A professional consultant-grade tool for analysing, optimising, and reporting on warehouse operations. Built for logistics consultants who visit client sites, identify inefficiencies, and deliver actionable improvement plans.
WOA is a decision-support dashboard used during client warehouse visits. You configure the warehouse layout, enter operational data, and the tool instantly:
- Detects bottlenecks across all zones using rule-based algorithms
- Scores efficiency using throughput balance and zone utilisation models
- Generates prioritised recommendations with ROI estimates
- Simulates what-if scenarios (peak demand, layout changes, automation, extra staff)
- Exports a consultant-grade PDF report with findings and roadmap
| Module | Description |
|---|---|
| Dashboard | Live KPI overview, throughput chart, warehouse heatmap, active issue feed |
| Warehouse Setup | Configure zones, staff headcount, operational parameters |
| Bottleneck Analysis | Severity-ranked issues with business impact assessment, radar chart, travel distance analysis |
| Optimization | 8+ rule-based recommendations with impact scores, effort ratings, and payback estimates |
| Simulation | 6 preset scenarios (peak demand, fast-mover relocation, automation, cross-docking…) with before/after comparison table and charts |
| Report | Full consultant report with executive summary, zone table, recommendations roadmap, and print-to-PDF export |
The analysis engine evaluates each zone across 5 dimensions:
- Zone utilisation — flags zones ≥ 85% (warning) or ≥ 92% (critical)
- Throughput mismatch — detects rate imbalances between sequential zones (e.g. picking < packing capacity)
- Travel distance — highlights zones where avg picker travel exceeds the 35m optimal threshold
- Peak capacity gap — calculates whether current max throughput meets peak demand (order_volume × peak_multiplier)
- Fast-mover SKU positioning — detects Pareto inefficiency where high-volume SKUs are stored far from picking
| Scenario | What It Models |
|---|---|
| Peak Season (+40% Volume) | Q4 demand surge vs. current staffing |
| Fast-Mover Relocation | Move top 20% velocity SKUs to golden zone |
| Workforce Expansion (+30%) | Add pickers and packers |
| Cross-Docking Strategy | Bypass storage for 30% of volume |
| Partial Automation (Conveyor) | Conveyor between picking and packing (+35% rate) |
| Split Picking Zones (A/B) | Dedicated fast/slow-mover picking zones |
- React 18 + TypeScript — component architecture
- Zustand — persistent state management (localStorage)
- Recharts — all charts (area, bar, radar, radial)
- Tailwind CSS — utility-first styling with dark theme
- Framer Motion — transitions
- React Router v6 — client-side routing
- Vite — build tooling
git clone https://github.com/YOUR_USERNAME/warehouse-optimization-advisor.git
cd warehouse-optimization-advisor
npm install
npm run devThis tool was designed to support logistics consultants during client site visits. Instead of manually building spreadsheets after each visit, a consultant can input warehouse parameters on-site, immediately see bottlenecks, run scenarios live during the client meeting, and export a branded PDF report at the end of the session.
It mirrors real WMS consulting workflows:
- Inbound receiving → staging → storage slotting → pick routing → pack → ship
- ABC/XYZ velocity analysis for slotting recommendations
- Staff allocation modelling per shift and zone
- Peak demand planning (×2–3 multipliers for B2C fulfilment)
src/
├── engine/ # Core algorithms (bottleneck detection, simulation, recommendations)
│ ├── bottleneck.ts # Rule-based bottleneck detection
│ ├── calculations.ts# KPI calculations, throughput, efficiency scores
│ ├── recommendations.ts # Recommendation engine with impact estimates
│ └── simulation.ts # What-if scenario runner
├── store/ # Zustand state management
├── types/ # TypeScript interfaces
├── data/ # Sample warehouse dataset
├── components/ # Reusable UI components
│ ├── layout/ # Sidebar, Header, MainLayout
│ └── shared/ # WarehouseMap, StatCard, SeverityBadge
└── pages/ # One file per route
MIT