Multi Warehouse Allocation Calculator

Inputs

Order and warehouse data

Paste CSV-like rows. Use the examples below.

Order lines (sku,qty) *

One line per SKU. Example: SKU-101,12

Warehouse stock rows *

Columns: warehouse,sku,stock,ship_cost,handle_cost,lead_days,capacity

Optimization options

Allow split fulfillment across warehouses

Max warehouses per order

Lower values reduce shipments and complexity.

Split penalty

Higher values prefer reusing fewer warehouses.

Weight: shipping

Weight: handling

Weight: lead time

Lead time is used in ranking, not cost.

Example

Example inputs and what they mean

Order lines example

sku	qty
SKU-101	12
SKU-205	8
SKU-330	5
SKU-777	9

These are items requested by the customer order.

Warehouse rows example

warehouse	sku	stock	ship_cost	handle_cost	lead_days	capacity
Karachi-1	SKU-101	10	6.50	0.35	2	100
Lahore-2	SKU-205	10	5.20	0.40	3	80
Islamabad-3	SKU-777	12	7.10	0.30	1	60

Repeat a row per warehouse and SKU combination.

Method

Formula used

For each SKU, the calculator ranks warehouses by a weighted score and allocates demand to the best options first.

Score(w) = w_ship·ShipCost(w) + w_handle·HandleCost(w) + w_lead·LeadDays(w) + SplitPenalty·NewWarehouse(w) + CapacityPenalty(w)

NewWarehouse(w) is 1 if the warehouse is not yet used, else 0.
CapacityPenalty sharply increases when capacity is exhausted.
Allocation is greedy: take as much as possible from the lowest score first.

Guide

How to use this calculator

Paste order lines as sku,qty rows.
Paste warehouse rows with stock and cost fields.
Set weights to match your business priorities.
Enable split fulfillment only when needed.
Click Allocate Inventory to generate the plan.
Export CSV or PDF for sharing and auditing.

Demand signals and order structure

Multi-warehouse allocation starts with order lines. Grouping by SKU reduces picking noise and makes shortages visible. In many catalogs, the top 20% of SKUs generate about 80% of order volume, so weighting those SKUs in replenishment decisions improves service levels. Use consistent SKU formatting, avoid mixed units, and validate quantities as positive numbers to prevent over-allocation.

Cost components you can tune

The calculator uses shipping cost per warehouse and handling cost per unit. Shipping cost represents fixed shipment overhead, while handling scales with units picked. A useful baseline is to set shipping and handling weights to 1.0, then raise shipping weight when split shipments hurt margins. Lead time weight acts as a ranking driver, helping faster facilities win ties without directly inflating monetary totals.

Capacity and throughput limits

Capacity is modeled as an upper bound on total shipped units per warehouse for the allocation run. If a warehouse hits capacity, the scoring effectively blocks further allocation there, pushing volume to other nodes. For operations, capacity can reflect daily pick limits, carrier cutoffs, or labor availability. Entering capacity values avoids plans that look cheap but cannot be executed.

Split fulfillment control and penalties

Allowing split fulfillment increases fill rate but can create multiple parcels. The split penalty discourages introducing a new warehouse, preferring reuse when inventories are comparable. When penalty is high, the algorithm behaves like “fewest shipments first”; when low, it behaves like “cheapest units first.” Test scenarios by adjusting penalty in steps of 0.5 to see stability.

Interpreting allocation and unmet demand

The allocation table is the execution plan: which warehouse ships which SKU quantity. The unmet table highlights stockouts by SKU, enabling targeted transfers or purchase orders. If unmet demand concentrates in one SKU, move safety stock closer to demand. If unmet demand spreads across many SKUs, review forecasting accuracy and minimum reorder points.

Using the Plotly graphs for decisions

The “Allocated quantity by warehouse” chart shows workload distribution. If one warehouse dominates, you may be over-optimizing cost while risking congestion. The “Unmet quantity by SKU” chart reveals where inventory gaps drive loss. Combine both: when a warehouse has spare capacity but unmet SKUs persist, inventory is in the wrong place, not missing.

FAQs

1) Does the calculator guarantee the globally optimal plan?

No. It uses a fast greedy ranking approach designed for planning. It produces strong results and is easy to tune with weights, penalties, and capacity constraints.

2) When should I disable split fulfillment?

Disable it when multi-parcel shipping is expensive, customer experience suffers from multiple deliveries, or warehouse coordination is limited. Enable it when fill rate is the top priority.

3) What should I enter for shipping and handling costs?

Use average per-shipment carrier and packaging cost as shipping cost, and average pick-pack labor plus materials per unit as handling cost. Keep units consistent across warehouses.

4) How does capacity affect the result?

Capacity limits total units allocated from a warehouse for this run. Once reached, that warehouse becomes effectively unavailable, so demand shifts to other nodes even if they are costlier.

5) Why do I see unmet demand even with stock available?

Common causes are low max warehouses, capacity limits, or split fulfillment disabled. Relax one constraint at a time to identify which rule is preventing allocation.

6) Can I use it for multiple orders in a batch?

Yes. Paste combined order lines by SKU, or merge multiple orders into a single demand list. For per-order shipment counts, run orders separately and compare outcomes.