Auditable formula · fictional example
Cost per Shipped Order: Formula and AI Logistics Baseline
Direct answer: Cost per shipped order divides the agreed eligible logistics cost by orders released for shipment in the same period. It measures operating cost at dispatch, not delivery success or customer price.
Numerator
R$120,000 eligible logistics cost
Denominator
8,200 shipped orders
Fictional result
R$14.63 per shipped order
Formula and worked example
Cost per shipped order = eligible logistics cost / shipped orders
Fictional example: R$120,000 of consistently defined logistics cost / 8,200 shipped orders = R$14.63 per shipped order.
The numbers demonstrate the calculation only. They are not a benchmark, forecast, target, vendor result, or promise.
Four-step measurement method
- Step 1
Freeze the period and list every eligible cost category.
- Step 2
Count orders actually released for shipment, net of cancellations before dispatch.
- Step 3
Divide cost by shipped orders without changing definitions between periods.
- Step 4
Segment the result by carrier, lane, service level, and package profile.
Checks before comparison
- Same cost categories in baseline and pilot
- Cancellations treated consistently
- Own-fleet and carrier costs not mixed silently
- Shipment count reconciled to the system of record
Common measurement errors
- Calling a shipped order a successful delivery
- Adding redelivery cost to only one period
- Treating a lower average as proof of AI impact
Attribution boundary
A change in this metric does not prove that AI caused it. Compare a frozen baseline and controlled pilot, then inspect route mix, distance, fuel, tolls, carrier rates, service level, package profile, promotions, weather, exclusions, and policy changes. Protect service and failure metrics while evaluating cost.
Frequently asked questions
Is cost per shipped order the same as cost per delivered order?
No. The shipped denominator includes orders released for transport; the delivered denominator includes successful deliveries and therefore exposes failure and redelivery effects.
Does this metric prove that AI caused the result?
No. A before-and-after change can also reflect route mix, distance, fuel, carrier rates, package profile, promotions, weather, exclusions, or operational policy. Use a controlled pilot and inspect segments before attributing causality.