Helping Operations Directors move from data overload to decisive action.
This project focused on the end-to-end redesign of our Analytics platform, used by Directors of Operations in manufacturing environments.
The goal was simple but critical:
Transform a complex, underused reporting tool into a decision-making system that clearly answers one core question:
“Which is my worst line — and why?”
My Role: As Product Designer (UX-focused), I led the Introduction of product analytics to the company, usage research and behavior analysis, User interviews with Directors of Operations, Insight synthesis and problem framing, Redesign of the Analytics experience, Definition of new data visualization standards, Dashboard configurability strategy.
The Challenge
The Analytics platform existed, but adoption and trust were low. There was no product analytics in place, no visibility into usage patterns, and limited understanding of user sentiment.
From a UX perspective, this wasn’t just a UI redesign — it was a foundational discovery and strategy project.
Research & Discovery
1. No Usage Data
The company had no visibility into how the Analytics platform was used. I introduced product analytics (event tracking, feature usage, funnels) to understand adoption gaps, identify drop-off points, measure engagement by role. This shifted the organization from assumption-based decisions to data-informed design.
2. User Interviews
Through qualitative interviews, several patterns emerged:
- Lack of Awareness: Some users didn’t even know the Analytics platform existed.
- Many users preferred to export data to, cross-reference with external sources, manipulate numbers manually, build their own comparisons
- Lack of Trust, Users reported differences in calculations in the realtime vs analytics platforms, creating uncertainty about calculation logic and confusion about what they were actually looking at
Without trust, analytics becomes noise.
- Meaningless Visualizations, line charts dominated the interface, but users didn’t need trends — they needed prioritization. A line graph does not answer clearly which line is performing worst? How far is it from target? Is it underperforming compared to others?
Critical Insight:
Context Drives Decisions
Numbers alone are meaningless without context.
Directors of Operations need to know, Are we meeting our targets? How far are we from our benchmark? Is this line underperforming compared to the others?
Benchmarks and target comparisons are essential for prioritization.
A line performing at 78% OEE may look “acceptable” — but if the target is 85%, it becomes a clear priority. Conversely, a lower number might be acceptable if aligned with realistic expectations.
Without targets and comparisons, users cannot assess urgency.
Problem Statement
The Solution
1. From Trends to Prioritization
We replaced most line graphs with ordered bar charts, designed to always sort from worst → best, clearly show deviation from target. Using consistent colour coding used in real-time monitoring was applied in Analytics to reinforce familiarity and reduce cognitive load.
Now, in one glance, users can see, which line is worst, how far it is from its target, how it compares to others, where immediate action is required. No interpretation required.
2. OEE Component Visibility
Instead of showing only aggregated OEE, users can now see at once, Availability, Performance and Quality. For each production line, alongside their respective targets. This allows them to immediately identify whether the issue is: Downtime (Availability), Speed (Performance) or Rejects (Quality). And whether each component is below benchmark.
3. Action-Oriented Clarity
Once the issue is identified, the user can clearly determine, Should we reduce downtime? Should we increase speed? Should we address quality issues?
Because every KPI is contextualized against its target, improvement opportunities become measurable and actionable.
4. Configurable Dashboard
Recognizing that operational priorities vary, we introduced a configurable dashboard, users can choose which KPIs to display, adjust timeframes, compare against targets or historical benchmarks, filter by line, product, or shift. This balances standardization with flexibility while preserving clarity.
Impact
Increased visibility into platform usage via In-app campaign after redesign, Improved trust through consistent calculations and visual logic, Clear benchmark visibility across all KPIs.
The redesigned Analytics platform empowers Operations Directors to move from:
“I have too much data and I don’t know what I’m looking at.”
to
“I know exactly where the problem is, how far it is from target — and what to fix.”