Most fleets are underutilized — the inefficiency
is just hard to see
Empty cubic space, low stop density, inconsistent route planning and rigid mode rules quietly erode productivity every day, even when operations appear to be running smoothly. OneRail helps shippers uncover hidden inefficiency and turn it into measurable gains in utilization, cost and service performance.
Optimize around the constraints that matter
Unlike traditional routing tools, OneRail’s AI Route Optimization contemplates many more constraints that reflect how your business actually operates. From fulfillment node logic and asset availability to customer promise, geography, order mix and delivery requirements, OneRail adapts routing decisions to your network, your operating model and your end-customers.
There is no average delivery stop
Every stop carries its own variables that affect timing, complexity and cost. OneRail’s AI Route Optimization accounts for real-world conditions like business type, demand cycles, traffic and drop-off complexity to build routes that reflect how delivery actually happens.
Optimize for profit per stop, not just cost per stop
Traditional routing models were built to reduce cost per stop. But in complex delivery networks, the lowest-cost stop is not always the best business decision. With more constraints, more data and AI capable of evaluating them together, shippers can now optimize for a more meaningful outcome: Profit per stop.
OneRail helps customers move beyond one-dimensional routing logic by considering the variables that actually impact profitability at the stop level. That includes customer margin profile, order characteristics, time-of-day complexity, delivery conditions, dwell time, traffic, service requirements and the unique realities of each business segment. The result is a routing decision that does more than lower cost — it helps protect margin, improve execution and prioritize the stops that create the most value.
Why this matters
Not all stops are equally profitableSome customers drive high volume but lower margin, while others generate stronger margin on fewer deliveries. Routing should reflect that difference.
Not all stops cost the same to serveTime of day, parking access, traffic, unloading conditions, labor availability and site congestion can dramatically change the true cost of a stop.
AI can evaluate more constraints in real timeTraditional routing relies on static assumptions. OneRail’s AI-native approach can weigh many more variables at once to improve decision quality.
The best route is not always the cheapest routeA slightly higher transportation cost may produce a better business outcome if it supports a higher-margin customer or reduces delivery complexity.
Optimization should improve margin, not just efficiencyProfit per stop helps align routing decisions with the financial realities of the business, not just miles and minutes.
How OneRail thinks differently
OneRail’s AI Optimized Routing is designed to reflect the fact that every stop has a different economic profile.
Instead of treating all deliveries as equal, OneRail helps shippers evaluate the interaction between:
- Customer Profitability
- Purchase Frequency
- Order Value & Order Mix
- Stop Complexity
- Dwell Time
- Service Windows
- Business Type
- Time-of-Day Traffic Patterns
- Fulfillment Origin
- Route Productivity
- SLA Requirements
A more advanced routing strategy does more than reduce route cost;
it maximizes route contribution.
Case in Point: Food Wholesale Distribution
Consider a food wholesaler serving two very different restaurant accounts.
A high-volume casual dining chain may be a lower-margin customer. Delivering there at 12 p.m. could be operationally expensive because the restaurant is busy, the parking lot is full, staff are occupied and unloading takes longer. Even if that stop fits neatly into a route, it may create a high cost-to-serve relative to the margin earned on the account.
Now compare that to an independently owned steakhouse with a stronger margin profile. That customer may be closed during lunch hours, making midday delivery far easier and less disruptive. The truck can access the site more easily, unloading is faster and the stop requires less effort. In that case, the delivery creates a better economic outcome because the stop is both higher margin and lower complexity.
This scenario illustrates: the difference between optimizing for
cost per stop and optimizing for profit per stop.
Key Benefits of AI Optimized Routing:
Prioritize the stops that create the most value
Align routing decisions with customer margin, not just geography.
Reduce the hidden cost of complex deliveries
Account for traffic, congestion, dwell time and site conditions before they erode profitability.
Make smarter time-of-day decisions
Match delivery timing to the operational realities of each customer.
Reflect the economics of each business segment
Optimize differently for restaurants, job sites, retailers, commercial accounts and other customer types.
Use AI to evaluate more variables than static routing can handle
Improve decisions by considering the real-world constraints that affect service and margin.
Turn routing into a profitability lever
Move beyond efficiency metrics and make delivery decisions that improve contribution at the stop level.
Fleet Utilization,
Minus the Guesswork
Most fleets aren’t overbuilt or underbuilt all the time — they’re right-sized for the typical day and strained during peaks. OneRail helps you operate in that sweet spot, running your fleet at high productivity most days, and for the inevitable shortfall, making smarter decisions about how to flex without wasting money on excess vehicles, the wrong vehicle mix or redundant coverage across locations.
OneRail helps you get more out of every vehicle
Right-size your fleet for your 80% day, and flex intelligently for the rest
OneRail uses last mile AI + Data Science to estimate the fleet capacity you need to cover the bulk of demand — the vehicles, people and operating plan that keep utilization high — and then helps decide what to do when you’re outside that range.
What this looks like:
- Knowing the right baseline fleet size for day-to-day demand
- Avoiding over-investment in vehicles that sit idle when demand normalizes
- Making informed flex decisions for peak days, instead of reactively shelling out
Plan holistically across your network, not store-by-storeFleet utilization breaks when planning is siloed. For instance, two nearby stores might each need a truck on paper — but when a truck is shared, an optimized plan could cover both routes efficiently.
OneRail looks at the fleet across locations as one system, and helps align:
- Route density & overlap (especially when routes are close together)
- Vehicle type & capacity (box truck vs. sedan) to reduce waste
- Coverage decisions across delivery radiuses that shift with demand
Why AI improves utilization, reducing costly mistakesTraditional approaches often rely on static rules — like fixed zip codes, delivery radiuses or statements like, “This store always covers this area.” Problem is, orders aren’t static. Demand shifts daily, and is prone to human error.
OneRail’s native AI helps by:
- Evaluating far more feasible permutations than a person can in the time available
- Learning which factors actually matter
- Ignoring noise that isn’t predictive
- Updating recommendations as execution data accumulates
Maximizing your fleet ops
Think of your fleet like a church on Sunday: You can’t size it for the rare overflow moments without wasting money the rest of the week, so OneRail AI helps you find the sweet spot (often ~80% coverage), where your vehicles run at peak efficiency most days, proactively managing the inevitable capacity shortfalls and mix decisions (box trucks vs. sedans) that otherwise create hidden waste.
With OneRail, your decisions get smarter over time, with fewer planning mistakes, better use of your owned fleet and more confident commands when you need to flex — with the final call still in the hands of an informed operator.
Optimization Analysis
OneRail’s analysis benchmarks the true cost of your current fleet operation, using route data, vehicle specs, pricing, delivery windows, SLA rules and manual routing behavior to uncover hidden inefficiencies. It then applies AI Optimized Routing to model a future-state operation — quantifying projected improvements in utilization, route time, asset needs and cost per stop.
More than utilization measurement
The analysis does more than measure utilization. It exposes how manual route edits — also known as route exceptions — and loosely governed exceptions can increase cost, reduce route density and create at-risk deliveries that do not improve SLA performance. Shippers can then compare the unoptimized baseline against a data-backed optimized scenario. It also measures route history, vehicle capacities, pricing, delivery windows, stop durations, item dimensions, available assets, business rules and telematics.
