Fleet managers do not have a data problem. They have a too-much-data problem. Telematics, TMS, ERP, delivery apps: every system generates metrics, dashboards, reports. The result, paradoxically, is that nobody looks at anything carefully, or they end up focusing on the wrong indicators.
Industry research confirms that data overload is the primary pain point for fleet managers. Not because data is useless, but because without a clear hierarchy of indicators, it becomes impossible to separate signal from noise.
This article isolates the 7 KPIs that truly matter in last-mile delivery fleet management, explains how to interpret them, and provides the benchmarks to aim for.
1. Cost per delivery
The most direct KPI, and often the most overlooked in its entirety. Cost per delivery is not just fuel and tolls: it includes vehicle depreciation, driver labor cost, administrative overhead, the potential cost of a redelivery attempt, and the share of fixed costs allocated per stop.
How to calculate it: total fleet costs for the period / number of successfully completed deliveries.
Benchmark: for last-mile operations in urban and suburban areas, a competitive cost per delivery falls between 6 and 12 euros, depending on area density and goods type. Values consistently above 15 euros almost always indicate structural inefficiencies.
How to improve it: route optimization is the primary lever. Reducing deadhead kilometers and increasing the number of stops per route lowers the unit cost directly. Those interested in the return on investment of route optimization software will find concrete figures to work with.
2. First-attempt delivery rate
How many deliveries are completed without requiring a second attempt? This indicator measures the operational effectiveness of the entire chain: from address quality to time-window planning, to communication with the recipient.
How to calculate it: deliveries completed on first attempt / total planned deliveries x 100.
Benchmark: top-performing fleets exceed 95%. The industry average sits around 90-92%. Every percentage point below 92% represents an avoidable, measurable cost.
How to improve it: the causes of failed deliveries are often predictable. Incomplete addresses, time windows incompatible with recipient habits, lack of specific access instructions. Those who systematically analyze the reasons behind each failed first-attempt delivery discover recurring patterns that can be addressed.
3. Km driven vs optimal km
The ratio between the kilometers actually driven by the fleet and those that an optimization algorithm would calculate as the ideal route. This KPI measures how far current planning deviates from theoretical efficiency.
How to calculate it: total km driven / km calculated by optimized route x 100. A value of 100% indicates perfect adherence to the optimal plan.
Benchmark: values between 105% and 110% are physiological (deviations due to traffic, road closures, unexpected events). Above 115%, there are significant improvement margins. Above 125%, you are simply burning fuel and time.
How to improve it: the first step is visibility. Without an optimization system that calculates the ideal route, this KPI is not even measurable. The second step is analyzing deviations: are they due to traffic, driver choices, or planning that does not account for real conditions? Historical data and predictive models allow many of these variables to be anticipated.
4. Vehicle utilization (%)
What percentage of available capacity is actually being used? A 12-cubic-meter van that goes out with 4 cubic meters of goods represents a fixed cost working at a third of its potential. Likewise, a vehicle sitting idle in the depot is immobilized capital.
How to calculate it: two dimensions to monitor in parallel.
- Volume/weight utilization: volume (or weight) carried / maximum capacity x 100.
- Time utilization: hours or days of actual use / hours or days available x 100.
Benchmark: volume utilization above 80% is a reasonable target. Time utilization depends on the operating model, but dropping below 70% on a weekly basis suggests potential fleet oversizing.
How to improve it: the key is intelligent matching between orders and vehicles. A planning system that simultaneously considers volume, weight, time windows and delivery zones can maximize the load of each vehicle, reducing the number of vehicles needed.
5. Fuel consumption per km
With fuel typically representing 25-30% of a fleet’s operating costs, monitoring consumption per kilometer is not optional. It is a necessity. And for those managing mixed fleets with electric vehicles, the KPI extends to energy consumption per km (kWh/km).
How to calculate it: liters (or kWh) consumed / km driven. Monitor by individual vehicle and by individual driver.
Benchmark: for medium-class diesel vans in urban operations, consumption of 10-12 l/100km is the norm. Values consistently above 14 l/100km for the same vehicle type indicate maintenance issues, driving style problems, or route planning inefficiencies.
How to improve it: three main levers. The first is route optimization (fewer km = less fuel). The second is driver training (harsh acceleration, high speeds, idling). The third is preventive maintenance (tire pressure, engine condition). For fleets transitioning to electric vehicles, CSRD reporting requirements already mandate granular tracking of these metrics.
6. On-time delivery rate (%)
In a market where delivery windows are becoming increasingly tight, punctuality is no longer a “nice to have.” It is a contractual requirement in B2B and a retention factor in B2C. This KPI measures the fleet’s ability to meet agreed time windows.
How to calculate it: deliveries made within the agreed time window / total deliveries x 100.
Benchmark: the minimum target in B2B is 95%. In B2C, with wider windows, 92% is generally acceptable. Below 90%, the problem becomes visible to customers and starts generating complaints.
How to improve it: punctuality depends on planning quality. Overestimating or underestimating travel times, not accounting for traffic variability by time of day, allocating too many deliveries per route: these are all planning errors that a system based on historical data and real-time updates can correct.
7. Average time per stop
How long does a driver take on average to complete a single delivery, from the moment of arrival at the address to the moment of departure? This KPI is often underestimated, but it has an enormous impact on fleet productive capacity.
How to calculate it: sum of stop times / total number of stops. Exclude regulatory breaks.
Benchmark: for standard B2C deliveries (parcels, e-commerce), the target is 5-8 minutes per stop. For B2B deliveries with unloading and verification, 15-25 minutes. Values consistently above the benchmark indicate process problems (paper documents, searching for the recipient, complicated access points).
How to improve it: time per stop is reduced with better information available to the driver (access instructions, recipient contact, delivery notes) and with digitization of processes at the delivery point. A digital Proof of Delivery system, for example, eliminates paper form completion and speeds up stop closure.
From measurement to action
The value of these 7 KPIs does not lie in measurement for its own sake. It lies in the ability to turn numbers into operational decisions. A fleet manager who consistently monitors these metrics knows where to intervene, with what priority, and can measure the effect of every change.
The practical advice is to start with the KPIs where the gap from the benchmark is widest. If the first-attempt delivery rate is at 85%, that is the priority. If fuel consumption is off the scale, start there. You do not need to improve everything simultaneously: you need to know where you are losing the most and focus resources on that point.
The data is there. The difference is made by those who read it the right way.