Methodology Example — Route Optimization

How the methodology models a 200-truck Texas Triangle LTL carrier's $1.85M annual operating cost reduction

Applied to a mixed-freight LTL operator running a relay network designed 10 years ago for a different customer mix. The methodology rebuilds the lane structure from scratch and identifies $1.85M in stranded efficiency.

200
Trucks in fleet
14
Relay points (before)
9
Relay points (after)
$1.85M
Annual cost reduction
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About this methodology example

This page presents a worked example of the route optimization methodology applied to a representative LTL carrier’s network. Identifying details are anonymized — we never share client information without explicit approval. Numbers reflect modeled cost reduction and projected outcomes from the analysis. Your results will differ based on your network footprint, customer mix, and operating conditions.

The operational profile

The profile applied is a 200-truck LTL carrier running mixed freight — dry van and temperature-controlled — across the Texas Triangle (Dallas–Houston–San Antonio) with extensions into El Paso, Albuquerque, Phoenix, and Oklahoma City. The fleet has grown through acquisition, absorbing two smaller regional carriers over five years, each with their own relay points, hub locations, and dispatch logic.

The result is a bloated network: 14 relay points, inconsistent sort windows, and drivers frequently running legs under 150 miles — well below the utilization threshold for profitable LTL linehaul. Leadership at this scale typically knows the network is inefficient but can’t untangle which facilities to close without risking service failures on key accounts.

Key insight

Post-acquisition networks almost always carry redundant infrastructure. The legacy relay points made sense for each carrier individually, but combined, three pairs of facilities were within 60 miles of each other — creating unnecessary handling, extra dock touches, and wasted linehaul hours.

What the methodology surfaces

The methodology ingests 18 months of shipment-level data, driver logs, and facility throughput records. Every linehaul move, sort event, and dock touch is mapped across the network, and each relay point is scored by volume throughput, geographic coverage overlap, and contribution to total transit time.

Inefficiency Impact Recoverable value
Redundant relay points (3 pairs within 60 mi of each other) 22% excess dock touches $720K
Suboptimal lane pairing (legs under 150 mi) 34% of legs below utilization threshold $480K
Sort window misalignment across hubs 6–8 hr freight dwell at 4 facilities $310K
Out-of-route miles to serve low-density corridors 3 corridors at <40% trailer fill $240K
Driver domicile mismatch (drivers starting far from first pickup) Avg 52 min stem time $110K
Total recoverable $1.85M/yr

What the methodology recommends

The methodology produces a 120-day implementation playbook with 11 specific actions. The modeled outcomes below reflect projected impact at full implementation. The top five recommendations alone account for 84% of the recoverable value:

1. Consolidate three relay pairs into single facilities. Waco and Temple (32 miles apart), Midland and Odessa (20 miles), and two San Antonio-area sort points are each merged into a single location. This eliminates 22% of dock touches and removes three lease obligations.

2. Restructure linehaul legs around 250-mile minimums. The methodology redraws lane assignments so that 90% of linehaul legs exceed 250 miles — the break-even point for driver utilization in the fleet’s cost model. Short shuttle runs under 150 miles are consolidated into fewer, longer pulls.

3. Realign sort windows across the DFW and Houston hubs. The two highest-volume hubs operate on sort windows set independently — freight arriving from one hub sits 6-8 hours waiting for the next sort at the other. Synchronizing the windows cuts average hub dwell from 7.2 hours to 3.4 hours.

4. Convert three low-density corridors to partner-carrier arrangements. The El Paso–Albuquerque, OKC–Tulsa, and Phoenix–Tucson corridors run at under 40% trailer fill. Rather than running its own trucks at a loss, the methodology recommends interline agreements with regional partners — maintaining service levels at 60% lower linehaul cost on those lanes.

5. Redomicile 18 drivers to match freight origins. Analysis surfaces 18 drivers starting their shifts 45+ minutes from their first pickup. Reassigning home terminals to align with high-volume origin points cuts average stem time from 52 to 24 minutes.

Network Visualization
Texas relay network — baseline vs. methodology target
TEXAS OKC Amarillo Dallas Waco El Paso Midland San Antonio Houston Laredo Ft. Worth Temple Odessa New Braunfels Baytown
14
Relay points
$1.85M
Annual cost reduction
Active relay point
Recommended for consolidation
Redundant in modeled redesign

Modeled outcome — baseline vs. methodology target

Baseline (operational profile)
Relay points14
Avg linehaul leg187 mi
Legs under 150 mi34%
Avg hub dwell (DFW–HOU)7.2 hrs
Avg driver stem time52 min
Methodology target (post-implementation)
Relay points9
Avg linehaul leg274 mi
Legs under 150 mi8%
Avg hub dwell (DFW–HOU)3.4 hrs
Avg driver stem time24 min

The methodology timeline

Days 1–3
Data intake
Eighteen months of shipment records, linehaul manifests, facility throughput logs, driver settlement data, and lease agreements for all 14 relay locations are intaken through the methodology’s secure intake portal.
Days 4–12
Network mapping and diagnosis
A complete graph model of the network is built — every facility, lane, and sort window. Geographic overlap, volume imbalances, and the six corridors responsible for 71% of excess cost are identified.
Days 13–25
Optimization modeling
Facility location optimization is run to determine the minimum relay footprint that maintains service commitments. Three consolidation scenarios are simulated and stress-tested against peak-season volumes.
Days 26–30
Executive readout
The deliverable presents the redesigned network to ops leadership (CEO / COO / VP Linehaul). The analysis produces a phased 120-day implementation plan and identifies $1.85M in annual cost reduction.

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