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Case Study · AutoO2 · Sustainability

AutoO2 by ProvisionAI

Rising fuel. Driver shortage. 98% utilization — and lower costs and emissions at the same time.

"Achieved industry-leading truck utilization while simultaneously cutting costs and carbon — proving efficiency and sustainability are not in conflict."
ProvisionAI Case Study · 2025 · Global CPG Leader
98%Truck utilization
4–8%Cost reduction
4–8%Emissions cut
2039Net-zero supported
Global CPG Leader — AutoO2 truckload optimization for sustainability and freight cost reduction
98% utilizationUp from 90–95%
4–8% CO₂ reductionScope 3 emissions
The challenge

90–95% utilization sounds efficient. At scale, the gap is millions.

A 3–8% utilization gap compounds into extra trucks, extra miles, and extra emissions across hundreds of daily shipments.
Rising fuel costs and a global driver shortage made the inefficiency of underloaded trucks increasingly expensive.
A hard 2039 net-zero commitment meant every avoidable truck was both a cost problem and a sustainability failure.

Persistent utilization gap

Trucks at 90–95% left a compounding cost gap across hundreds of daily shipments.

Rising fuel and carrier costs

External cost pressures made underloaded trucks increasingly expensive to absorb.

Global driver shortage

Every unnecessary shipment consumed scarce carrier capacity preferred carriers needed elsewhere.

Hard emissions targets

A 2039 net-zero commitment meant every avoidable truck was both a cost and a sustainability failure.

The solution

AutoO2 closed the utilization gap — on every load, every day.

300+ constraints solved simultaneously — weight, cube, axle distribution, damage risk, stacking — on every shipment.
Utilization pushed to 98% — fewer trucks needed to move the same volume, cutting both cost and CO₂.
Efficiency and sustainability are the same gain — every tightly packed load is one fewer truck on the road.
PlanningERPWMSRF Devices
AutoO2 truckload optimization — 98% utilization for global CPG sustainability
The insight

Efficiency and sustainability are not in conflict. Maximizing payload per truck is simultaneously the lowest-cost and lowest-emission way to move the same volume of product.

01 — Data ingestion

Shipment data pulled from planning and ERP

Every item's dimensions, weight, stacking constraints, and customer requirements ingested before building a single load plan.

PlanningERPItem master
02 — 300+ parameter optimization

Weight, cube, axle, damage — all solved simultaneously

Maximum legal payload on every shipment — no reloads, no violations, no trade-offs between cube and axle compliance.

300+ constraintsGlobal optimum
03 — Visual guidance to warehouse floor

Step-by-step instructions on RF devices

Removes expertise dependency — every loader builds the optimized load correctly, every shift, every day.

RF devicesZero expertise required
04 — Fewer trucks, same performance

Higher payload = fewer total shipments

Fewer miles driven, less fuel burned, less CO₂ emitted — efficiency gains and Scope 3 sustainability gains are identical.

Scope 3 reductionNet-zero roadmapCost savings
The results

Industry-leading utilization. Lower costs. Lower emissions.

98% Truck utilization Up from 90–95% baseline
4–8% Transport cost reduction Fewer trucks, same volume
4–8% Carbon emissions cut Scope 3 reduction
2039 Net-zero supported Every truck eliminated counts
"Achieved industry-leading truck utilization while simultaneously cutting costs and carbon — proving efficiency and sustainability are not in conflict."
ProvisionAI Case Study · 2025 · Global CPG Leader

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