One inspection grammar across every QDIP application.
- 01Situation
- 02Alternatives
- 03Evaluation
- 04Risk / uncertainty
- 05Recommendation
- 06Evidence
- 07Trace
Scenario context
A distribution network must keep demand served when warehouses, routes or capacity become constrained or unavailable.
How should inventory, inbound supply, transfers and fulfillment be reallocated while preserving service floors and minimizing economic loss?
Tests disruption recovery, candidate warehouse evaluation and economic comparison under the same normalized network.
- • Warehouses, inventory and throughput capacities
- • Demand points and product classes
- • Suppliers, inbound supply, delivery/transfer routes and baseline fulfillment
- • Storage/receiving/dispatch capacity
- • Route capacity and lead time
- • Minimum service and concentration policy
- • Scenario analysis explicitly distinguishes hard constraints from soft policy penalties; solver optimality and MIP gap are reported.
Analyst data workspace
Choose synthetic data, semantic parsing for unfamiliar business files, or the exact versioned template when reproducibility matters.
Smart import
CSV / XLSX / JSON is analyzed by the shared Decision Intake capability. It proposes semantics; it does not silently run the optimizer.
Exact template
Versioned JSON bypasses semantic inference. Validation must pass before it can replace the active scenario input.
Data dictionary
| Field | Meaning | Type / unit | Requirement |
|---|---|---|---|
| product_classes[] | Products with value, holding cost and storage class | object[] | required |
| demand_points[] | Demand locations and daily product demand | object[] | required |
| warehouses[] | Warehouse location, storage/flow capacities and inventory | object[] | required |
| suppliers[] | Supplier nodes | object[] | required |
| inbound_supply[] | Available inbound supply and routes to warehouses | object[] | required |
| delivery_routes[] | Warehouse-to-demand routes with lead time, capacity and cost | object[] | required |
| transfer_routes[] | Inter-warehouse routes | object[] | required |
| policy | Service floors, concentration limits, penalties and solver budget | object | required |
Exact-data validation run
Run the optimizer directly on the validated exact network. This path does not infer field meaning and never falls back to demo data.
Load a valid exact template first.
Demo model · synthetic data · calculations are performed by QDIP
When a warehouse drops out, see the least-cost way to keep stores supplied.
Start with a working retail network, take one warehouse offline, and see how QDIP reroutes inventory, protects demand and tests whether another warehouse would pay off.
warehouses
stores
product groups
Try the disruption in 3 steps
1 · Review the current operational plan on the map
2 · Choose a warehouse and take it offline
3 · Compare the recovery plan and test warehouse options
What this demonstrates
QDIP turns the same demand, inventory, capacity and route constraints into an auditable recovery decision: what to reroute, what remains uncovered, what it costs and whether extra warehouse capacity improves the economics.
Warehouses
Choose a warehouse from the list or tap it on the map.