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AI in Supply Chain

Most supply chain AI tells you what to do.
ProvisionAi's AI does it.

There are two kinds of AI in supply chain. One predicts, recommends, and surfaces insights for humans to act on. The other calculates the optimal outcome and executes it — autonomously, at scale, without waiting for a planner to approve each decision. ProvisionAi builds the second kind.
Predictive AI Recommends the next step

Forecasts demand, surfaces exceptions, suggests actions. A human reviews the recommendation and decides whether to act. The execution gap remains.

Autonomous AI — ProvisionAi Takes the next step

Computes the optimal load or deployment schedule and executes it. No human approval required for routine decisions. The execution gap closes.

Two Types of AI

The difference between AI that advises and AI that executes.

Most supply chain AI investments produce better dashboards and smarter alerts. The planner still makes the call. The execution gap — between what AI recommends and what actually ships — remains. Autonomous execution AI closes that gap by removing the human bottleneck from routine decisions.

Predictive AI

Recommends. Alerts. Surfaces insights.

The planner reviews the recommendation and decides whether and how to act. Execution still depends on human judgment at every step.

Demand forecasting
Predicts what inventory will be needed and when — but doesn't decide how to deploy it or whether the network can absorb the resulting volume.
Exception management
Flags anomalies and surfaces alerts — but the planner still decides which exceptions to resolve and how to resolve them.
Prescriptive recommendations
Suggests the optimal action based on current conditions — but a human approves or overrides before execution begins.
Autonomous AI — ProvisionAi

Calculates. Optimizes. Executes.

The system computes the optimal outcome and acts on it. Planners manage exceptions — not every decision.

AutoO2 — autonomous load building
Computes the maximum legal payload configuration for every load — 300+ parameters simultaneously — and delivers the plan to the loader's RF device. No planner approval required.
LevelLoad — autonomous deployment scheduling
Builds a 30-day capacity-balanced deployment schedule across the entire network and triggers carrier tenders automatically. Planners resolve exceptions — not routine decisions.
Hands-off planning
LevelLoad automates tender initiation without human intervention. Planners focus on the few remaining exceptions instead of managing every shipment.
The Execution Gap

The distance between what AI recommends and what actually ships. Autonomous AI eliminates it.

Every supply chain has an execution gap — the distance between what the planning system outputs and what the physical network actually does. Predictive AI narrows it by improving recommendations. Autonomous AI closes it by replacing the human approval step with direct execution. ProvisionAi's clients don't have planners approving every load build or every carrier tender. The AI handles it — and planners manage exceptions.

0 Human approvals required for routine load and tender decisions
Autonomous AI in Practice

How AutoO2 and LevelLoad use AI to execute — not advise.

ProvisionAi's two products represent autonomous AI applied to the two most expensive decisions in freight operations — what goes on each truck, and when each truck moves. Both compute the optimal outcome and execute it. Neither waits for a planner to approve the answer.

Physical Load Optimization

AutoO2

AI that computes the maximum legal payload on every load and delivers it to the loader — autonomously.

AutoO2 solves a combinatorial optimization problem that no human could solve manually at speed — finding the maximum legal payload configuration across 300+ simultaneous constraints for every load, every shift. The answer is computed in seconds and delivered as step-by-step guidance to the loader's RF device.

Combinatorial optimization

300+ parameters — axle weight by state, cube utilization, stacking constraints, damage risk, pick sequence — solved simultaneously to find the true maximum legal payload.

Real-time recalculation

If inventory changes mid-load, AutoO2 recalculates instantly and updates the loader's guidance — maintaining the optimal configuration without interrupting the loading process.

Autonomous execution

No planner approves each load build. AutoO2 computes and delivers the plan — the loader executes it. Planners manage exceptions, not routine decisions.

5–10% Freight cost reduction from maximized payload. 88,000 trucks eliminated annually.
See How AutoO2 Works →
Network Flow Stabilization

LevelLoad

AI that builds a 30-day capacity-balanced deployment schedule and triggers carrier tenders — autonomously.

LevelLoad solves a network-wide optimization problem across thousands of variables — balancing shipment volume, DC capacity, carrier availability, and inventory priority across every lane simultaneously, 30 days out. The resulting deployment schedule is executed automatically, with carrier tenders triggered without planner intervention.

Simultaneous network optimization

Every DC, every lane, every carrier — optimized at once across a 30-day horizon. Fixing one constraint doesn't cascade into another because the entire network is solved simultaneously.

Automated tender initiation

LevelLoad triggers carrier tenders autonomously through your TMS — no planner picks up the phone or logs into a portal. Carriers receive tenders 2.5 days earlier than standard.

Exception-only planning

Planners shift from managing every shipment to resolving only the exceptions LevelLoad flags — a fundamental change in how supply chain teams spend their time.

60% Variability reduction. 97% first tender acceptance. 4-month ROI.
See How LevelLoad Works →
Proof at Scale

Autonomous AI deployed at the world's largest consumer goods operations.

ProvisionAi's autonomous execution AI isn't a pilot or a proof of concept. It runs live across the North American operations of some of the most complex supply chains on earth — processing thousands of loads daily without human approval at every step.

Kimberly-Clark

60%

Reduction in daily shipment variability

LevelLoad deployed across all North American operations — autonomously building deployment schedules, triggering carrier tenders, and smoothing network flow without planner intervention on routine decisions.

Read the case study →
Riviana Foods

10%+

Freight cost reduction per lane

AutoO2 solving the mixed weight SKU problem — autonomously computing optimal load configurations across rice products ranging from 20-pound bags to 6-ounce cups, maximizing payload on every truck.

Read the case study →
Unilever

98%

Truck utilization achieved

AutoO2 pushing truck utilization to 98% across Unilever's North American network — autonomously building every load to its legal maximum while advancing a hard 2030 net-zero emissions target.

Read the case study →
88k Trucks eliminated annually By AutoO2 across the network
$160M Saved per year In freight costs
285k Tons CO₂ reduced Annually across the network
97% First tender acceptance With preferred carriers
See Autonomous AI in Your Operation

Find out what autonomous execution AI would change in your freight operation.

ProvisionAi has deployed autonomous AI across the supply chains of P&G, Kimberly-Clark, Nestlé, Unilever, and more. If you're shipping 5,000+ truckloads a year, we'll show you exactly where the opportunity is.
Talk to an Expert → See Our Solutions
For operations shipping 5,000+ truckloads/year · Response within one business day

Frequently Asked Questions

AI in supply chain planning refers to machine learning and optimization algorithms applied to supply chain decisions — demand forecasting, inventory optimization, load building, network deployment, and carrier management. There are two categories: predictive AI that surfaces insights and recommendations for humans to act on, and autonomous AI that computes optimal outcomes and executes them directly. ProvisionAi's AutoO2 and LevelLoad are autonomous — they act, not advise.
Predictive AI forecasts outcomes, surfaces exceptions, and recommends actions — but a human still approves each decision before execution. Autonomous AI computes the optimal outcome and executes it directly, without waiting for human approval on routine decisions. The difference is the execution gap: predictive AI narrows it, autonomous AI closes it. ProvisionAi's AutoO2 builds the optimal load without planner approval. LevelLoad triggers carrier tenders automatically. Planners manage exceptions — not every decision.
AI optimizes truck load building by solving a combinatorial optimization problem — finding the maximum legal payload configuration across hundreds of simultaneous constraints including axle weight limits by state, cube utilization, product stacking rules, damage prevention, pick sequence, and customer-specific requirements. AutoO2 runs this optimization for every load, delivering the result as step-by-step visual guidance to the loader's RF device. The outcome: 5–10% freight cost reduction and zero axle violations.
AI improves supply chain network planning by solving the capacity-constrained deployment scheduling problem that traditional planning tools ignore. LevelLoad reads 30 days of forecasted demand alongside DC throughput limits, carrier capacity, and lane-level constraints — then builds a network-wide deployment schedule that balances all variables simultaneously. The result is 60% less daily variability, 97% first tender acceptance, and a plan the physical network can actually execute.
AI is best suited to supply chain problems that involve large numbers of simultaneous variables, constraints that interact with each other, and decisions that need to be made repeatedly at speed. Load building — optimizing hundreds of parameters for thousands of loads per day — is a perfect AI problem. Network deployment scheduling — balancing DC capacity, carrier availability, and inventory priority across hundreds of lanes simultaneously — is another. These are problems where human judgment can't match the speed or scale of computation required.

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