Use cases

Use cases from large logistics networks

FLEET

Linehaul networks · LTL / FTL · 5 casesExplore FLEET →
FLEET · Case 1 of 5Network geometry optimizationNext case ↓↑ Top
United States · National

Hub relocation saves $1.5M a year

Challenge
In a linehaul network, the hub locations determine the routes, the number of trucks and the transit time of every shipment. Customer acquisition, customer attrition and market dynamics change freight flows over time. A hub network without regular reevaluation risks misalignment with the new flows, at a cost carried by every route and every truck.
Approach
FLEET is a bottom-up, high-fidelity simulator of the linehaul network. FLEET simulates every shipment, including the route the shipment takes, the time the shipment waits for consolidation, and the choice between a weekly scheduled and an ad-hoc truck. Planners use FLEET to evaluate alternative network geometries, including hub relocations, on realistic costs rather than on shortest paths or top-down metrics.
Result
FLEET works as a flight simulator for the network. Planners can run scenarios on new network geometries in a safe environment that stays close to actual operation, before committing to a relocation. On a national US network, one hub relocation saved $1.5M a year, and the operations team implemented the recommendation with confidence.
$1.5Msavings per year
1 hubrelocated, all other hubs unchanged
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0
candidate sites evaluated
0
stations re-attached
—
savings per year
FLEET · Case 2 of 5Network operationNext case ↓↑ Top
United States · National

Saving $1M a year with the right balance of weekly scheduled and ad-hoc departures

Challenge
Weekly scheduled capacity is committed before the freight is known. Scheduling too many weekly departures leaves trucks poorly loaded. Scheduling too few forces ad-hoc trucks at premium rates. Either way, linehaul spend leaks week after week.
Approach
Teranalytics built FLEET to find the optimal mix of scheduled and ad-hoc capacity. A proprietary FLEET algorithm plans a weekly departure when enough recurring freight justifies such commitment, and dispatches ad-hoc trucks for the remaining freight. Every operating constraint holds: station hours, trailer capacity, carry-over rules, service levels and truck repositioning.
Result
FLEET delivers the optimal weekly schedule, simulated shipment by shipment, with realistic cost, on-time performance and load factor. Planners can easily deep-dive into the solution, audit the network behavior, and build confidence in the recommendations provided. On a national US network, the new schedules saved $1M a year, with better use of company drivers and fewer contract drivers.
$1Msavings per year
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Scheduled truck, by load factorAd-hoc lane and truck, on callScheduled laneHub
0%
freight on scheduled trucks
0
ad-hoc trucks on the map
0 of 3
KPIs on target
FLEET · Case 3 of 5Network operationNext case ↓↑ Top
Europe · United States

Hub bypassing: the right balance between cost, service and load factor

Challenge
Consolidating freight at hubs fills trucks but adds handling and transit time. Sending freight direct speeds delivery but lowers load factors. Each network has its own best balance between cost, on-time performance and load factor. Without measuring the effect across the whole network, the balance remains a matter of judgment, and the network pays in extra trucks or late deliveries.
Approach
FLEET gives planners a bypass knob that shifts the network between hub consolidation and direct trucks. Every position of the knob triggers a full network optimization, and FLEET reports the effect on cost, on-time performance and load factor. The Teranalytics AI planner can also turn the knob to meet target KPIs.
Result
Planners control the network's performance by varying the knob and measuring the effect on the KPIs that matter to the operations team. A recent application to a large European network revealed how FLEET optimized operations by reducing cost enough to allow operators a more aggressive bypassing strategy.
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Station to hub, on roadsHub-to-hub linehaul, on roadsDirect truck bypassing the hubsHub
0
direct lanes, hub bypassed
—
bypass level
FLEET · Case 4 of 5Network design and operationNext case ↓↑ Top
France · National

A digital twin to price every network lever

Challenge
On an LTL network, consolidation rules, schedules, the routing guide, the hub footprint and carrier rates all interact. A change to any of these levers shifts cost, service and truck count across the whole network. Without a reliable model of the network, the effect of a change becomes visible only after go-live, when customers already feel the consequences.
Approach
FLEET builds a digital twin of the network from raw system extracts. Calibration continues until an hour-by-hour replay of the twin reproduces actual cost, trucks and service. On the calibrated twin, planners change parameters and adjust network behavior to reach the desired KPIs, and every scenario runs as a full network optimization. FLEET also tracks carrier rates lane by lane, and shows whether a rise in cost comes from higher prices or from more freight.
Result
Planners measure the effect of every change on the calibrated twin before the change reaches real trucks, drivers and customers. On a national network in France, retuning consolidation alone raised on-time performance by about ten points at lower cost. A new routing guide and a smaller hub footprint added several million euros a year in savings.
≈ +10 ptson-time, from retuning consolidation alone
Several €Ma year, from a new routing guide and a smaller hub footprint
Explore FLEET →
Today
Step 1
Step 2
Cost
Reference
Lower
Lowest
On-time
Reference
≈ +10 pts
≈ +10 pts
Trucks
Reference
Slightly fewer
More
FLEET · Case 5 of 5LTL and FTL combinedNext: FLEET-D ↓↑ Top
United States · Regional

Using FTL capacity in an LTL network

Challenge
FTL trucks often return empty on corridors where the LTL network pays for separate trucks. The two businesses usually run as separate P&Ls, each paying for capacity the other could use. Combining the two P&Ls requires significant organizational effort, and management needs a credible estimate of the saving before committing.
Approach
Planners use FLEET to add the empty FTL returns to the LTL network as available capacity. On the return trip, an FTL truck can carry LTL freight from a station near the customer site, through the hub, to the station closest to the FTL warehouse. Every scenario runs as a full network optimization, and FLEET decides which LTL trucks the FTL returns can replace, corridor by corridor.
Result
FLEET gives management a credible figure for the saving of a combined LTL and FTL network, before any reorganization begins. Part of the saving can fund a discount for FTL customers who agree to share return trips, while outbound trips remain dedicated to the FTL customer.
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Separate LTL and FTL networks
Combined LTL + FTL network
FTL truck, loadedFTL truck, emptyFTL delivery, loadedFTL leg, emptyFTL return carrying LTL freightLTL lanes station–hub–stationLTL station to hubLTL hub to hub

FLEET-D

Driver routes · Hours of service · 3 casesExplore FLEET-D →
FLEET-D · Case 1 of 3Driver routesNext case ↓↑ Top
United States · National

Rebuilding driver routes at minimum cost

Challenge
In a linehaul network, every weekly schedule needs a driver, and the driver routes weigh on network cost as much as the schedules themselves. Depending on network policy, a route returns the driver to the home station or ends at another station, and every empty leg added to reposition a driver costs miles without freight. Across hundreds of schedules and drivers, the possible combinations of routes far exceed what can be evaluated by hand.
Approach
Planners use FLEET-D to rebuild every driver route from the weekly schedules. FLEET-D assigns each schedule to a solo driver or a two-driver crew, with routes either closed at the home station or left open, as the network allows. Planners can freeze any route that must stay unchanged, and every scenario runs as a full optimization of driving cost and empty miles.
Result
Planners get a complete set of driver routes at the lowest cost, with the timetable of every driver. In an engagement on a national US network, the rebuilt routes lowered weekly driver cost by about 6% compared with the existing routes, with the same number of drivers and about 10% fewer empty miles. The number of drivers subject to California's AB5 rules fell by more than half.
≈ −6%weekly driver cost, same drivers
≈ −10%empty miles
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Solo driver routeTwo-driver crew routeEmpty legFrozen routeTerminal
—
weekly driver cost, index
—
empty miles a week
—
drivers
FLEET-D · Case 2 of 3Driver categoriesNext case ↓↑ Top
United States · National

Replacing third-party drivers

Challenge
Every driver route is assigned to a driver category: company solo drivers, two-driver crews, or third-party drivers such as owner-operators and subhaulers. Third-party drivers offer flexibility but usually cost more per hour. Company drivers come with constraints of their own, such as guaranteed weekly miles, which determine the routes company drivers can take. Assigning routes with each constraint considered separately keeps third-party drivers on routes that company drivers could run at lower cost.
Approach
In FLEET-D, the operations team sets the constraints of every driver category, including cost, working hours and weekly mileage. FLEET-D assigns every route to the category with the lowest total cost across the network, and shows how each constraint shifts work between company and third-party drivers.
Result
FLEET-D delivers a driver plan that keeps third-party drivers only on the routes where third-party capacity is the cheaper option, and makes explicit the constraints that limit company drivers. In an engagement on a national US network, adjusting the weekly mileage constraints moved about two thirds of the scheduled work to company drivers, at about 18% lower driving cost.
≈ 2/3of the scheduled work to company drivers
≈ −18%driving cost
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Company solo driverCompany two-driver crewThird-party driver
—
weekly mileage floor, solo / crew
—
scheduled work on company drivers
—
driving cost, index
FLEET-D · Case 3 of 3ComplianceNext: FLEET-P&D ↓↑ Top
United States · National

Hours-of-service compliance, driver by driver

Challenge
Driver routes in a linehaul network must satisfy company constraints, such as weekly mileage and returns to the home terminal. Hours-of-service regulations, such as the FMCSA rules in the United States, add legal constraints on top and regulate the driving, duty and rest time of every driver. Every additional constraint narrows the ways in which weekly schedules can be chained into routes. Finding the plan with the lowest cost across hundreds of schedules is a computationally challenging problem that has to be solved regularly. A manual process is therefore often inefficient and suboptimal.
Approach
The operations team sets the constraints of every driver category in FLEET-D, such as working hours, weekly mileage and returns to the home terminal. FLEET-D combines all the weekly schedules into driver routes that satisfy every constraint, adds empty legs to reposition drivers where needed, and minimizes the total driver cost, including the cost of empty miles. For categories with two drivers, FLEET-D checks that the activities of each driver follow the regulations, including driving, sleeper-berth time, standby and rest.
Result
FLEET-D delivers the compliant driver plan with the lowest total cost, together with the timetable of every driver. The operations team can also submit current routes for evaluation, and FLEET-D flags each breach with the driver and the hour concerned.
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DrivingSleeper berth while the co-driver drivesStandby at a terminalRest or home timeOver the limit of 70 hours in any 8 days
—
driver 1, on duty in the last 8 days
—
driver 2, on duty in the last 8 days
—
FMCSA limit

FLEET-P&D

Pickup and delivery · First and last mile · 3 casesExplore FLEET-P&D →
FLEET-P&D · Case 1 of 3Company fleet, third-party providers and routesNext case ↓↑ Top
France · National

Make vs buy, freight by freight

Challenge
Every pickup and delivery stop can be served by the company fleet or outsourced to a third-party provider, and the cheaper option changes from one stop to the next, even within a single postal zone. Third-party providers bill per shipment, whereas the cost of the company fleet depends on vehicle routing. Every reallocation also changes the rounds of several neighboring stations, and decisions taken station by station fail to reach the lowest cost for the network.
Approach
Planners use FLEET-P&D to design a complete outsourcing strategy for the network, dividing the freight between the company's own stations and third-party providers. Every scenario rebuilds the rounds of neighboring stations together and runs as a full optimization at the lowest total cost of company fleet and third-party providers. The current allocation always remains a feasible solution, and the optimized plan can never cost more than the existing operation.
Result
Planners get a make-vs-buy decision for every stop, together with the rebuilt rounds. On a national network in France, the model reproduced the actual cost of the company fleet to within about 5%. At the stations where the optimized plan changed the allocation, cost fell by about 7%, and the company fleet took back 13% more stops from third-party providers.
≈ −7%cost at the stations that gain
+13%stops for the company fleet
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StationThird-party depotRounds, station 1Rounds, station 2Second round, same vehicleStop served by a third-party providerThird-party assignmentStop taken back in houseStop given to a third-party providerStop moving to the other stationOptions priced for a stop
—
stops for the company fleet
—
stops given to third-party providers
—
vehicles / rounds
—
km driven
FLEET-P&D · Case 2 of 3Network designNext case ↓↑ Top
Regional

High-fidelity warehouse location and service-day optimization

Challenge
In a pickup network, the warehouse locations set the length of every pickup round, and the service days of each pickup zone set the number of vehicles needed each day. Additional warehouses shorten the rounds but add rent and staff, while an uneven spread of service days sizes the fleet for the busiest day of the week. Each decision changes the cost of the others, and the combinations of warehouse count, locations and service days are far too many to evaluate by hand. Deciding one dimension at a time leaves avoidable cost in every daily round.
Approach
Planners use FLEET-P&D to evaluate any number of warehouses at any candidate location. For every configuration, FLEET-P&D rebuilds the pickup rounds stop by stop, chooses the service days of every zone, and runs a full optimization of sites, vehicles and kilometers.
Result
Planners get the number and locations of warehouses with the lowest total cost, together with the pickup rounds and the service days of every zone. FLEET-P&D prices every alternative on the same basis, and planners see the saving of the recommended network over each alternative.
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Pickup pointSearch for a warehouse positionWarehousePickup rounds, by warehouse, rebuilt for every positionPickup zone served on the highlighted day
—
pickup points
—
warehouses
—
vehicles on the busiest day
—
daily cost, index (lowest = 100)
FLEET-P&D · Case 3 of 3First mile and linehaul↑ Top
Europe

Operating first mile and linehaul as one network

Challenge
Collection rounds and linehaul departures often answer to different teams with separate objectives. Over time, misalignments can creep in, such as a pickup waiting longer than necessary at a hub, a late round missing the last departure, or a truck leaving with unused space. Across dozens of hubs and hundreds of rounds, these occasional inefficiencies add up.
Approach
Planners use FLEET-P&D and FLEET to optimize collection and linehaul as one network. The Teranalytics AI planner runs both engines and iterates between the two until the plans fit together. FLEET-P&D schedules each collection vehicle to reach the hub shortly before a linehaul departure, and FLEET sizes the departures to the freight the collection vehicles bring in. Every iteration runs as a full optimization of the combined network, at the lowest total cost of collection and linehaul.
Result
FLEET-P&D and FLEET produce collection and linehaul plans that work together. Pickups reach the hub in time for the planned linehaul departures, and linehaul capacity matches the volume collected. The aligned networks run at a lower total cost, even when collection alone costs slightly more.
Explore FLEET-P&D →
Separate plans
—longest freight wait at a hub
—rounds with freight waiting over 3 hours
—linehaul trucks a day
Daily cost—
One plan
—longest freight wait at a hub
—rounds with freight waiting over 3 hours
—linehaul trucks a day
Daily cost—
HubCollection roundLinehaul lane and departureFreight waiting over 3 hoursDaily cost of collection and linehaul

Start with a few months of shipment history

We build the digital twin, then walk through the optimized plan and the resulting KPIs.

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