Business Context & Problem

This tool supports scientists and process engineers in scaling bioprocesses from lab experiments to production-scale manufacturing.

Scaling is not a simple multiplication process. It involves maintaining biological viability, process stability, and regulatory compliance while predicting performance across different reactor sizes. Errors at this stage can lead to failed batches, production delays, and significant financial impact.

The organization needed a digital tool that could help users model scaling scenarios accurately, reduce manual calculations, and support confident decision-making.

However, existing workflows were fragmented, calculation-heavy, and cognitively demanding.

Business Goal Design Lead Role & Scope

UX design was led — from discovery through delivery. This was a cross-functional, high-complexity environment where domain translation was critical.

My Responsibilities included

• Understanding scientific workflows

• Structuring the interaction model

• Designing decision-support interfaces

• Facilitating alignment between domain experts, product, and engineering

• Ensuring usability without compromising scientific accuracy

Users & Research Insights

Primary Users

Key Insights

• Process development scientists

• Bioprocess engineers

• Manufacturing planners

User Goals

• Predict scaling outcomes

• Compare process scenarios

• Maintain parameter consistency

• Reduce experimental risk

• Users think in process relationships, not isolated parameters

• Decision confidence depends on traceability of assumptions

• Cognitive load was extremely high due to fragmented tools

• Users needed simulation visibility, not just results

This shifted the design direction from a calculation tool to a decision-support environment.

Problem Framing & Design Strategy

Three Design Principles

Process Visibility

Show relationships, not just outputs.

Decision Confidence

Make assumptions explicit.

Progressive Complexity

Reveal depth without overwhelming users.

Exploration & Workflow Structure

Restructured the experience into a guided workflow:

Key Design Elements

• Structured parameter grouping

• Scenario comparison visualization

• Interactive data modeling views

• Clear traceability of assumptions

1

Define source process

2

3

4

5

Configure scaling parameters

Run simulation

Compare scenarios

Evaluate risk indicators

Key Decisions & Trade-offs

Each decision was validated with domain experts.

Validation & Iteration

We validated designs through iterative SME reviews and usability walkthroughs.

Key Improvements after testing

• Clearer parameter dependencies

• Improved scenario comparison clarity

• Reduced interpretation ambiguity

Users reported improved understanding of scaling outcomes and reduced mental effort during evaluation.

Outcomes & Impact

The final tool improved both usability and decision confidence.

• Reduced time to evaluate scaling scenarios

• Fewer manual calculation steps

• Improved traceability of decisions

• Higher user confidence in scale - up and scale - down planning

Stakeholders also reported better cross-team communication because results were easier to interpret and share.