Km per delivery (also km/stop) is distance driven divided by the number of deliveries: how capillary the route is. In Mexico–U.S. distribution it is the key performance indicator (KPI) for distance efficiency — distinct from drop size (kg per visit) and OTIF (on time and in full).
- route efficiency formula
- km ÷ stops
- that is drop size
- ≠ kg/stop
- that is load density
- ≠ kg/km
- no 99% target here
- not a %
Cluster: distribution KPIs · drop size · distribution costs · OTIF.
What km per delivery is
In one sentence: how many kilometers each stop “costs” on average. If you drive 480 km and make 40 deliveries, km per delivery is 12. If the same day you make only 20 deliveries in 480 km, it rises to 24 km/stop: the route became more capillary or worse sequenced.
What it is not: a service percentage, a synonym for drop size, or tractor load density (kg per kilometer). Mixing units is the fastest path to a dashboard leadership celebrates and the customer does not recognize.
Formula and units
Numerator: kilometers driven in the period or route (include empty miles if your rule says so — document it). Denominator: number of deliveries / stops / contacts completed under the same rule.
Km per delivery
Formula: km driven ÷ number of deliveries
Unit: km/stop
Question it answers: How capillary is the route?
Formula: kg (or m³) ÷ number of deliveries
Unit: kg/stop
Question it answers: How much do I leave per visit?
Load density
Formula: kg delivered ÷ km driven
Unit: kg/km
Question it answers: How much weight per kilometer?
MXN per delivery
Formula: freight (+accessorials) ÷ deliveries
Unit: MXN/stop
Question it answers: How much does each visit cost?
Illustrative example (calibrate): 480 km ÷ 40 stops = 12 km/delivery. Same 40 stops with 11,400 kg to drop size 285 kg/stop. Same day, two KPIs.
Km/delivery vs drop size vs OTIF
A healthy dashboard shows all three without mixed targets. Strong service does not forgive endless routes; short routes do not forgive incomplete orders.
Km / delivery
- km ÷ stops
- Lever: sequence and densify
- Typical fail: % target
Drop size
- kg ÷ stops
- Lever: consolidate visits
- Typical fail: ignore OTIF
| Joint signal | Read | What not to do |
|---|---|---|
| High km/delivery + low drop size | Capillary route and tiny stops | Only inflate OTIF in the spreadsheet |
| Low km/delivery + low OTIF | Dense route but broken service | Celebrate km “efficiency” |
| High km/delivery + high OTIF | You fulfill far away: review MXN/delivery | Freeze the network without looking at cost |
| High drop size + high km/delivery | Few stops but far | Assume “consolidate” already solved it |
How to read it in Mexico and the corridor
An “acceptable” km/delivery in dense Bajío urban delivery is not the same as a Monterrey–Laredo corridor with few long stops. Segment by network type: plant-to-DC, regional distribution, B2B last mile.
Service context (so you do not mix): the 4th National Logistics Indicators Study 2026 (#SoyLogístico / LDM / EGADE) reports ~93% average delivery fill rate in its sample. That figure coexists with density and distance KPIs in the same business — but it does not turn km/delivery into a percentage.
Dock dwell and missed appointments inflate km/delivery when redeliveries happen: the odometer grows and the “successful delivery” denominator shrinks. Cross with distribution costs (MXN/km and MXN/delivery).
When to attack km per delivery
Lower km/delivery when route cost and time justify it — not when someone wants a “greener KPI” without a unit. Illustrative table; calibrate.
Km/delivery ↑ and MXN/delivery ↑
Typical action: Re-sequence; densify windows
KPIs to cross: Drop size, empty %, OTIF
Risk: Cut stops and break SLA
Km/delivery ↑ but OTIF stable
Typical action: Review empty miles and cold returns
KPIs to cross: Empty km, geofence, TMS
Risk: Optimize the map and lose evidence
Km/delivery ↓ and OTIF ↓
Typical action: Do not celebrate “efficiency”
KPIs to cross: OTIF, appointments, POD
Risk: Short route with broken orders
New customer far from the cluster
Typical action: Price / frequency / drop size
KPIs to cross: MXN/kg, SLA
Risk: Absorb km without renegotiating
Fleet vs 3PL in dispute
Typical action: Same formula on both
KPIs to cross: Dispatch CV, trip file
Risk: Comparing apples to kg
Unit mistakes (the costly ones)
These are the failures most often seen on distribution dashboards copied from generic posts. Fix them before you set targets.
Reporting kg/delivery as “route efficiency”
Why it hurts: That is drop size; you hide real capillarity
Correction: Separate drop size (kg) and km/delivery (km)
“99% world-class” target on km/delivery
Why it hurts: You invent a % where there are only kilometers
Correction: Target in km/stop or MXN/stop — not %
Using kg in the “efficiency” denominator
Why it hurts: You mix weight with distance
Correction: Denominator = deliveries; numerator = km
Ignoring empty km in the numerator
Why it hurts: You understate true network cost
Correction: Document whether empty miles are in or separate
Single national average
Why it hurts: Urban saves the long corridor in the mean
Correction: Segment by corridor and delivery type
Counting failed attempts as deliveries
Why it hurts: You artificially lower km/delivery
Correction: Successful delivery = POD / closed appointment
Operable checklist
For tower, route planning, and the third-party logistics (3PL) partner. If the formula is not written down, the number is not a KPI — it is a rumor.
Measurement order
From odometer to decision
Define
Km and stops
Segment
Corridor · network
Cross
Drop · OTIF · MXN
Act
Sequence · empty
Elige un paso para ver el detalle
Detalle del paso · 01
Formula in writing
OCL and route efficiency
OCL Cargo is an autonomous TMS with AI agents: it ties GPS milestones, stops, and documents to the trip file so km/delivery comes from the real route — not an average drawn in WhatsApp. When fiscal scope applies, OCL can stamp invoices and Carta Porte. Your team decides exceptions.
Typical 6–8 week corridor pilot; auditing 100% of the flow, the published recovery pattern is 5–7% of freight spend — relevant when “invisible” km (redeliveries, unreported empty miles) hid in accessorial lines. Hub: distribution KPIs.
Key takeaways6 points
- Km per delivery = km driven ÷ number of deliveries — route distance efficiency.
- Distinct from drop size (kg/stop) and load density (kg/km).
- Do not hang a “99% world-class” service target (OTIF/fill) on a kilometer KPI.
- High km/delivery signals sparse routes or poor sequence — do not “fix” it by inflating OTIF in the spreadsheet.
- Measure by corridor; a national average hides the lane that breaks MXN/delivery.
- OCL ties GPS and stops to the trip file so the route key performance indicator (KPI) is auditable.
Want km/delivery with correct units — not an invented 99%?
Related reading
Frequently asked questions
It is kilometers driven ÷ number of deliveries (stops). It measures how capillary or distance-efficient the route is — not how many kg you leave per visit, and not whether the order arrived on time and in full.
No. Drop size is kg (or volume) ÷ stops. Km per delivery is km ÷ stops. They are cousins: together they explain cost per delivery; they are not synonyms.
It does not make sense. Km/delivery is an average distance, not a service percentage. “99% world-class” usually comes from OTIF or fill rate — hanging it on km/stop is a units error.
Kg/km answers how much weight you move per kilometer (trip load density). Km/delivery answers how many kilometers each stop “costs.” Both are useful; do not swap them on the same dashboard row.
It depends on the corridor: dense urban vs plant-to-DC vs B2B last mile. Do not copy a single national average. Use the distribution KPIs hub and calibrate to your network.
OCL is an autonomous TMS: it ties GPS, stop sequence, and the trip file to the same ID so km/delivery comes from route facts — not a chat. Agents operate portals; your team handles exceptions.