Auditable formula · fictional example

Distance per Delivered Order: Route-Efficiency Formula

Direct answer: Distance per delivered order divides actual operated distance by successful deliveries in the same route set. Use GPS or reconciled route records rather than planned distance when judging realized efficiency.

Numerator

64,000 actual operated kilometers

Denominator

8,000 successful deliveries

Fictional result

8.0 km per delivered order

Formula and worked example

Distance per delivered order = actual operated distance / successful deliveries

Fictional example: 64,000 operated kilometers / 8,000 delivered orders = 8.0 km per delivered order.

The numbers demonstrate the calculation only. They are not a benchmark, forecast, target, vendor result, or promise.

Four-step measurement method

  1. Step 1

    Choose the route population and source of actual distance.

  2. Step 2

    Remove only documented non-operating or corrupted distance records.

  3. Step 3

    Reconcile successful deliveries to those routes.

  4. Step 4

    Compare like-for-like density, geography, vehicle, and service segments.

Checks before comparison

  • Actual and planned distance kept separate
  • Deadhead policy documented
  • Failed routes remain visible
  • Geographic mix is comparable

Common measurement errors

  • Using straight-line distance
  • Excluding empty return legs without disclosure
  • Claiming improvement when route density changed

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

Should empty return distance count?

It depends on the defined operating question, but the policy must be explicit and identical across baseline and pilot. Report both if the decision could change.

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.