High-volume process industries often focus on replacing foundational systems like demand and supply management systems, forgetting that optimizing load building is crucial for reducing freight costs and improving efficiency across the supply chain. Load planning and optimization software acts as a vital bridge between supply planning and the ERP.
This blog will cover:
- How legacy load planning leads to freight inefficiencies, i.e., waste.
- The significant cost and CO2 reductions achieved with ProvisionAi’s AutoO2 load optimization software.
- Case studies showing the transformative benefits of load optimization.
Why Digital Transformations Must Include Load Optimization
Digital transformations that focus on replacing demand and supply planning systems, transportation management systems (TMS), warehouse management systems (WMS), and even the enterprise resource planning system (ERP) miss optimizing the element that drives much of supply chain costs. They should replace their legacy load-building to enhance supply chain efficiency and effectiveness.
The Importance of Load Building
Firstly, let’s understand why load building is necessary. Systems such as supply planning or materials requirements planning create unstructured needs—for example, a few cases or pallets of product. For efficient supply chains, purchases or replenishment must be in full truckloads. These truckloads are generally made up of a mix of products for inventory efficiency. Thus, a load builder is needed to convert these unstructured requirements into logistics-efficient aggregations.
The Downside of Legacy Load Builders
Unfortunately, these legacy load builders do a terrible job of maximizing the capacity utilization of each vehicle or container. They mostly use simple algebraic rules that produce sub-optimal loads. An extensive analysis of hundreds of thousands of shipments has shown that more than 90% of all trucks moving in the United States have significantly wasted weight or cube capacity.
Building logistics-efficient loads is hard. Consider the following example—the goal is to ship as much as possible:
- Two trucks, each with a capacity of 45,000 pounds.
- Two different pallets, 20 of 2200 pounds and 22 of 2000 pounds
Here is what the legacy load builder did to create two trucks:
Inefficient Payloads With Legacy Load Builders
However, observe that the weight capacity of each truck is not entirely used. What could be done to add another 2000-pound pallet? Using more advanced math, it’s possible to solve this problem. The solution is shown in the following diagram:
Optimize to Improve Payload
Could you do that?
Now consider the additional complexities that must be accounted for:
- There are many items with differing weights and density
- The load must be built to be axle-legal for all the states or countries it crosses.
- Should a driver brake rapidly or swerve, the product should not shift or be damaged.
Legacy load builders won’t cut it.
How ProvisionAi’s AutoO2 Solves Load Optimization
Load optimization can be defined as maximizing payload within all the operational and legal constraints. In the continental U.S., these constraints include a gross combination weight limit of 80,000 pounds for trucks, trailers, and payloads, as well as strict axle weight restrictions (see diagram). Certain states have axle replacement requirements, which means the load must be able to be positioned differently to enable the axle weight limits to be met. For instance, California’s regulations stipulate that the rearmost axle cannot be more than 40 feet from the kingpin. This pushes the rear tandem forward and, as with the seesaw playground toy, brings significantly more weight onto the rear tandem. Additionally, it is essential to factor in product stackability – you wouldn’t want to stack bricks on top of the eggs! It’s also required to segregate products – for example, dog food can’t be put near detergent.
But that’s not all – many other requirements drive operational efficiency, such as:
- Maintaining pairing when items are stored double-stacked
- Trying to avoid turning pallets — if they are stored on the narrow dimension, try to maintain that.
- Maximizing the ability to use specialized material handling equipment like double-wide forklifts or triple pallet jacks
Other outside factors often come into play, such as:
- Sometimes, there is a shortage of available vans. Load building must prioritize what is shipped to match trailer availability. The goal is to minimize the opportunity for shortages at the customer-facing distribution center.
- Different vehicles have varying payload capacities.
- Container dimensions may limit product placement—for example, they may have refrigeration units that hang from the ceiling, preventing double stacking.
ProvisionAi’s AutoO2 addresses all these complexities, delivering optimized, efficient load solutions.
Ebook: Transportation Planning
Helping the Forklift Driver Execute Optimized Loads
It is one thing to design a shipment and another to make it happen at the dock. After all, good companies plan, and great companies execute. Unfortunately, many companies have reduced their target payload weight or cube to accommodate loaders’ inability to execute (Get all the product on the truck). This is a costly mistake that AutoO2 can overcome.
Here is a case study: A major consumer products company significantly reduced the maximum weight its planners could put on each shipment destined for their distribution center in California. Using AutoO2 and its load diagrams, they improved the load factor (payload) by a staggering 10%.
Load Optimization: A Critical Link Between Supply Planning and the ERP
Load optimization uses supply planning requirements and item master data. The output can be a purchase, stock transfer, or vendor-managed inventory order that is transferred to the ERP and a loading diagram that goes to the floor.
Load Optimization Sits Between Planning and the ERP
Why Replacing Legacy Load Builders with AutoO2 Matters
Legacy load builders hinder both cost efficiency and environmental progress. ProvisionAi’s AutoO2 provides a significant competitive advantage by optimizing load planning, reducing costs by 5-10%, and cutting Scope 3 emissions.
Scope 3 emissions are a category of greenhouse gas (GHG) emissions that occur outside of an organization’s direct control but are still a result of its activities, such as the transportation of products from plants to distribution centers. Reducing these emissions is increasingly prioritized as organizations seek to cut their carbon footprint.
For example, consider shipments of heavy products (Pallets of 1,500 to 2000 pounds each) to a site in California. The graph below shows how optimization drives substantially bigger loads than the original shipments created by the legacy load builder, shipping the same amount of product on 10 fewer trucks!
Optimized Load Building Out Performs Legacy Approaches
Expanding Efficiency to Customer Loads with AutoO2
So far, the focus has been on loads that replenish inventory. Customer loads, which may be up to half the freight spend, must also be optimized. But customers’ orders cannot be changed—they must be precisely what the customer ordered. Similarly, incremental items cannot be added to fill them.
There is a solution to improving customer orders. Procter & Gamble spoke at an environmental conference and outlined the following:
- Freight costs are built into the products’ price
- The price of the product depends on how much is ordered
- Customers must order truckloads according to P&G’s order guidelines to achieve the best price.
- Adjusting the guidelines for what constitutes a “truckload” too stringently could prevent the sites from executing the shipments correctly (i.e., the customer’s order would be incomplete or damaged). Definitely not a good thing
- Procter employed AutoO2 to guide pickers and loaders in executing customer shipments.
- AutoO2 made picking and loading a repeatable process – any picker or loader produces the same result on the same order.
- The repeatability generated by AutoO2 allowed P&G to raise order minimums (i.e., the requirements of what makes up a truckload). Customers ordered more for each shipment, generating significant incremental freight savings.
Case Study: A Mid-Sized Company Sees Big Savings
Riviana Foods, a manufacturer of rice products, implemented AutoO2 in their Memphis plant. Not only did they plan each shipment, but they also gave loaders the AutoO2 guidance about how to place each pallet on the shipment. Guidance was necessary because:
- Riviana takes advantage of some lightweight equipment, enabling loads to be built much larger than the standard trucks it uses.
- As the company grows, it hires new forklift drivers. Without product experience and sometimes even truckloading expertise, they struggle to get shipments to pass the scale.
- Mitigating damage is a priority.
Riviana’s results are impressive:
- $1mm freight savings from a single plant
- Less damage
- Faster training of lift-truck drivers
- Ecological benefits (Reduce Scope 3 emissions)
These are outlined in this testimonial from their transportation leader:
Unilever Talks Freight Cost Reduction with AutoO2
Unilever’s head of supply chain spoke about load optimization generating substantial benefits for their operations:
- Reduced transportation cost
- CO2 reduction
- Operational efficiencies such as fewer trucks to load and unload
What was most interesting to Unilever was that using AutoO2 could free up preferred carriers’ equipment to ship loads to their customers, driving enhanced service.
Load Optimization: Essential for Modern Supply Chains
Load optimization is indispensable in today’s supply chains. It reduces excess freight costs and aligns with corporate environmental goals by minimizing carbon emissions. Load optimization should be the first step in any supply-chain transformation. Companies integrating load optimization tools like AutoO2 into their digital transformations see financial and operational benefits. Don’t overlook this crucial step in building an efficient, sustainable supply chain. You’ll be glad you did it.
