Duration
- 1 month
Supply Chain Performance
How I redesigned a legacy supply-chain reporting workflow into a configurable enterprise report — helping retail teams at Walmart, Rite Aid, and Sobeys analyze performance across product, store, and distribution hierarchies in one place.
Overview
After NIQ's convergence efforts, supply-chain analysis still relied on multiple reports and legacy patterns. Users could answer important questions about in-stock position, inventory health, and sales impact, but only by navigating fragmented tools. This project focused on creating a more unified performance report: one that allowed users to compare top-line impact, drill across product, store, and DC hierarchies, and understand how inventory conditions were affecting sales and operational performance. This aligned closely with the product goal of converging older supply-chain reporting paths into a more modern Activate workflow.
My Role
Lead Product Designer
Team Role
- 2 Product Managers
- 2 BI Developers
- 2 Data Scientists
- 3 Engineers
Target Audience
- Inventory planners
- Retail operations teams
- Category managers
- Supplier / brand partners
Scope
Research, UX, UI, information architecture, KPI structuring, prototyping, testing
Tools
- Figma
- Excel
- Highcharts
- Jira
Industry
- Retail analytics
- Supply chain planning
- Inventory performance
Platform
Web application — Desktop-first enterprise reporting
Users Impacted
Brand managers, promotion analysts, category managers, and supply chain managers across supplier and retailer workflows
Data Coverage
Sales + inventory performance across product hierarchy, store hierarchy, and distribution centers
| Business Question | UX Response |
|---|---|
| Where are we underperforming? | →Surface KPI summary row first |
| What is the inventory impact? | →Unified sales + inventory view |
| Which child segments are driving it? | →Coordinated bottom-table breakdown |
| Is it product, store, or DC level? | →Flexible hierarchy selection model |
| What should teams investigate next? | →Continuation path to trend / export |
Research & Discovery
From feedback to interface requirements
Discovery showed that the report was not failing because users lacked data. It was failing because different roles entered with different pressure: analysts needed faster root-cause paths, replenishment managers needed evidence for escalation, and reviewers needed confidence before a number became a QBR narrative.
01Analyst signal
The workflow was slowing them down.
Analysts knew the root-cause patterns. The bottleneck was navigation friction, dead-end filters, and repeated context rebuilding across Product, Store, and DC views.
Design response
Default to exceptions first, then preserve the full inventory path for deeper review.
02Manager signal
A red number was not enough evidence for a review conversation.
Escalation was the recurring failure: a stockout seen on Tuesday could become Friday executive pressure without a clear evidence trail.
Design response
Add alert history, affected locations, and an exportable review snapshot.
03Data trust signal
The number could be correct and still misleading.
Days on Hand and velocity looked precise even when store feeds were delayed or partial, so confidence had to sit beside the metric.
Design response
Put confidence states and calculation context directly beside key metrics.
Design Principles
Principle → interface behavior mapping
Exception-first hierarchy
Surface stockout risk, overstock, and velocity anomalies before the full inventory table.
Interface behavior
Exceptions panel prioritizes issues by impact score and confidence.
Top exceptions
| Issue | Impact | Status |
|---|---|---|
| Stockout risk | 98 | Investigate |
| Overstock | 86 | Review |
| Velocity drop | 72 | Monitor |
See all (128) →
Context continuity
Carry filters and hierarchy through Product → Store → DC drill-down.
Interface behavior
Path bar preserves context and active filters across levels.
Inventory
Context preserved
View path →
Comparative clarity
Show prior period, YoY, benchmark, and peer-set comparisons before export.
Interface behavior
Comparison matrix is visible in-line with consistent metrics.
Product A · Store 214
| Metric | Current | Prior | Signal |
|---|---|---|---|
| On hand | 12.4K | 11.8K | ↓ |
| Sales | $428K | $401K | ↑ |
| Velocity | 2.1 | 2.4 | ↓ |
Review-ready evidence
Package issue, exposure, locations, confidence, and next action into a review snapshot.
Interface behavior
One-click snapshot compiles the right evidence and recommended action.
Review packet
- Issue
- Exposure
- Locations
- Confidence
- Next action
Share packet →
These principles map directly to decision moments in the supply chain: triage, investigate, compare, and escalate.
Report Architecture
The report architecture was organized around a repeatable investigation path: start with exceptions, choose the right lens, drill into affected locations, and validate the finding before export.
Scan exceptions
Exception queue
| Issue | Impact | |
|---|---|---|
| Stockout risk · Dairy | 98 | |
| OTIF drop · Region West | 86 | |
| Velocity anomaly | 72 | |
| Overstock signal | 64 |
Full table below
DecisionSurface the highest-risk exceptions before the full inventory table.
Select lens
PeriodCategoryClient
Investigation lens
Product
Store
DC
Period
DecisionPreserve product, store, DC, period, and issue context.
Drill into affected locations
Affected locations
| Location | Variance |
|---|---|
| Store #1142 | –4.2pp |
| Store #0891 | –2.1pp |
| Store #2204 | –0.8pp |
DecisionShow the operational driver behind the exception.
Validate and export
Prior
YoY
Benchmark
Review packet
- Issue
- Exposure
- Confidence
- Export
Export evidence
DecisionValidate the finding before it becomes review evidence.
The architecture preserved context across the path, helping users move from signal to evidence without rebuilding the investigation.
Final Reporting Experience
From investigation path to review-ready evidence
The report architecture became three production tabs — each designed to move users from signal to review-ready evidence without rebuilding context.
Exception Overview
The overview tab became the business entry point for supply chain performance — helping category and operations teams see where risk is concentrated, which KPIs need attention, and what to review before drill-down or escalation.
Investigate
The investigate tab became the drill-down path from summary to root cause — helping analysts compare regions and categories in stacked tables, select an exception row, and open evidence in a persistent drawer without rebuilding context.
Trends
The trends tab became where teams validate signals over time — pairing multi-metric charts with hover tooltips that expose week-level in-stock and out-of-stock detail without leaving the report.
Full-width views from the NIQ Supply Chain Performance report — exception-first overview, investigate drill-down with root-cause drawer, and trends validation with hover detail.
Reflection
What this work changed in my practice
This project strengthened my thinking around enterprise reporting systems: not just how to display metrics, but how to design for hierarchy logic, configurability, and analytical continuity.
Structure complexity, don't remove it
In complex B2B tools, clarity comes less from removing complexity and more from structuring hierarchy logic, permissions, and analytical continuity well.
Exception-first beats data-first
Defaulting to items needing attention now — stockout risk, velocity anomalies, DC concentration — reduced time-to-diagnosis versus legacy full-table entry.
Context must survive drill-down
Preserving product, store, DC, and period context across tabs stopped analysts from rebuilding the investigation path on every click.
Trust is a UI problem
Confidence states, escalation paths, and exportable snapshots matter as much as the charts — operational trust is designed, not assumed.
Next case study
Vendor Compliance Scorecard
A performance scorecard that helps retailers and suppliers align on KPIs, track efficiency, and drive better decision-making across global supply chains.
