When Complexity Becomes a Liability

Before our redesign, the CPV experience inside ValGenesis was a landscape of fragmented screens, dense statistical outputs, and workflows that grew organically over years of incremental development.

• Quality SMEs — some of the sharpest domain experts in pharma manufacturing — were wrestling with:
• CPV plans scattered across multiple views
• Statistical charts that required deep interpretation
• Batches and variables buried under non-intuitive tables
• Slow, error-prone configuration steps
• A lack of cohesive information architecture

Compliance-critical actions (like electronic signatures) hidden inside inconsistent flows

In a regulated environment where deviations can have real consequences, complexity is not just inconvenient; it’s a risk.

My mandate as UX Lead was not simply to redesign screens.

It was to create a unified, navigable, reliable system that supports scientific decisions, regulatory compliance, and operational confidence — all inside the Next Generation Platform (NGP) ecosystem.

This required deep system thinking, careful orchestration, and a scientific UX mindset.

Entering the Problem Space, Understanding the System Before Changing It

I began by leading multiple discovery cycles:

• On-site sessions with Quality SMEs and Validation Engineers

         Understanding their mental models of reference batches, variability, PCA, CpK thresholds, and deviation detection.

• Stakeholder interviews

       From product managers to statistical SMEs to regulatory experts, to uncover what “trust” means in a CPV system.

• Heuristic evaluation of the legacy CPV workflow

       Identifying mismatches between user expectations and the interface — especially around dense data sets and statistical visualisation.

• Archetype development

       Mapping precise behavioral patterns across SMEs, reviewers, and approvers to drive role-based UX decisions.

       Considering these conversations, a clear problem emerged:

The CPV workflow produced insights, but the system did not help users understand, validate, or trust them.

The information architecture wasn’t supporting the scientific reasoning SMEs needed.

The tool executed steps — but it didn’t explain them.

And navigation patterns didn’t reflect the mental model of CPV as a cyclical, decision-driven process.

This gave us the direction:

Redesign CPV as a guided, intelligible, and reliable end-to-end system , not as a collection of isolated screens.

Orchestrating Alignment, Turning a Complex Domain Into Shared Understanding

Leading this redesign required more than UX craft.

It required orchestration across teams, personas, and constraints.

I guided the team through:

Cross-functional workshops

• Mapping the CPV lifecycle: plan creation → reference batch selection → variability assessment → limit configuration → routine monitoring → reporting.
• Identifying breakpoints where users typically lost context or confidence.
• Defining system boundaries for what belongs to NGP standards vs. where CPV needs domain-specific adaptations.

Pattern alignment sessions with developers

• Converting complex statistical logic into reusable visual patterns.
• Establishing loading, threshold, and alert patterns that could scale across variables and plans.
• Ensuring feasibility within legacy architecture while raising the overall UX maturity.

Guiding junior designers

• Providing the design system’s structural rules.
• Defining the interaction logic and directional patterns.
• Delegating components while keeping the system cohesive.

This orchestration ensured that everyone — designers, developers, SMEs — shared the same mental model of the CPV system.

System Thinking, Re-architecting CPV Into a Cohesive Experience

The core of this project was not UI aesthetics.

It was system architecture — reorganizing a scientifically complex workflow into a coherent, guided experience.

The Turning Point: The 6-Step CPV Configuration Wizard

The old CPV creation process was scattered and unintuitive.

So I redesigned it into a structured 6-step flow that reflects real SME decision-making:

1. Define plan metadata

2. Select reference batches

3. Evaluate variability

4. Configure parameters and limits

5. Validate configuration

6. Finalize and publish

This wizard became the backbone of CPV clarity.

The Reference Batch Variability Preview , Making Statistics Understandable

On the following concept, SMEs can see a redesigned preview of PCA, Hotelling’s T², and Q residual plots.

Previously, these plots were visually overwhelming and detached from workflow context.

The redesign:

• Brought all variability indicators into a unified, interpretable layout.
• Introduced consistent legend behaviour across PCA, loadings, and variability charts.
• Emphasised thresholds and confidence intervals using NGP’s visual language.
• Placed these insights exactly where SMEs make decisions — not in a later disconnected step.

This change improved user comprehension and reduced back-and-forth navigation.

Routine Dashboard, From Chaos to Hierarchical Clarity

The Routine Dashboard (the following concept) previously mixed charts, tables, and signals in a flat layout.

I reorganized the view into:

• Clear hierarchical navigation (Overview → Control Charts → Histogram → Capability Metrics)
• Modular chart containers follow consistent patterns across all statistical views
• Batch-specific deep dives that maintain context through breadcrumb structures

This transformed monitoring from a “hunt and guess” activity into a predictable, navigable flow.

 CPV Plan Details: Bringing Order to Dense Data Tables

In the “View CPV Plan”, the tables listing plans, batches, and variable attributes had to balance:

• high-density data
• regulatory accuracy
• multi-filter interactions
• batch-level comparisons
• quick scanning for deviations

I introduced:

• column grouping
• anchored headers
• adaptive row density
• interaction affordances consistent with NGP table patterns

These changes made dense scientific data readable without diluting its precision.

Notification Workflows & Audit Trail, Designing for Compliance and Trust

The “Manage Notifications & Pending Requests”  highlights critical compliance moments:

Electronic signatures, reviewer workflows, and audit trail entries.

Key improvements included:

• A unified approval card pattern for author, reviewer, and approver actions
• Modal-driven electronic signature flows, with clear legal messaging
• Separation of pending vs. completed actions
• Consistent visual signals for responsibility & status

This redesign reduced ambiguity around regulatory actions and reinforced system trust.

UX Metrics Strategy & Validation Framework

To ensure the redesign delivered real, measurable value, I defined a compact metrics framework aligned to CPV’s scientific and operational goals. As UX Lead, this allowed me to evaluate clarity, efficiency, and system trust objectively—not just through perception, but through evidence.

UX Metrics Defined

I focused on four categories:

Efficiency

• Time to complete CPV plan configuration
• Number of navigation steps per task

Comprehension

• Correct interpretation of PCA, other complex indicators
• SME confidence in variability assessment

Trust & Compliance

• Reduction in external tool usage
• Completion success of e-signature workflows

Consistency

• Adoption of NGP components
• Reduction of UI inconsistencies across CPV

These metrics gave us a clear definition of “good,” validated against real SME expectations.

Validation Approach

To measure outcomes, I used a concise validation loop:

• Baseline benchmarking of the legacy experience
• Moderated SME usability sessions to test comprehension and workflow alignment
• Prototype interaction analytics to confirm efficiency improvements
• Targeted debriefs with Validation Engineers and SMEs to capture decision confidence and trust signals

This combined scientific UX testing with behavioral insights, ensuring the redesigned system supported accurate, reliable decision-making.

Metric Outcomes (High-Level Summary)

While specific numbers remain confidential, the redesigned experience delivered directional improvements:

• Significant reduction in CPV configuration time
• Higher comprehension rates of PCA, Hotelling’s T², CpK/PI thresholds
• Fewer misinterpretations of batch variability
• Clear improvements in system trust and cross-team adoption
• Consistent interface patterns across CPV steps and NGP
• Reduced reliance on external statistical tools

This turned CPV into a structured, high-clarity, scientifically interpretable workflow.

UX Impact & Outcomes

While some outcome metrics are confidential, the redesign delivered clear improvements:

Efficiency

• Faster CPV plan creation due to the structured wizard flow
• Fewer navigation jumps between steps and charts
• Reduced decision fatigue during variability analysis

Clarity & Confidence

• SMEs reported increased understanding of PCA and CpK indicators
• Reduced reliance on external statistical tools to interpret charts-
• More consistent comprehension across global teams

Consistency & Scale

• Established a CPV-wide pattern library reusable across the NGP ecosystem-
• Reduced design and development fragmentation-
• Created a scalable IA model for future features (e.g., trending, automated deviation insights)

Regulatory Alignment

• Stronger audit-trail visibility and signature patterns
• Clearer separation of compliance-critical actions

This was not simply a redesign — it was a system transformation that elevated the entire CPV workflow into an intelligible, reliable, and scalable experience.

Designing for High-Stakes Systems

CPV is a domain where clarity isn’t optional; it’s foundational.

A misinterpreted deviation or a hidden signal can have real-world consequences.

Leading this project demanded:

• Systems thinking to reorganise complexity-
• Scientific UX strategies to make statistical insight understandable-
• Leadership and orchestration to align designers, developers, and SMEs-
• Vision to see the future state of a unified CPV experience inside NGP

This case study represents more than a UX project.

It reflects how design can bring order to scientific complexity, empower decision-making, and build trust in high-stakes environments.

And it reflects my approach as a UX leader:

To transform systems, not screens, and to guide teams toward clarity in places where clarity matters most.

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