Home/Projects/Food Logistics Dashboard
An enterprise inventory and supply chain dashboard concept for food manufacturers — one screen for stock, demand, deliveries, and the decisions each of them triggers.
Role
Solo Product Designer
Passion project
Timeline
Passion project
Ongoing exploration
Platform
Web · Desktop
Enterprise
Tools
Figma · Base44
AI-assisted build
Food manufacturers manage thousands of moving parts. Existing tools show everything at once — this dashboard surfaces only what needs attention right now.
Challenge
Enterprise supply chain platforms are powerful but overwhelming — teams struggle to find what matters in a sea of data.
Opportunity
Operations users don't need more data, they need the right data at the right moment. Prioritize action, not exhaustive reporting.
Solution
A focused dashboard organizing inventory, demand, and deliveries around alerts and next steps rather than raw metrics.
I led product strategy, IA, user experience, interface design, and AI-assisted development to bring a functional prototype to life.
What I owned
I owned the whole thing — problem framing, domain research and a competitive audit, IA, flows, UI, prototyping and interaction design, and the AI-assisted build.
Visibility into ingredients, demand, shipments, and delivery timing is scattered across tools — and that's exactly where things break down.
What's going wrong
Food manufacturing depends on ingredients, demand, shipments, and delivery timing all lining up — but visibility into them is scattered across tools. Shortages stall production, excess stock ties up capital, and delays surface only after they've already hit the line.
The solution in one line
A single dashboard that surfaces what needs attention — low inventory, delayed shipments, changing demand — before it becomes a problem.
Secondary research into food-manufacturing operations plus a teardown of leading enterprise platforms — no primary interviews, so the user picture here is built from published material and documented workflows.
Study at a glance
3
Enterprise supply chain platforms audited.
12
Operational scenarios mapped from secondary research.
5
Core decision types identified across roles.
What research told me
The opportunity was to invert the enterprise model — start with actions and alerts, not endless data tables.
"We don't need more numbers. We need to know what to do about them."
Different responsibilities across the operation, aligned by the need to make faster, better-informed decisions.
Persona
Supply Chain Manager
Materials, deliveries, and forecasting
Responsible for ensuring materials arrive on time so production continues smoothly.
Goals
Pain points
Persona
Operations Manager
Production planning and efficiency
Responsible for production decisions and matching output to demand.
Goals
Pain points
Persona
Warehouse Supervisor
Stock, restocking, and shipments
Managing ingredients and inbound/outbound shipments day to day.
Goals
Pain points
Leading enterprise systems own the space but leave a clear opening for a focused, decision-first workflow.
Competitor
Enterprise-grade supply chain management with extensive reporting.
Strengths
Weaknesses
How this concept improves
Focus on daily operational decisions instead of exhaustive configurability.
Competitor
Broad business platform with inventory management modules.
Strengths
Weaknesses
How this concept improves
Food-industry-specific workflows and terminology out of the box.
Competitor
Enterprise supply chain suite with strong data management.
Strengths
Weaknesses
How this concept improves
Prioritize clarity and speed for smaller operations teams.
Instead of leading with tables, the dashboard leads with what needs attention right now.
Every screen answers one question before it shows anything else: what should this user do next? Data supports that answer — it never precedes it.
Enterprise model
Data tables → filters → interpretation → decision (slow, expert-only).
This concept
Alerts → context → action → supporting data (fast, glanceable).
Why it matters
The critical path collapses multi-system checks into a single view.
01
Open dashboard
Manager opens the overview.
02
Review alerts
Low-stock and delivery alerts surface first.
03
Check inventory
Drill into ingredient status.
04
Track deliveries
See ETAs and delays in one view.
05
Review demand
Spot trending and declining products.
06
Take action
Reorder, reschedule, or reassign — inline.
07
Share status
Push updates to warehouse and ops teams.
08
Log outcome
Track the result of the decision made.
09
Learn
Trends feed forecasting over time.
10
Repeat
Daily loop, minutes not hours.
Wireframes organized the dashboard into four decision areas: overview, inventory, demand, and deliveries.
Low-fidelity wireframes used to establish the dashboard structure, prioritize what information appears first, and decide how users move between overview, inventory, demand, and deliveries — before any visual design was applied.
Walkthroughs with people familiar with enterprise operations surfaced three clear improvements.
Problem
Dashboard felt data-heavy.
Observation
Reviewers scanned for numbers instead of decisions.
Design improvement
Promoted alerts and status indicators above raw metrics.
Problem
Ingredient status wasn't glanceable.
Observation
Reviewers had to read labels to understand stock health.
Design improvement
Added color-coded status and threshold indicators tied to production needs.
Problem
Delivery delays felt buried.
Observation
Reviewers missed delayed shipments in tabular views.
Design improvement
Surfaced delayed shipments in the overview with time-to-impact context.
Every change here traces back to a testing observation.
01 · Iteration
Reorganized from data-first to alert-first. What needs attention now sits at the top.
02 · Iteration
Introduced color-coded status and threshold-based sorting so low stock rises to the surface.
03 · Iteration
Grouped by impact instead of arrival time, so delayed shipments read first.
High-fidelity screens showing alerts, inventory, demand, and delivery views working together.
01 · Screen
The overview leads with actionable alerts — low stock, delayed shipments, product changes — so users know where to look first.
02 · Screen
Ingredient rows with color-coded status. Restock recommendations sit inline with the metric that triggered them.
03 · Screen
Trending and declining products with the underlying data one tap away.
04 · Screen
Incoming trucks, ETAs, and delays with time-to-impact so teams know what to reschedule.
Selected screens from the shipped design.
Because this is a concept product, these are the metrics I would use to evaluate whether the experience is working after launch — not measured results.
| Metric | Why it matters | Target |
|---|---|---|
| Time to first action | How long from opening the dashboard to taking a decision. | < 45 sec |
| Alerts acknowledged | Alerts reviewed within an hour of surfacing. | ≥ 90% |
| Missed shortages | Ingredient shortages that reached production. | Trend down |
| Delivery delay lead time | Warning window before a delay impacts production. | ≥ 24 hrs |
| Daily active users | Ops team members opening the dashboard daily. | ≥ 80% |
| Decision cycle time | Average time from signal to action logged. | Trend down |
This concept demonstrates that enterprise UX can be simple without being shallow — clarity is the feature.
What I learned
Good dashboard design isn't about showing more information. It's about helping users understand information faster.
Challenges
Balancing depth with clarity — enterprise users still need the full data, just not first.
What's next
Predictive alerts, cross-team collaboration surfaces, and forecasting layered onto the same architecture.
The lesson from this project: the hardest part of designing for complex workflows is deciding what not to show.
This was a concept, but the constraints reflect the reality of the enterprise space.
Constraint
Screens had to feel credible at enterprise data volume, not just demo-friendly.
Constraint
Same screens had to serve supply chain, operations, and warehouse roles.
Constraint
The design had to complement, not replace, existing enterprise systems.
Constraint
Managers scan dashboards between meetings — clarity in seconds was non-negotiable.
Type, color, and components tuned for information density without visual noise.
Typography
Display
Food Logistics
Heading
Inventory overview
Body
Ingredient stock is 12% below production threshold.
Caption
Updated 2 min ago
Color palette
Ink
Foreground
Paper
Background
Surface
Surface
Accent
Signal green
Accent soft
Green tint
Muted
Muted
Buttons
Spacing scale
Components
Delayed
2 shipments
OK
Sugar · healthy
Accessibility choices baked into the dashboard from the first wireframe.
01
Every status indicator pairs color with an icon and text label.
02
Data tables and alerts meet WCAG AA against both light and elevated surfaces.
03
Every action in the dashboard is reachable and operable with the keyboard.
04
Same visual language for the same status across all views.
05
Line-height and spacing tuned so dense tables stay legible.
With more time and access to real users, these directions would come next.
Next
Use historical trends to warn before a shortage or delay happens.
Next
In-context handoffs between supply chain, operations, and warehouse teams.
Next
Blend demand and delivery data into rolling forecasts inside the same UI.
Next
A pared-down mobile view for warehouse teams walking the floor.