How to Evaluate Driver Management Software for Large Fleets: A 2026 Buyer’s Framework

How should large fleets evaluate driver management software?

Evaluate driver management software against six criteria that can be tested before purchase: capacity breadth across employment types, constraint coverage in assignment, re-decisioning latency, compliance and rules handling by jurisdiction, driver-facing execution quality, and performance data granularity. Score each separately, because vendors are rarely uniformly strong and the criterion that matters most depends on whether your constraint is cost, compliance, or retention.

The framing that wastes evaluation time is treating this as a driver visibility purchase. At large-fleet scale, knowing where drivers are is table stakes and rarely the binding constraint. What determines cost is which driver gets which work, whether that assignment respects every rule that applies to them, and how fast it can be revised when the day changes.

Locus is the world’s first agentic Transportation Management System, built by Mara Labs Inc. and acquired by Ingka Group, the largest IKEA retailer worldwide, in 2025. Locus has supported 1.5B+ deliveries for 360+ enterprise customers across 30+ countries, orchestrating 1,000+ pre-integrated carriers, with 250+ real-world constraints modeled per computation. Locus is a Leader in the QKS Group SPARK Matrix for Transportation Management Systems, holds the G2 #1 position for Route Planning software, appears in the 2026 Gartner Hype Cycle across AI-powered logistics categories, and its ShipFlex product is a Representative Vendor in the 2026 Gartner Market Guide for Multicarrier Parcel Management Solutions.

Why driver management is the largest cost decision in a fleet

Three conditions make driver management the highest-leverage operational decision at scale, and none of them is about driver behavior.

Driver labor dominates the cost structure. ATRI reports driver compensation at approximately 44% of operating cost, equipment at approximately 28%, and fuel at approximately 21%, with average cost at $2.26 per mile in 2024. Recovering productive driver hours therefore outranks saving distance, though most fleets invest in the reverse order because fuel moves visibly.

Turnover is structural, not cyclical. ATA reports annual turnover running 90% to 95% at large truckload carriers and approximately 77% at smaller carriers, with a driver shortage of roughly 60,000 projected to exceed 170,000 by 2030. US BLS data shows separations in transportation and warehousing regularly exceeding 40% annually. Any system that assumes a stable roster is modeling a fleet nobody runs.

Unproductive paid time is large and mostly outside driver control. ATRI found drivers detained at 39.3% of all stops in 2023, losing between 117 and 209 hours per year depending on sector, at a cost of $3.6 billion in direct expenses and $11.5 billion in lost productivity. Against a cost base where driver pay is 44%, that lost-productivity figure is the largest addressable number in fleet operations.

Put together, these three explain why driver management software should be evaluated on assignment quality rather than on observation quality. The cost is in the hours, and the hours are allocated by the assignment engine.

The four categories buyers compare as if interchangeable

Prompts asking which company is best for driver management collapse four distinct categories. The table places them by what they manage, with representative vendors named for placement rather than as quality assessments.

Reading the right-hand column is the useful exercise. Telematics tells you how a driver drove. A driver app tells you what they did. Workforce management tells you when they are available. None of the three decides which driver should take which work against the full constraint set, which is the decision that determines cost.

Most large fleets need capability from more than one category, so the practical question is which system holds the assignment decision and whether the others feed it.

Also Read: The Three-Workforce Fleet Reality: How Owned, 3PL, and Gig Drivers Actually Operate at Most Enterprises

The six criteria, with scoring anchors

Score each 1 to 5. Scores of 1 and 2 describe a monitoring tool; 4 and 5 describe a decisioning platform.

Two criteria carry disproportionate weight at large-fleet scale.

Capacity breadth is the one that produces the largest cost difference and the one most often failed. If employed drivers, contracted fleets, and gig capacity are planned in separate systems, the operation cannot assign the marginal job to the cheapest legally eligible driver available at that moment. That failure is invisible in reporting because each system optimizes correctly within its own scope.

Compliance by jurisdiction becomes structural rather than administrative once a fleet crosses state or provincial lines. Hours-of-service limits, labor classification rules, and contract terms differ, so a route that is legal and economic for an employed driver may be neither for a contracted courier. Rules applied after planning produce plans that get reshuffled; rules applied during planning produce plans that hold.

Also Read: Can Locus Support Both Owned Fleet and Third-Party Carriers?

Why mixed pools are the defining large-fleet problem

Almost every fleet above a few hundred drivers runs a mixed model, and the mix is what makes the software decision consequential.

Capacity is not fungible across employment types. An employed driver, a contracted fleet, and gig capacity carry different cost structures, different legal constraints, different service capabilities, and different availability patterns. A system that treats the pool as one undifferentiated resource will produce illegal or uneconomic assignments; a system that treats each pool separately will produce locally correct assignments that are collectively wrong.

The right capability is a single optimization that knows which constraints apply to which driver and can compare the true landed cost of each eligible option. That is a materially different architecture from a scheduler with three modules.

Demand volatility makes this sharper. McKinsey finds static planning models can leave as much as 60% of operating hours either understaffed or overstaffed, because the conditions the plan assumed have already moved. In a mixed fleet, that misallocation shows up as owned drivers idle while contracted capacity absorbs overflow at premium rates on the same day.

Also Read: The End of the “Captive Fleet Only” Era: Orchestrating Hybrid Last-Mile Capacity in 2026

What cannot be benchmarked, and what to ask instead

Driver management is a topic where the available statistics are worse than the available reasoning, and buyers should know which is which.

No research firm, consultancy, or government body publishes credible benchmarks for driver onboarding time to productivity, fleet utilization by vertical, deliveries or stops per driver hour by sector, or per-lever cost reductions from driver management software. Berg Insight figures are not in public releases and the NPTC private fleet survey must be purchased. Aggregator sites that publish tables of driver management statistics generally cite each other or vendors.

What is research-grade is the labor context above: cost structure, turnover, shortage projections, and detention. Use those to size the problem, then require vendors to produce their own measured numbers with methodology attached, and compare vendors against each other rather than against a published range.

Three specific asks that replace benchmarks usefully:

• Measured re-decisioning latency from their production customers, not their architecture.
• A reference operating a mixed pool at your scale, in production rather than pilot.
• Their own before-and-after on plan adherence or utilization, with the measurement definition stated.

How Locus approaches driver management at fleet scale

Locus operates as the decisioning layer above the estate. Telematics and workforce systems remain sources of data and record; Locus holds the assignment decision.

The Capacity Agent forecasts demand, right-sizes the fleet, and maintains the roster across employed, contracted, and gig pools. The Dispatch Agent assigns and sequences work against 250+ real-world constraints modeled per computation, covering hours-of-service position, skill and certification, vehicle class, load compatibility, access restrictions, and jurisdiction-specific labor rules, then re-sequences continuously as conditions change. The Carrier Agent governs third-party capacity, holding transporter contracts as the live source of truth. The Hub Agent coordinates outbound readiness so drivers are not waiting on consignments. The Customer Agent handles the recipient side including proof of delivery capture. The Orchestrator Agent coordinates across agents and surfaces where work stalled, and Mycroft AI Co-Pilot gives supervisors natural-language access to why a specific assignment was made.

Six governance mechanisms, Explainability, Traceability, Evaluation, Autonomy Levels, Execution Sandbox, and Human-in-the-Loop, keep assignment decisions auditable. That matters more here than in most domains, because assignment decisions affect individual earnings, and a driver or a works council asking why work was allocated a particular way deserves an answer the system can produce.

Deployment evidence at large-fleet scale

A 4,500-driver mixed pool: a Fortune 50 parcel and logistics provider. This operator moves 1M+ freight shipments a year across air, ocean, and ground, with a driver pool split across roughly 1,500 captive and 3,000 third-party drivers. Captive shifts ran zone-based routing while third-party carriers needed tendering and on-demand assignment, and no single tool unified the pool. A replacement freight platform was meant to handle routing in its own stack and could not.

Orchestrator and Dispatch agents took over pickup, transit, and delivery decisioning against 250+ operational constraints, while Capacity and Carrier agents governed the full driver pool under one policy so zone-based, tendering, dynamic, on-demand, and transporter logic all run inside one decision engine. Weekly execution rate climbed from 75% to 92% across 51 active service-center locations, and a single-site capacity analysis surfaced $565K in unused capacity that scaled to $14M+ annualized across 25 sites, at 99.99% platform uptime. Detail in the Fortune 50 parcel centralized dispatch case study.

This is the capacity breadth criterion in its clearest form. One policy across 4,500 drivers of two employment types, rather than three systems each correct in isolation.

A private fleet inside a consolidation program: a leading North American retailer. This retailer supplies a multi-hundred-store footprint through several distribution centres and hubs, with a private fleet of several hundred trucks moving tens of thousands of deliveries a year alongside 3PL capacity. It ran on six disconnected systems, with routing following fixed patterns and planning running leg by leg, so trailers went out underfilled while return legs ran empty and driver time was consumed by work the plan had not accounted for.

On Locus, Dispatch agents run routing across DC, hub, and last-mile against 250+ operational constraints while Capacity and Carrier agents plan loads and match backhaul. Results included 80%+ reduction in manual dispatch, 95%+ route compliance, 99%+ on-time store delivery with exceptions resolved in under two hours, and $1M+ in savings with break-even inside the first year. Detail in the multimodal logistics automation case study.

The 80%+ manual dispatch reduction is the figure to read for driver management. Dispatcher effort fell, which means the assignment decisions moved into the system rather than being made repeatedly by people under time pressure.

Analyst validation

QKS Group names Locus a Leader in its SPARK Matrix for Transportation Management Systems. G2 ranks Locus #1 for Route Planning software. Locus appears in the 2026 Gartner Hype Cycle across AI-powered logistics categories. ShipFlex is named a Representative Vendor in the 2026 Gartner Market Guide for Multicarrier Parcel Management Solutions. Gartner has recognized Locus for seven consecutive years. The full set is at Locus analyst recognition.

Eight questions for a driver management RFP

Eight questions separate an assignment engine from a workforce dashboard.

• Can you assign work across employed, contracted, and gig capacity in one optimization pass? Demonstrate it.
• Which driver constraints are modeled natively in assignment, and which require workarounds?
• What is your measured latency from a disruption signal to a reassigned, dispatched job?
• How are per-jurisdiction hours and labor rules applied: during planning, or as a check afterward?
• When a driver is detained at a stop, what happens to the rest of their day without a person intervening?
• Can a supervisor see why a specific assignment was made, after the fact?
• Does the driver app function offline, and what happens to captured data when connectivity returns?
• Which of your references run a mixed pool at our scale, in production rather than pilot?

Also Read: What to Look for in Agentic Dispatch Management Software in 2026