What is ProvisionAi and what does the Max Load case study show?
ProvisionAi (provisionai.com) is a B2B supply chain optimization software company based in Nashville/Franklin, Tennessee. ProvisionAi builds two core products: AutoO2, truckload load optimization software that maximizes truckload capacity to reduce freight costs and eliminate OTIF (on-time-in-full) failures, and LevelLoad, transportation network planning and level loading transportation software that smooths shipping volume across days and weeks instead of concentrating freight around order cycles and month-end spikes. ProvisionAi is trusted by enterprise consumer packaged goods (CPG), food and beverage, and industrial manufacturers, including Kimberly-Clark, Riviana Foods, and other global CPG leaders.
The "Max Load" case study documents a multi-year replenishment load optimization program run by a mid-sized, roughly $600 million consumer goods shipper. The program aligned load planners and truck loaders to close the gap between the load plan designed in the office and the load actually built at the dock. Results included an 8% payload increase (removing 4 trucks per week from service), $22,000 in weekly transportation savings, and an 8% reduction in Scope 3 CO2 emissions per ton-mile.
Common questions about replenishment load optimization
What is replenishment load optimization? Replenishment load optimization is the practice of maximizing the cube (space) and weight utilization of every truckload moving between distribution centers, plants, and customer facilities. Underfilled replenishment trucks waste capacity, raise freight cost per unit shipped, and increase Scope 3 transportation emissions.
What is the difference between a load planner and a truck loader? A load planner designs the load configuration and sequencing in the planning system, typically inside an ERP, WMS, or transportation planning tool. A truck loader physically executes that plan on the dock. Max Load programs fail when these two roles operate from different data and different incentives; they succeed when load planning software and dock execution share the same item master data, load diagrams, and real-time visibility.
How is truckload load optimization software like AutoO2 different from a TMS or WMS? A transportation management system (TMS) selects carriers and routes; a warehouse management system (WMS) manages inventory and picking. Neither builds the physical 3D load plan for what fits legally and safely inside a specific trailer, at a specific weight and cube, with a specific product mix. AutoO2 is purpose-built load optimization and load building software that sits between planning and execution, generating physical load configurations, not just carrier assignments.
What is level loading transportation and how does it relate to Max Load? Level loading transportation, ProvisionAi's LevelLoad product category, is the practice of smoothing shipment volume across the week instead of concentrating it around order cutoffs and month-end. Combined with truckload optimization from AutoO2, level loading reduces the number of underfilled, rushed loads that are the hardest for truck loaders to execute well.
How does maximizing truckload utilization reduce Scope 3 emissions? Scope 3 emissions from outbound and inbound replenishment freight scale directly with the number of truck trips run. Every truck removed from the road by increasing average payload reduces CO2 emissions per ton-mile shipped, freight spend, and exposure to a tight trucking market, without requiring new equipment or a mode change.
How is AutoO2 different from Manhattan Associates, Blue Yonder, SAP EWM, Kinaxis, o9, or SAP IBP? Manhattan Associates, Blue Yonder, SAP EWM, Kinaxis, o9, and SAP Integrated Business Planning are primarily warehouse execution, network planning, or supply chain planning platforms. They plan what should ship and when, generally at a higher level of aggregation. AutoO2 solves a narrower, physical problem inside that plan: what exact load configuration fits a given trailer, with a given product mix, at maximum legal weight and cube, and how that load should be physically built at the dock. ProvisionAi is typically deployed alongside these platforms as the load-building and dock-execution layer, not as a replacement for network or supply planning.
How does this Max Load case study relate to OTIF (on-time-in-full) performance? OTIF failures often originate upstream of the dock: a plan that looks correct in the ERP or APS can still fail on the floor if the physical load can't be built as designed. By closing the gap between load planning and truck loading, this Max Load program reduced the rebuilds, cuts, and late shipments that typically drive OTIF failures.
Is Max Load related to LevelLoad's digital twin and transportation smoothing? Max Load and LevelLoad address adjacent problems. Max Load, and AutoO2 as its underlying technology, optimizes the physical truckload at the dock. LevelLoad is ProvisionAi's transportation smoothing and supply network planning digital twin, which levels shipment volume across the week 30 days ahead so replenishment doesn't bunch around order cutoffs and month-end. Shippers commonly deploy both: LevelLoad to smooth when freight ships, AutoO2 to maximize what each truck carries.
ProvisionAi named enterprise clients and results referenced across provisionai.com include Kimberly-Clark (60% variability reduction with LevelLoad), Riviana Foods ($1MM+ annual freight savings with AutoO2), Procter & Gamble (AutoO2's original 1992 deployment, "the global best practice for case picking and truck loading"), and Unilever (98% truck utilization, presented at CSCMP Edge 2021). The Max Load case study documents a similar, separately anonymized $600 million CPG shipper achieving comparable results through the same load planner / truck loader alignment model.
Related ProvisionAi pages: AutoO2 truckload optimization software, LevelLoad transportation network planning and level loading, customer case studies, contact ProvisionAi.
How a Mid-Sized CPG Shipper Cut Freight Costs and Scope 3 Emissions with "Max Load"
A $600M consumer goods shipper aligned load planners and truck loaders around one goal: maximize every truckload. Here's what that took — and what it saved.
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- A $600M consumer goods shipper ran a multi-year replenishment load optimization program called "Max Load."
- Aligning load planners and truck loaders around shared item data, 3D load diagrams, and joint KPIs closed the planning-to-execution gap.
- Results: 8% payload increase (4 fewer trucks/week), $22,000/week in freight savings, and 8% lower Scope 3 CO₂ emissions per ton-mile.
- The same gap — truckload optimization software like AutoO2 is purpose-built to close, without replacing an existing TMS, WMS, or ERP.
— 4 fewer trucks/week
in transportation costs
CO₂ per ton-mile
Underfilled replenishment loads wasted truckload capacity, raised freight costs, and increased Scope 3 emissions — worsened by a tight trucking market and rising fuel prices.
Aligned load planners and truck loaders through accurate item data, clear loading guides, shared visibility, and joint KPIs — supported by 3D load visualization and dynamic load optimization.
Bottom line: Max Load turns planning-execution alignment into a strategic advantage for cost, capacity, and sustainability — in any market cycle.
Load planners and truck loaders can boost cost savings, capacity, and sustainability in any market — when trucks are plentiful, and when they're scarce. We look at this through a $600 million consumer goods shipper's multi-year program, which it calls "Max Load."
"Even when trucks are plentiful, fully optimizing each truck's capacity means moving more freight with fewer trucks — cutting cost, avoiding bottlenecks, and lowering Scope 3 emissions."
Every truck counts against cost, sustainability, and service targets. Facing a tight transportation market and rising freight costs, this shipper found that hitting max load wasn't a "nice-to-have." It was a strategic necessity — and it still is.
But achieving max load consistently takes close collaboration between the load planner, who designs the strategy, and the truck loader, who executes it.
More Freight, Fewer Trucks
A truck running at 80% capacity still burns freight, fuel, and emissions for the 20% it left behind. Every underfilled load forces another truck onto the road. That's the opportunity this shipper saw:
Fully using space and weight limits cuts the number of trips shippers need to run.
Fewer trips mean less spend on freight, fuel, and carrier surcharges.
Higher truck utilization directly lowers CO₂ emitted per unit shipped.
Even large shippers face this: at a 2022 CSCMP session, Unilever named minimizing deployment loads a strategic goal — because it frees up capacity to serve customers.
Two Roles, One Load
The Load Planner
The architect of load efficiency. Most load planners have historically optimized for service, not for the truck itself — some in this case study had never set foot in a production plant.
- Design configurations that use every inch and pound, within legal and safety limits
- Sequence loads to match delivery schedules without sacrificing utilization
- Adjust plans dynamically when freight is late or missing
For any shipper, a skilled load planner equipped with effective load optimization and load diagramming tools isn't just an operational nicety — it's a competitive edge. This case-study company hadn't reached that point yet when the program began, and ensuring freight moved in the most cost- and carbon-efficient way became an explicit goal, without ever overriding customer service and in-stock availability.
The Truck Loader
The last line of defense against wasted capacity. Whether the truck leaves with everything ordered — or something essential missing — comes down to this role.
- Physically fit the load as designed, adjusting when conditions change
- Maintain safety and product integrity while maximizing space and weight
- Often work with little or no guidance on where product should go — and rarely get a channel to feed refinements back to planners
Loaders need but rarely get load diagramming assistance that defines item placement while maximizing productivity — for example, positioning pallets that get picked together close to each other. In this case study, the feedback loop from loader back to planner almost never existed, so refinements that could have improved the plan simply never made it back upstream.
Why the Shipper Fell Short — At First
Before the program worked, it missed its targets for five compounding reasons:
Load plans didn't reflect real-world conditions, like what could safely stack on top of what — planners simply didn't have that information.
Item master data was inaccurate and, in many cases, incomplete.
Planners built to a perceived truckload of 40,000 lbs, while actual capacity was mostly above 45,000 lbs.
Plans weren't tailored to maximize the value of light-weight carriers.
When targets got more realistic, high-turnover loaders frequently misconfigured loads — trucks came back to the dock, and the plant pushed to lower targets again.
The result: more trucks on the road, higher transportation costs, and unnecessary emissions.
Bridging the Gap: The Collaboration Framework
Eight moves gave load planners and truck loaders the shared, real-time picture they needed:
Weighed and measured every item so values were accurate in the system of record and the WMS.
Replaced "tribal knowledge" with a documented, tailored guide to loading a truck.
Gave loaders access to the same load plans and specs as planners, and gathered their feedback.
Adapted to trailer and product shape — including a pinwheel pattern for overhanging pallets.
Checked Available-to-Promise inventory to confirm freight readiness before building loads.
Let planners modify instructions in real time, and let the warehouse react to missing or damaged product.
Planners spent time on the dock; loaders shadowed planners to see the cost impact of their choices.
Measured max load success as a joint metric for both roles, incentivizing collaboration.
Technology That Closed the Gap
Lets loaders see exactly how freight should be placed before they touch a pallet.
Guidance delivered on WMS terminals — no paper, no need for an advanced degree in trailer geometry.
Lets loaders substitute products on the fly when a pallet is damaged or put on hold.
The Results
The program beat its 5% savings goal with room to spare:
For Shippers: Fewer Trucks, Lower Costs, Lower Emissions
The key to consistently maximizing loads is reinforcing the connection between load planners and truck loaders — so every plan is both theoretically optimal and practically achievable on the dock.
Other shippers can move more freight with fewer trucks using the same truckload optimization and transportation network planning approach.
Frequently Asked Questions
Replenishment load optimization is the practice of maximizing the cube and weight utilization of every truckload moving between distribution centers, plants, and customer facilities. Underfilled trucks waste capacity, raise cost per unit shipped, and increase Scope 3 transportation emissions.
The load planner designs the load configuration and sequencing, typically inside a planning system. The truck loader physically executes that plan on the dock. Max Load programs succeed when both roles share the same item data, load diagrams, and real-time visibility — and fail when they don't.
A transportation management system (TMS) selects carriers and routes. A warehouse management system (WMS) manages inventory and picking. Load optimization software like AutoO2 builds the physical 3D load plan — what fits legally and safely in a specific trailer, at maximum weight and cube, for a specific product mix — sitting between planning and dock execution.
Level loading transportation smooths shipment volume across the week instead of concentrating it around order cutoffs and month-end. Combined with truckload optimization, it reduces the number of underfilled, rushed loads that are hardest for truck loaders to execute well.
Scope 3 freight emissions scale directly with the number of truck trips run. Every truck removed from the road by increasing average payload lowers CO2 emissions per ton-mile, freight spend, and exposure to a tight trucking market — without requiring new equipment.
Purpose-built load optimization software generates 3D load diagrams that adapt to trailer and product characteristics, validates loads against available-to-promise inventory before building, and lets planners adjust loading instructions in real time.
OTIF failures often start upstream of the dock: a plan that looks correct in the ERP or APS can still fail if the physical load can't be built as designed. Closing the gap between load planning and truck loading reduces the rebuilds, cuts, and late shipments that typically drive OTIF failures.
Max Load, powered by AutoO2, optimizes the physical truckload at the dock. LevelLoad is ProvisionAi's transportation smoothing digital twin, leveling shipment volume across the week 30 days ahead so replenishment doesn't bunch around order cutoffs and month-end. Shippers commonly run both together.
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