Logistics KPIs aren’t “a spreadsheet for management.” They’re a way to tell a real problem from a vague impression. Without metrics, logistics looks like the weather: “it’s stormy today, tomorrow will be better.” With metrics, you can see exactly where it’s storming: in the warehouse (picking errors), on the linehaul (delays), on the last mile (NDR), or in inventory management (extra storage days and frozen cash).
The most common mistake is measuring everything at once and improving whatever “shouts the loudest.” You end up with a lot of KPIs, little meaning, a tired team, and still an unhappy customer. Below is a practical set of key indicators (including OTIF, SLA, and turnover), how to calculate them, which side effects to watch for, and which actions usually improve results without self-deception.
1) OTIF (On Time In Full)
What it means: the share of orders delivered on time and in full.
How it’s measured: OTIF = (number of orders “on time and complete” / total number of orders) × 100%. “On time” is taken from the customer promise or the SLA.
Why it matters: OTIF is the most “honest” customer-facing metric. You can be fast but miss items — OTIF drops. You can be accurate but late — OTIF drops too. It doesn’t let you make pretty excuses.
2) SLA (Service Level Agreement)
What it means: a set of agreed service parameters: lead times, time windows, response time, notification rules, and claims procedure.
How it’s measured: by the share of obligations met and by specific time/percent targets (e.g., “incident response ≤ 30 minutes”).
Why it matters: SLA turns “quality” into numbers and makes expectations the same for all parties.
3) Inventory turnover and Days of Inventory (DOI)
What it means: turnover shows how quickly inventory turns into sales; DOI shows how many days of stock you have on hand.
How it’s measured: a common approach is DOI = (Average inventory / Average daily usage) in days. Turnover = Sales/COGS for the period ÷ Average inventory (depending on methodology).
Why it matters: logistics isn’t only “deliver it,” but also “don’t hold too much.” Excess inventory means frozen money, warehousing costs, and the risk of dead stock.
4) Picking accuracy
What it means: the share of orders picked without errors in SKU/quantity/batch.
How it’s measured: % of error-free orders or errors per 1,000 order lines.
Why it matters: warehouse errors often get “masked” as delivery issues, but they’re fixed in the warehouse.
5) Fill rate
What it means: the share of demand you were able to fulfill from available stock (without shortages).
How it’s measured: shipped / ordered (in units, order lines, or value).
Why it matters: you can have excellent delivery, but if the item is out of stock, the customer still won’t get the order.
6) NDR / Failed delivery rate
What it means: the share of deliveries that didn’t happen on the first attempt (or didn’t happen at all).
How it’s measured: % of failed deliveries and the reasons (no answer, wrong address, reschedule, refusal).
Why it matters: NDR directly increases last-mile cost and reduces OTIF.
| KPI | What it shows | Where it usually “breaks” |
|---|---|---|
| OTIF | Service quality for the customer | Warehouse (completeness), delivery (lead time) |
| SLA compliance | Predictability and controllability | Incidents, notifications, time windows |
| DOI / turnover | Inventory efficiency | Planning, purchasing, “dead” SKUs |
| Picking accuracy | Warehouse quality | Locations, labeling, control |
| Fill rate | Product availability | Inaccurate stock, shortages |
“Minimum KPI set” (so you can start without drowning)
When it fits: small/medium businesses, logistics is growing, data is limited, processes aren’t fully formalized yet.
Pros: quick to implement; creates focus; easy to explain to the team.
Limitations: won’t show subtle root causes; you’ll need to go deeper after stabilization.
Risks: measuring KPIs but not analyzing causes. KPIs without causes are like a thermometer without a doctor.
Set: OTIF, NDR, picking accuracy, DOI (days of inventory), cost per successful delivery.
“KPI system across the chain” (warehouse → transport → last mile → inventory)
When it fits: multiple warehouses/carriers, complex geography, strong seasonality, many sales channels.
Pros: transparency at handoffs; easier contractor management; you can improve with precision.
Limitations: requires better data capture and unified statuses.
Risks: too many metrics and “war for the numbers” if there is no single logic.
Scenario 1: baseline — OTIF at 82%, complaints about “late” and “wrong items.” Actions — decomposed OTIF by reasons, started tracking picking accuracy and NDR separately. It turned out: half of OTIF failures came from the warehouse (mis-picks and missing items), not delivery. Result — after implementing location-based storage and a packing verification step, OTIF improved without changing the carrier. We’ve worked in this field for over 13 years, and this is a classic: people replace the courier when the warehouse is the one that hurts.
Scenario 2: baseline — high DOI, a packed warehouse, cash frozen in inventory. Actions — identified slow movers, introduced clearance/assortment optimization, and for fast movers switched to more frequent replenishment in smaller batches. Result — days of inventory decreased, the warehouse unloaded, and fill rate didn’t drop. Important: reducing inventory must be done carefully, otherwise you just replace “excess” with “stockouts.”
1) What’s more important: OTIF or SLA?
They’re connected. SLA defines the rules of the game (what counts as “on time,” which windows, what happens during incidents), and OTIF shows how well you actually keep the promise. Without SLA, OTIF is hard to calculate; without OTIF, SLA stays paper-only.
2) How do you improve turnover without losing sales?
Do it together with fill rate: reduce days of inventory for “dead” and slow SKUs, and for fast movers improve forecasting and replenishment. The point is not “less inventory at any cost,” but “less excess, more of what’s needed.”
3) Which KPI most often gives a quick effect?
Picking accuracy and NDR. They directly affect OTIF and last-mile cost. Sometimes fixing labeling, location management, and address/contact data quality is enough for the numbers to noticeably improve even without major investments.