About the project

SINEC NMS is an enterprise (B2B) on-premises network management system built to support large-scale industrial environments, enabling efficient monitoring and maintenance of complex network infrastructures.

As a Senior UX Designer, I worked collaboratively with the Lead UX Designer to enhance monitoring, troubleshooting and scalability of the application — creating a more intuitive and consistent experience for network engineers and operators.

Collaborators

Product Owner, Development Lead, Senior Project Manager (Germany)

Tools

Adobe XD, Figma, Miro, Internal Siemens Design System

Challenges & Goal

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

Key challenges

• Quickly identifying and resolving device or connectivity issues

• Navigating complex and hierarchical network structures

• Maintaining visual consistency across large, data-dense dashboards

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.

© 2026 All rights reserved. Designed with purpose.

Divya HARIDAS