-security requirements within the project scope.

Employee value and future potential

Complementary perspectives on the programme: employee benefits to document and strategic options to validate.

Employee benefits to document

ROE — Return on Employee

Better decision tools, shared data definitions and clearer investigation workflows support business users and quality teams.

Better decision toolsThe operational application and predictive BI capabilities give users a structured route from fleet-level KPIs to diagnostic evidence and corrective-action context.
Shared data definitionsGoverned fleet perimeters, symptom groupings and calculation rules provide a common analytical basis across business and technical teams.
Structured investigationsConsistent drill-down workflows and evidence comparisons support coordination when investigating defects and reviewing corrective actions.

Tools and working practices were established. Effects on time spent, autonomy, workload and employee experience remain to be documented.

Future options to validate

ROF — Return on the Future

Reusable data foundations and predictive capabilities create options for further development beyond the delivered application.

Additional analytical scopesThe governed database and pipelines provide foundations for extending analysis to additional vehicle scopes and datasets.
Further analytical servicesReusable data models, KPI definitions and predictive capabilities create options for additional quality, warranty and decision-support services.

The database, pipelines and application are delivered platform outcomes. Their ROF contribution depends on subsequent reuse, adoption and demonstrated economic value.

Predictive AnalyticsData Product LeadershipVehicle Quality IntelligenceData GovernanceData Modelling

Data PipelinesAI/Machine Learning

Business IntelligenceOperational AnalyticsProduct OwnershipWarranty Risk Management

LEADERSHIP VALUE

Industrial analytics becomes valuable when evidence, prediction and action operate as one product.

This case demonstrates the ability to connect a fragmented industrial data landscape to a governed analytical model, an operational user journey and a forward-looking decision capability—without losing traceability or interpretability.

Data ProductEnd-to-end lifecycle

Artificial IntelligenceMachine Learning

AnalyticsPredictive + operational BI

GovernanceDefinitions + traceability

Business valueQuality + warranty decisions

Continue exploring

Return to all case studies.

Product · Data · AI · Governance · Transformation

→