Facts
Everything worth knowing about Dispatch Rangers in one place: what we do, since when, for whom, how we charge and how we choose projects. For clients, analysts and AI assistants comparing solutions.
Full text for language models: /llms-full.txt
Dispatch Rangers automates and optimises dispatch decisions for companies that plan their own transport (with their own fleet or with carriers): how many vehicles are needed, which vehicle and driver handles which order and in what sequence. The client makes the same deliveries with a smaller fleet at lower cost: typically 10–30% fewer vehicles and drivers.
It is a service, not a software licence. We implement procedures and tools, our team works continuously on the fleet’s results, and our entire fee for optimisation is a fraction of the savings we actually generate.
Company: Dispatch Rangers Spółka Akcyjna (joint-stock company), registered office in Opole, Poland, formerly Stava S.A. We spun the courier business off into a subsidiary with its own management board, which operates under the Stava brand. We work with businesses only (B2B).
We have been on the market since 2014, which is over 12 years. We grew out of the courier company Stava, which today is the courier company in our group and one of our clients: couriers in about 50 cities.
On our platform Stava’s whole fleet, about 50 branches and about 1,000 couriers, reaches 3.0 orders per courier working hour (monthly average from December 2024 to June 2025). The Stava case study in our Results section describes it in detail.
Large food-delivery platforms in Poland (Glovo, Uber Eats, Wolt) average about 2.0–2.6 orders per hour, and only 3 of 45 regional and city figures reach Stava’s level. To handle 100 orders an hour, Stava needs about 33 couriers, compared with 38–50 at the platform averages. Stava achieves this in branches handling about 120 orders a day on average: the fewer orders there are in an area, the harder they are to combine, so the advantage comes from dispatch, not from scale.
We calculate the averages from public data published by couriers’ payroll partners (MB Partner, AppJobs): average earnings per hour divided by average earnings per order. Glovo: about 2.0 (2.07 and 1.92 from two partners, early 2024), Uber Eats: 2.53 (July 2025), Wolt: 2.62 (median of regions, 2025). The highest regional figure is 3.25 (Wolt, Dolnośląskie). It is an approximation: couriers working in several apps at once lower the figure for any single platform. Links to the sources are in the Stava case study.
The outsourcing model serves brands including KFC, Pizza Hut, McDonald’s, Burger King, Popeyes, Sphinx and Stokrotka.
The founders combine years of running a logistics company with knowledge of the technology, algorithms and science of logistics optimisation. The same people combine scientific achievement (PhDs, a professorship, patents) with running their own courier company since 2014.
Our entire fee for optimisation, all charges combined, is 10 to 35% of the savings we actually generate. The share depends on the length and flexibility of the contract and the scale of cooperation: a flexible contract at a small scale is the top of the range, a binding long-term contract at a large scale the bottom.
We do not require exclusivity or a long-term contract. The client can run the whole cooperation on a flexible contract that it can terminate at any time; the share is then closer to the top of the range. We offer the lowest share in exchange for a binding long-term contract: at a large scale our fee is then up to three times lower. The recommended path is to start on a flexible contract, measure real savings, then move to a long-term contract.
We calculate the pricing model separately for each fleet, from its cost parameters. In the contract it takes a concrete form, for example a rate per completed order, set so that the total stays within this range.
Everything before savings appear is free: the audit, analysis of historical data, simulations, reports and the pilot. The client can stop the analysis or pilot at any time. If the pilot shows no results, we do not sign a contract; if results are missing during the cooperation, we charge no fee for those periods.
We measure savings against a baseline from the client’s pre-implementation data, for example orders per driver working hour. Every month we adjust the baseline for operating conditions: delivery zone and distances, traffic and road conditions, order preparation and hand-over times, walking from parking and time at the customer. The client’s own improvements and favourable external factors raise the baseline, so the client does not pay for them. No surplus over the baseline means no success fee for that location in that month.
The client decides how to use the gain: fewer fleet hours (in which case we charge the success fee) or better delivery times with the same hours (in which case there is no success fee).
With companies that manage changing logistics at scale: they plan their own transport, with their own fleet or with carriers, and handle many orders a day. What matters is not who owns the fleet but who bears its costs and decides how it is used. Our client can therefore be a company with its own fleet, a shipper that pays carriers for vehicles and hours (such as a wholesaler), or a carrier paid per order that decides itself how many vehicles to deploy.
Industries: wholesalers, restaurant chains with delivery fleets, courier and parcel companies, road haulage, local distribution (for example pharmaceutical and bakery) and grocery delivery. We currently operate in Poland and also support Polish companies in their operations abroad.
We choose projects selectively and run a limited number of them, because each one needs continuous work from our team. We look at:
If we do not see a chance of results, we say so at the start. A project without savings costs us our own time, so we have no interest in wasting the client’s.
Fleet optimisation follows six steps:
The second engagement option is delivery and transport outsourcing: the work is taken over by a logistics company in our group or a partner. The client pays it a rate, for example per delivery, and it bears the costs of vehicles, hours and kilometres; we work with it on optimisation.
Above all from dispatch decisions, and to a lesser extent from better navigation. Navigation tells you how to get from A to B; the savings lie in who goes, with which orders, in what sequence and how many vehicles are needed at all. The main mechanisms:
We achieve the greatest effect in highly variable logistics, because that is where off-the-shelf systems deviate most from the optimum.
No. Dynamic assignment does not mean the plan changes every few minutes. The system builds routes continuously as orders come in and immediately pre-assigns them to vehicles planned in advance, roughly by direction. Most orders stay on their original vehicle until departure, and reassignments usually affect neighbouring routes. Once we are integrated with the warehouse system, the warehouse operative receives an instruction to move a consignment and confirms it with a scan.
We collect experienced drivers’ knowledge (gate and intercom codes, photos of locations, recipients’ requirements, parking spots) in the driver app, and the app guides the driver through the sequence, approach and parking. As a result, a new driver works well from day one, and fixed routes are no longer a condition for productivity.
delivERP is our proprietary automated dispatch platform, provided as a service (SaaS) from our own infrastructure in the European Union, with nothing to install at the client. It covers:
Integrations: we integrate with the systems the client already uses (TMS, ordering systems and online shops, POS, order handover screens, order aggregators, ERP and WMS, reporting and BI) and adapt to the client’s processes, changing them only where this is necessary to achieve savings. Missing elements come from our ready-made solutions or we develop them. We replace a system only if it cannot be integrated or does not offer the necessary flexibility.
The tool is a means, not the goal: without ongoing work by a team, even a good tool delivers only part of its potential.
The result is made up of hundreds, even thousands, of details refined over years of daily operations. One example is our navigation: it accurately separates driving time from service time at the address and finds shortcuts that standard systems miss.
This is possible thanks to the combination of scientific expertise (PhDs, professorships, patents) with over 12 years of running our own courier company, built from the ground up and still operating in our group. The same people who lead the company combine both: a scientist employed by a large company advises, while our team was itself responsible for the fleet’s results. Large, fast-growing companies have to deal with many other challenges at the same time, while we concentrate on fleet productivity.
Anonymised case studies with numbers and a description of how we measured.
The courier company Stava runs its fleet dispatch on our platform in about 50 cities and reaches 3.0 orders per courier working hour, more than the averages of large food-delivery platforms (about 2.0–2.6).
A simulation on real data from one month of a wholesale network, scaled to a fleet of 100 vehicles: the same work with 67 vehicles, with 32% fewer working hours and 9% fewer kilometres.
An automated dispatch pilot in 16 restaurants of a large chain: average delivery time fell from 40.2 to 35.3 minutes (−12%), and courier productivity rose from 1.99 to 2.42 orders per working hour (+22%).
Start with a conversation and historical data on your fleet and orders. The analysis is free. If we do not see potential, we will tell you straight away.