Streamlined Maintenance
This project involved designing a cloud-based reliability management solution for a global enterprise software suite. The system was used by reliability engineers, floor operators, and maintenance planners to assess asset performance, identify potential failures, and recommend preventive actions.
This project is protected under an NDA. The following overview shares my design approach and outcomes at a high level.
More details can be discussed privately.
Project Type
Enterprise UX / SaaS (Industrial Systems)
My Role
Senior User Experience Designer
Responsibilities
Experience Design, Workflow Mapping, User Research
Duration
14 months
Task
My role centered around simplifying highly technical workflows, understanding the ground-level context of how maintenance decisions are made, and defining scalable, human-centered design patterns for an enterprise platform transitioning from legacy data systems to a unified cloud experience.
Context
The product team was modernising a legacy reliability management module used in critical industrial environments from automotive and manufacturing to heavy engineering.
The older system relied heavily on technical forms, spreadsheets, and manual data entry. Our challenge was to design an interface that was:
Easy to navigate
Even for first-time users with technical backgrounds.
Scalable
To handle complex data relationships between assets, failures, and recommendations.
Collaborative
Allowing distributed global teams to align on maintenance strategies in real time.
As part of the global SAP design ecosystem, our India design team worked closely with stakeholders in Germany (domain experts and product owners) and the USA (engineering architects) to bridge business goals, technical feasibility, and real-world usability.
Design Challenge
Reliability engineering deals with identifying the why behind equipment failure and preventing recurrence.
While the existing system captured data effectively, it lacked the usability and structure to support:
Visibility into linked data relationships.
Fast assessments and strategy creation.
Consistency across different types of industrial assets.
Our design challenge was not simply about “making it usable” but about translating a highly analytical, engineering-driven process into an experience that feels intuitive and assistive for the user.
Research Approach
To understand the domain deeply, I collaborated with:
Reliability Engineers and
Floor Managers
who shared ground-level perspectives on how they identify, report, and act upon failures.
Product Managers and Domain Experts
who explained system dependencies and data modeling constraints.
Technical Architects
who guided backend integration and API feasibility.
Through virtual workshops, call transcripts, and knowledge transfer sessions, I built empathy for how reliability assessments are conducted from the shop floor (where failures are observed) to management layers (where maintenance strategies are approved).
Key Takeaways
Users think in hierarchies,
not pages. They see assets as interconnected systems, not isolated components.
Context
is everything. A maintenance recommendation depends on where the equipment operates, not just how it behaves.
Terminology consistency is critical; engineers trust systems that speak their language precisely.
Design Process
Understanding the Legacy System
I began by mapping existing workflows from the legacy platform, identifying key pain points like redundant data entry, inconsistent hierarchies, and low task visibility. This mapping became the foundation for the redesign strategy.
Collaborative Ideation
Working closely with global teams, we co-created design hypotheses around guided task flows, contextual visibility, and data reuse. Regular workshops helped align technical feasibility with user needs.
Designing for Cloud and Scale
The new experience was structured for cloud deployment with modular components, adaptive data views, and role-based flexibility. This allowed engineers to manage complex data models efficiently while supporting integration across enterprise systems.
Testing and Iteration
Through iterative validation sessions with reliability engineers, we refined workflows, reduced cognitive load, and improved information clarity. Progressive disclosure and contextual hierarchy patterns replaced rigid form structures, making the tool intuitive
and scalable.
Impact
A modernized, cloud-based interface that reduced completion time for key workflows by over 50%.
35% higher adoption among reliability engineers due to reduced learning curve.
Improved cross-team visibility, with data and recommendations linked contextually.
The foundation for predictive, AI-driven maintenance insights built atop the same design structure.
Reflections & Learnings
Enterprise empathy is built, not assumed. Understanding how a floor technician perceives data changed how we designed interfaces for engineers.
Designing for trust in enterprise tools means balancing user freedom with data integrity.
Cross-geographical collaboration demands clarity in documentation and communication more than speed.
Legacy modernisation isn’t about replacing UI, it’s about preserving institutional knowledge while enabling scalability.