Auditable formula · fictional example
First-Attempt Delivery Success: Formula and Failure-Reason Audit
Direct answer: First-attempt delivery success divides orders completed on their first eligible attempt by all orders that received an eligible first attempt. It exposes address, access, capacity, customer, and planning failures that averages can hide.
Numerator
7,600 orders completed on the first attempt
Denominator
8,000 eligible first-attempt orders
Fictional result
95.0% first-attempt success
Formula and worked example
First-attempt success = first-attempt deliveries / eligible first-attempt orders x 100
Fictional example: 7,600 first-attempt deliveries / 8,000 eligible first attempts x 100 = 95.0%.
The numbers demonstrate the calculation only. They are not a benchmark, forecast, target, vendor result, or promise.
Four-step measurement method
- Step 1
Define what starts and completes an eligible delivery attempt.
- Step 2
Assign one primary reason code to each failed first attempt.
- Step 3
Calculate the rate and reconcile attempts to order IDs.
- Step 4
Compare reason-specific changes before and after the pilot.
Checks before comparison
- Attempts deduplicated by order
- Customer reschedules classified separately
- Bad-address and access failures retained
- Proof-of-delivery events reconciled
Common measurement errors
- Dividing by all scan events
- Deleting failed attempts after redelivery succeeds
- Blaming the route model for data-quality failures without evidence
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
What should accompany this rate?
Use failure-reason counts, redelivery cost, on-time rate, and route segment. The percentage alone does not identify why the first attempt failed.
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.