Auditable formula · fictional example
Cost per Delivered Order: Formula and Shipping-Cost Test
Direct answer: Cost per delivered order divides consistently defined logistics cost by successfully delivered orders. It is often more useful than cost per shipment because failed attempts and redelivery work remain in the cost while only completed deliveries enter the denominator.
Numerator
R$120,000 eligible logistics cost
Denominator
8,000 successful deliveries
Fictional result
R$15.00 per delivered order
Formula and worked example
Cost per delivered order = eligible logistics cost / successfully delivered orders
Fictional example: R$120,000 of eligible logistics cost / 8,000 delivered orders = R$15.00 per delivered order.
The numbers demonstrate the calculation only. They are not a benchmark, forecast, target, vendor result, or promise.
Four-step measurement method
- Step 1
Define a successful delivery and the measurement period.
- Step 2
Sum the same transport, handling, toll, redelivery, and agreed planning costs.
- Step 3
Reconcile completed deliveries and remove unresolved or cancelled orders.
- Step 4
Compare the metric with on-time and first-attempt success so cost does not hide service loss.
Checks before comparison
- Delivery status is final and reconciled
- Redelivery cost is included consistently
- Service levels are comparable
- Route and order mix are segmented
Common measurement errors
- Using shipped orders in the denominator
- Improving cost by excluding difficult routes
- Ignoring on-time performance
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
Why can this be higher than cost per shipped order?
Because failed or unresolved shipments can add cost without becoming successful deliveries, leaving a smaller denominator.
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.