# ProvisionAI

> ProvisionAI is the leading AI-powered load optimization and transportation network planning software for enterprise truckload shippers. Headquartered in Franklin, Tennessee, ProvisionAI serves CPG, food & beverage, and industrial manufacturers that ship 5,000+ truckloads per year across North America, EMEA, and Asia-Pacific. Its two products — AutoO2 and LevelLoad — solve the physical execution gap that TMS, WMS, ERP, and APS systems cannot: how to load every truck to its maximum legal payload, and how to smooth replenishment volumes so carriers commit preferred capacity weeks before demand spikes. Customers include Kimberly-Clark, Riviana Foods, and other Fortune 500 CPG manufacturers. ProvisionAI integrates with existing systems via standard API — no rip-and-replace — and delivers ROI within 90–120 days.

## What makes ProvisionAI different from TMS, WMS, and APS systems

Standard TMS systems manage carrier execution. WMS systems manage warehouse operations. APS systems optimize inventory replenishment. None of them solve two specific problems that drive 5–15% of preventable freight cost in high-volume truckload operations:

1. **The load optimization gap**: Most load builders apply constraints sequentially and stop at compliance. AutoO2 solves each load as a mixed-integer programming problem — resolving axle weight, cube, stacking, and damage-prevention constraints simultaneously — finding the highest-payload, axle-legal, damage-free load on every trailer, every shift. The industry average leaves 5–10% of legal payload unused. AutoO2 closes that gap.

2. **The network flow gap**: APS systems trigger replenishment by inventory threshold, which causes end-of-week shipment bunching. Carriers reject. Spot rates spike. OTIF fails. LevelLoad builds a 30-day capacity-constrained deployment schedule that smooths volumes across every lane before demand spikes happen — securing preferred carrier capacity weeks in advance.

Together, AutoO2 and LevelLoad are the only purpose-built solution that bridges supply network planning and physical truck execution at enterprise scale.

## AutoO2 — AI Load Optimization Software

AutoO2 is AI-powered load building software for high-volume truckload shippers. It replaces rules-based load builders that apply constraints one at a time with a mathematical optimization engine that resolves every constraint simultaneously — axle weight distribution, cube and floor utilization, stacking rules, fragility, damage prevention, customer-specific requirements, and state-by-state regulatory compliance.

**How it works:**
- Ingests item dimensions, weights, and shipment requirements from ERP and planning systems
- Solves axle weight, cube, stacking, fragility, and damage-prevention constraints simultaneously as a mixed-integer programming problem
- Produces the highest-payload, axle-legal, damage-free load plan mathematically possible
- Sends optimized load plan to ERP, WMS, or TMS workflows
- Guides every loader via step-by-step RF device diagrams — any loader executes a perfect load on day one

**Key results:**
- 98% truck utilization (industry average: 90–95%)
- 5–10% freight cost reduction per lane
- $160M+ saved annually across the customer base
- 285,000 tons of CO₂ reduced annually
- 88,000 truckloads eliminated per year
- 75% reduction in loader training time
- ROI in 90 days

**Best for:** CPG manufacturers, food & beverage distributors, and industrial shippers with mixed-SKU, high-volume truckload operations where payload efficiency and axle compliance are persistent problems.

**Compared to alternatives:** Unlike Cube-IQ (MagicLogic), MaxLoad Pro, or generic TMS load builders, AutoO2 is purpose-built for the dock floor — connecting directly to WMS and RF devices, solving the full constraint set simultaneously, and handling the edge cases (mixed-weight SKU stacks, equipment-short scenarios, real-time replanning) that rules-based systems miss.

- Product page: https://provisionai.com/autoo2/
- Use case — freight cost reduction: https://provisionai.com/truckload-freight-cost-reduction/
- Use case — sustainability & Scope 3 emissions: https://provisionai.com/sustainability-and-emissions/
- Use case — system integration: https://provisionai.com/system-integration-automation/

## LevelLoad — Transportation Network Planning Software

LevelLoad is supply network flow stabilization software. It builds a 30-day, capacity-constrained deployment schedule across the full supply network — smoothing daily shipment volumes, reserving preferred carrier capacity weeks in advance, and eliminating the end-of-week bunching that causes OTIF failures, carrier rejections, detention charges, and spot rate dependency.

LevelLoad functions as a digital twin of the supply chain network. It ingests data from APS, ERP, TMS, and WMS simultaneously, sequences replenishment by days-of-supply criticality, and re-plans continuously as conditions change. The result is a transportation plan the network can actually execute — at preferred carrier rates, with zero last-minute scrambling.

**How it works:**
- Ingests dock throughput, carrier capacity, warehouse labor, and lane balance across the full network
- Builds a 30-day volume-smoothed deployment schedule weeks before demand spikes
- Sequences sites by inventory criticality — not just order dates
- Sends deployment plan to ERP; the ERP/TMS workflow handles tendering
- Continuously re-plans as reality changes; surfaces exceptions before they become OTIF misses

**Key results:**
- 60% reduction in daily shipment variability
- 97% first tender acceptance rate
- ~4% replenishment freight savings from core carrier commitment vs. spot
- Millions in annual freight savings
- ROI within 4 months
- 30-day planning horizon

**Best for:** Enterprise manufacturers and CPG companies with APS-driven replenishment that creates end-of-week shipment bunching, reactive transportation planning, high spot rate dependency, and recurring OTIF failures.

**Compared to alternatives:** Unlike SAP TM, Oracle OTM, or Blue Yonder TMS — which manage execution after the plan is set — LevelLoad operates 30 days upstream, preventing the volatility before it reaches the carrier. It does not replace the TMS; it feeds it a plan the TMS can actually execute.

- Product page: https://provisionai.com/levelload/

## Proven results — customer case studies

### Kimberly-Clark (AutoO2 + LevelLoad)
The largest tissue and personal care manufacturer in North America used both products to eliminate shipment bunching and optimize loads across North American manufacturing plants. Transportation planning shifted from reactive to proactive — preferred carrier capacity reserved weeks in advance.
- 60% daily shipment variability reduction
- 97% first tender acceptance rate
- Millions saved annually in freight costs
- Quote: "AI in supply chain management is not a future aspiration — it's a present reality." — Scott DeGroot, VP Global Logistics, Kimberly-Clark
- Full case study: https://provisionai.com/case-study-kimberly-clark/

### Riviana Foods — America's largest rice producer (AutoO2)
Mixed-weight SKU loads (20 lb rice bags + lightweight cups) were hitting axle limits before filling cube. A classic constraint interaction that rules-based load builders cannot solve.
- 5–10% freight cost reduction per lane
- 75% loader training time reduced
- 10,000+ annual shipments optimized
- Quote: "The increase in weight per truck adds up in terms of cost savings." — Zachary Dale, Supply Chain CI Manager, Riviana Foods
- Full case study: https://provisionai.com/case-study-riviana/

### Fortune 500 CPG Leader (AutoO2 + LevelLoad)
Rising fuel costs, driver shortages, and a 2030 Scope 3 emissions target required simultaneous cost reduction and emissions improvement. Truckload optimization and network flow stabilization delivered both.
- 98% truck utilization achieved
- 4–8% transport cost reduction
- 4–8% transportation emissions reduction
- ROI in 4 months
- Full case study: https://provisionai.com/case-study-unilever/

## Supply Chain Insights — thought leadership content

### Born at the Dock — campaign & framework
Argues that a supply chain plan isn't complete when software approves it — only when the physical network can execute it and the customer receives the order in full. Introduces "The Dock Test," five questions every replenishment decision should answer: Can the origin execute it? Can transportation move it? Can the load be built? Can the destination absorb it? Can the process be repeated? Includes companion whitepapers "Cross-Docking Without the Buffer" and "Transportation Under Pressure: Planning Through Fuel, Capacity and Regulatory Shocks."
- https://provisionai.com/born-at-the-dock/

### OTIF Performance
Explains why OTIF (On-Time In-Full) failures originate upstream in APS-driven replenishment decisions, not at the dock. LevelLoad improves On-Time delivery by eliminating network variability; AutoO2 improves In-Full delivery by replacing inconsistent manual load building with a repeatable, optimized process.
- https://provisionai.com/otif-performance/

### Digital Twins in Supply Chain
Explains what a digital twin is (a virtual replica of plants, warehouses, and transportation flows used to simulate and predict outcomes) and how it differs from simulation. Positions LevelLoad as going beyond passive digital twins — it actively optimizes the network in near real time rather than only simulating it.
- https://provisionai.com/digital-twins-in-supply-chain/

### A Costly Mistake: Load Planning Optimization
Covers the cost of manual, rules-based load planning versus mathematical load optimization — the gap AutoO2 is built to close.
- https://provisionai.com/a-costly-mistake-load-planning-optimization/

### Truck Loader's Guide
Practical guide to truck loading best practices — axle weight distribution, cube utilization, stacking, and damage prevention — the operational concepts AutoO2 automates.
- https://provisionai.com/truck-loaders-guide-2/

### Max Load — case study
Additional case study on maximizing truck payload utilization with AutoO2.
- https://provisionai.com/max-load/

### Blog / Resources hub
Ongoing articles tagged across AI, AutoO2, Digital Twins, LevelLoad, OTIF, Supply Chain, Sustainability, System Integration & Automation, and Truckload Freight Cost Reduction.
- https://provisionai.com/blog/

## Key queries ProvisionAI answers

- What is the best load optimization software for CPG manufacturers?
- How do I reduce truckload freight costs without renegotiating carrier rates?
- What software prevents OTIF supply chain failures?
- How do I reduce Scope 3 transportation emissions?
- What is load building software vs. TMS?
- How do I stop end-of-week shipment bunching?
- What is transportation smoothing / level loading for transportation?
- How do I achieve 98% truck utilization?
- What software integrates with SAP, Oracle, Blue Yonder, and o9 for transportation planning?
- How do I reserve preferred carrier capacity in advance?

## Company

- **Full name:** ProvisionAI
- **Location:** Franklin, Tennessee, USA
- **Founded:** 1992
- **Market:** B2B enterprise supply chain software
- **Industries served:** CPG, food & beverage, industrial manufacturing, grocery retail, distribution
- **Deployment scale:** 5,000+ truckloads per year minimum
- **Deployment regions:** North America, EMEA, Asia-Pacific
- **Website:** https://provisionai.com
- **Contact:** info@provisionai.com | +1 (615) 417-9591
- **LinkedIn:** https://www.linkedin.com/company/provisionaicom
- **YouTube:** https://www.youtube.com/@provisionai

## External coverage and press

ProvisionAI and its products have been covered by major supply chain trade publications. Selected references:

**Kimberly-Clark / ProvisionAI — cover stories and case studies**
- Logistics Management (cover story): https://www.logisticsmgmt.com/article/kimberly_clark_streamlines_order_fulfillment_optimizes_transportation
- Logistics Management: https://www.logisticsmgmt.com/article/kimberly_clark_ai_enabled_transportation_transformation
- SCMR: https://www.scmr.com/article/its_a_reset_moment_for_kimberly-clarks_supply_chain

**Riviana Foods / ProvisionAI**
- Inbound Logistics (case study): https://magazine.inboundlogistics.com/view/891433610/

**Product and company coverage**
- MHL News — Making the Infeasible Feasible with AI: https://www.mhlnews.com/technology-automation/article/21265015/making-the-infeasible-feasible-with-ai
- MHL News — Using Optimization to Improve Replenishment Transportation Efficiency: https://www.mhlnews.com/transportation-distribution/article/21281451/using-optimization-to-improve-replenishment-transportation-efficiency
- Supply Chain Brain — Green and Cost Savings Can Go Together: https://www.supplychainbrain.com/articles/37200-green-and-cost-savings-can-go-together-really
- Supply Chain Brain — Breaking Down Planning and Operations Silos: https://www.supplychainbrain.com/articles/38817-breaking-down-the-planning-and-operations-silos-to-smooth-transportation-flow
- Supply Chain Brain — Removing Trucks from the Road to Cut Carbon Emissions: https://www.supplychainbrain.com/articles/39596-removing-trucks-from-the-road-to-cut-carbon-emissions
- Supply Chain Brain — AI in the Supply Chain (interview): https://www.supplychainbrain.com/articles/40028-watch-ai-in-the-supply-chain-fact-and-fiction
- Supply Chain Brain — Born at the Dock: Plan With Execution Built In: https://www.supplychainbrain.com/articles/44668-born-at-the-dock-plan-with-execution-built-in
- Supply Chain Brain — Transportation Under Pressure: https://www.supplychainbrain.com/articles/44667-transportation-under-pressure-planning-through-fuel-capacity-and-regulatory-shocks
- SDC Executive — Top Supply Chain Projects: https://www.sdcexec.com/sourcing-procurement/article/22657359/top-supply-chain-projects-supply-chain-visibility-helps-companies-work-smarter-together
- SDC Executive — Turning Modern Transportation Challenges into Opportunities: https://www.sdcexec.com/transportation/fleet-management/article/22862183/provisionai-turning-modern-transportation-challenges-into-opportunities
- Food Logistics — Rock Stars of the Supply Chain: https://www.foodlogistics.com/software-technology/supply-chain-visibility/article/22880958/rock-stars-of-the-supply-chain-disrupting-supply-chain-disruptions
- Food Logistics — How AI Smooths Transportation in Food and Beverage: https://www.foodlogistics.com/software-technology/ai-ar/article/22912551/provisionai-how-ai-smooths-transportation-in-the-food-and-beverage-industry
- Inbound Logistics — Top 20 AI Applications in the Supply Chain: https://www.inboundlogistics.com/articles/top-20-ai-applications-in-the-supply-chain/
- Inside Logistics — Smooth Operator: https://www.insidelogistics.ca/opinions/smooth-operator-new-tools-mesh-seamlessly-with-existing-software-to-optimize-fulfillment/
- Global Trade Magazine — AutoO2 Paves the Way for Greener Supply Chains: https://www.globaltrademag.com/provisionais-autoo2-solution-paves-the-way-for-greener-supply-chains/
- Bev Industry — The Present and Future of AI in Beverage Delivery: https://www.bevindustry.com/articles/96315-the-present-and-future-of-ai-in-beverage-delivery

**Leadership and company profile**
- Pulse2 — CEO Tom Moore profile: https://pulse2.com/provisionai-tom-moore-profile/
- Authority Magazine — Tom Moore interview: https://medium.com/authority-magazine/the-future-is-now-tom-moore-of-provisionai-on-how-their-technological-innovation-will-shake-up-277d3034cede
- SDC Executive — Pros to Know, Tom Moore: https://www.sdcexec.com/software-technology/software-solutions/video/22891232/provisionai-pros-to-know-provisionais-tom-moore-serves-as-evangelist-for-supply-chain-optimization
- The Middle Market — ProvisionAI merger: https://www.themiddlemarket.com/feature/data-is-advancing-deals-in-the-logistics-industry

## System integrations

AutoO2 and LevelLoad integrate with existing enterprise systems via standard API. No rip-and-replace required. Both products are designed to work alongside — not replace — existing TMS, WMS, ERP, and APS platforms.

**ERP systems:** SAP S/4HANA, SAP ECC, Oracle ERP Cloud, Oracle E-Business Suite, Microsoft Dynamics 365
**TMS systems:** Blue Yonder TMS, Oracle OTM, SAP TM, MercuryGate, Transplace, C.H. Robinson Managed TMS
**WMS systems:** Manhattan Associates WMS, Blue Yonder WMS, SAP EWM, Oracle WMS
**APS / planning systems:** Blue Yonder (JDA) Demand, o9 Solutions, Kinaxis RapidResponse, SAP IBP, Oracle Demantra
**RF / warehouse execution:** Zebra Technologies, Honeywell, Datalogic RF devices for load diagram delivery
**Data formats:** EDI, API (REST), flat file, direct database integration

## Frequently asked questions

**What is load optimization software?**
Load optimization software calculates the best way to arrange products on a truck to maximize payload, comply with axle weight laws, prevent damage, and meet customer requirements — all simultaneously. It goes beyond basic load planning by solving the full constraint set mathematically rather than applying rules sequentially.

**How is AutoO2 different from a TMS load builder?**
TMS load builders apply constraints one at a time and stop when compliance is met. AutoO2 solves every constraint simultaneously as a mixed-integer programming problem, finding the highest possible legal payload on every load. The difference is typically 5–10% more weight per truck — which translates directly to fewer trucks and lower freight cost.

**What is level loading in supply chain?**
Level loading (also called transportation smoothing) is the practice of distributing shipment volumes evenly across a planning period — typically 30 days — rather than allowing end-of-week bunching driven by inventory replenishment triggers. Even shipment flow enables carriers to commit preferred capacity in advance, reducing spot rate dependency and OTIF failures.

**How does LevelLoad differ from an APS system?**
APS systems optimize inventory replenishment but trigger shipments based on inventory thresholds — which causes daily volume spikes. LevelLoad operates downstream of the APS, taking the replenishment plan and redistributing shipments across a 30-day horizon constrained by dock throughput, carrier capacity, and warehouse labor. It does not replace the APS; it makes the APS output executable by the transportation network.

**Does ProvisionAI replace our TMS, WMS, or ERP?**
No. AutoO2 and LevelLoad integrate with existing systems via standard API and operate alongside them. The ERP or TMS continues to handle tendering, carrier communication, and execution. ProvisionAI solves the optimization layer those systems cannot.

**How long does implementation take?**
AutoO2 is typically live in 90 days. LevelLoad is typically live in 90–120 days. Both follow a structured implementation process with ProvisionAI's customer success team.

**What is the minimum volume to justify AutoO2 or LevelLoad?**
ProvisionAI's products are designed for operations shipping 5,000+ truckloads per year. Below that threshold, the ROI math typically does not support enterprise optimization software.

**What industries does ProvisionAI serve?**
CPG (consumer packaged goods), food and beverage, industrial manufacturing, grocery retail, and distribution. Any high-volume truckload operation with mixed-SKU complexity and tight carrier relationships is a fit.

**Can AutoO2 handle mixed-weight SKU loads?**
Yes. Mixed-weight SKU optimization — such as heavy bags combined with lightweight units — is one of the core use cases AutoO2 was built for. The Riviana Foods case study (America's largest rice producer) is a primary example.

**What is first tender acceptance rate?**
First tender acceptance rate measures the percentage of loads that are accepted by the carrier on the first tender attempt, without rejection or re-tendering. Industry average is typically 80–90%. LevelLoad customers achieve 97%+ because preferred carriers receive capacity commitments weeks in advance and are not surprised by volume spikes.

**Does ProvisionAI help with Scope 3 emissions reduction?**
Yes. By maximizing truck utilization (fewer trucks per shipment) and eliminating unnecessary truckloads, AutoO2 directly reduces Scope 3 Category 4 transportation emissions. ProvisionAI customers have eliminated 285,000 tons of CO₂ annually and removed 88,000 truckloads per year from the road.

## Glossary

**Load optimization:** The process of mathematically determining the best arrangement of products on a truck to maximize legal payload while satisfying all constraints — axle weight limits, cube, stacking rules, fragility, customer requirements, and state regulations.

**Axle weight compliance:** U.S. and international regulations limit the weight each axle of a truck can carry. Load optimization software must distribute product weight across the trailer to stay within per-axle legal limits, not just total gross vehicle weight.

**Truck utilization:** The percentage of a truck's legal payload capacity that is actually used on a given load. Industry average is 90–95%. ProvisionAI AutoO2 customers achieve 98%+.

**Level loading / transportation smoothing:** Distributing outbound shipment volumes evenly across a planning period to eliminate end-of-week spikes, enable advance carrier capacity reservation, and reduce spot rate dependency.

**First tender acceptance:** A carrier's acceptance of a load tender on the first attempt. High first tender acceptance (97%+) indicates that carrier capacity was reserved in advance and volumes were predictable.

**OTIF (On Time In Full):** A retail compliance metric measuring whether shipments arrive at the destination on time and with the correct quantity. OTIF failures trigger financial penalties from major retailers. End-of-week shipment bunching is a primary driver of OTIF failures.

**Crawl budget:** The number of pages a search engine or AI crawler will index on a site within a given time period. Crawl-delay settings in robots.txt directly affect how quickly AI crawlers re-index updated content.

**Mixed-integer programming (MIP):** A mathematical optimization technique that solves problems involving both continuous and discrete variables simultaneously. AutoO2 uses MIP to solve the full load constraint set — including discrete decisions like which product goes on which layer — as a single optimization problem rather than a sequential set of rules.

**Deployment schedule:** In supply chain planning, a time-phased plan that specifies when and in what quantity each product should be shipped from each origin to each destination, constrained by capacity at every node in the network.

**Spot rate:** A one-time freight rate negotiated for a single load, typically higher than contract rates. High spot rate dependency is a symptom of poor transportation planning and unpredictable shipment volumes.

**Preferred carrier:** A carrier with whom a shipper has a contractual rate and capacity commitment. Maintaining high first tender acceptance rates preserves preferred carrier relationships and avoids spot market exposure.

**Scope 3 emissions (Category 4):** In greenhouse gas accounting, Scope 3 Category 4 covers upstream transportation and distribution emissions — the CO₂ generated by trucking finished goods. Load optimization directly reduces these emissions by eliminating unnecessary truck trips.

## Key pages

- Homepage: https://provisionai.com
- AutoO2 product page: https://provisionai.com/autoo2/
- LevelLoad product page: https://provisionai.com/levelload/
- Solutions overview: https://provisionai.com/solutions-2-2/
- Case studies: https://provisionai.com/case-studies-ebooks/
- Kimberly-Clark case study: https://provisionai.com/case-study-kimberly-clark/
- Riviana Foods case study: https://provisionai.com/case-study-riviana/
- Fortune 500 CPG Leader case study: https://provisionai.com/case-study-unilever/
- Truckload freight cost reduction: https://provisionai.com/truckload-freight-cost-reduction/
- Sustainability & Scope 3 emissions: https://provisionai.com/sustainability-and-emissions/
- System integration & automation: https://provisionai.com/system-integration-automation/
- Agentic AI supply chain: https://provisionai.com/agentic-ai/
- Supply chain loss analysis tool (free): https://provisionai.com/loss-analysis-tool/
- OTIF Performance: https://provisionai.com/otif-performance/
- Digital Twins in Supply Chain: https://provisionai.com/digital-twins-in-supply-chain/
- A Costly Mistake: Load Planning Optimization: https://provisionai.com/a-costly-mistake-load-planning-optimization/
- Truck Loader's Guide: https://provisionai.com/truck-loaders-guide-2/
- Born at the Dock: https://provisionai.com/born-at-the-dock/
- Max Load case study: https://provisionai.com/max-load/
- About: https://provisionai.com/about-us/
- Blog / Resources: https://provisionai.com/blog/
- Contact: https://provisionai.com/contactus/