---
license: mit
tags:
- operations-research
- supply-chain
- inventory-allocation
- omnichannel
- synthetic
task_categories:
- other
---

# Omnichannel Network and Order Scenarios

Five synthetic omnichannel fulfillment networks (one per demo scenario),
each with a central distribution center, two regional warehouses, a
supplier hub, a cross-dock, a returns/repair/recycle cluster, 24 stores,
a 60-SKU catalog with substitute groups, noisy inventory records, and a
day's worth of multi-line customer orders — generated with a fixed random
seed for reproducibility.

## Scenarios

| Key | Description |
|-----|-------------|
| `peak_season_surge` | Demand spikes 65% while picking capacity stays flat |
| `regional_stockout` | Regional warehouse inventory collapses, forcing ship-from-store |
| `high_return_category_wave` | Apparel return rate more than doubles |
| `capacity_constrained_dc` | Central DC picking capacity cut to 35% |
| `green_logistics_push` | Corporate mandate to weight carbon heavily in allocation |

## Files

| File | Contents |
|------|----------|
| `manifest.json` | Index of all scenario files with entity counts |
| `sample_<scenario_key>.json` | Full network snapshot: nodes, SKUs, inventory, transport links, orders, scenario events |

## Schema

Each `sample_*.json` file is a serialized `Network` object:

```json
{
  "scenario_id": "...",
  "nodes": {"DC1": {"node_id": "...", "node_type": "central_dc", "daily_pick_capacity": 0}},
  "skus": {"SKU0000": {"sku_id": "...", "category": "...", "unit_price": 0.0, "substitute_group": "..."}},
  "inventory": [{"node_id": "...", "sku_id": "...", "on_hand_qty": 0, "accuracy": 0.0}],
  "links": [{"origin_id": "...", "dest_id": "...", "distance_km": 0.0, "ship_mode": "standard"}],
  "orders": [{"order_id": "...", "demand_node_id": "...", "priority_class": "...", "lines": []}],
  "events": [{"event_type": "...", "description": "...", "affected_node_ids": []}]
}
```

## Generation

Regenerate with:

```bash
python scripts/build_assets.py
```