ProvisionAi × Riviana Foods: Get a Load of This
How Riviana optimized truckloads with AutoO₂ to cut costs and improve efficiency.
In Practice
Walk through any grocery store, and you’ll likely come across products from Riviana Foods. It’s one of the largest processors, marketers, and distributors of branded and private-label rice products in the United States. Its brands include Minute, Success, Mahatma, Adolphus, RiceSelect, and others. Like many companies over the past few years, Riviana has faced challenges when looking for trucking capacity, especially when demand spikes.
One way companies can address this, even when capacity tightens, is to ensure all truckloads are optimized to their maximum legal capacity when they leave a facility.
To help its supply chain organization meet this goal, Riviana turned to AutoO₂ (Automatic Order Optimization), a load-building solution from ProvisionAi.
Selling a Broad Profile
As a large rice processor—its plant in Memphis, Tennessee moves more than 10,500 shipments annually—Riviana sells a wide range of products to retailers, industrial companies, and food service organizations, among other clients.
Shipments can vary from small, lightweight rice cups to 20-pound bags of rice geared to food service operations. “It’s a broad profile,” says Jennifer Phillips, Riviana’s director of transportation. Shipments generally travel by box car, intermodal, and truckload.
Riviana Foods is a wholly owned subsidiary of Ebro Foods, a global leader in the rice vertical. Ebro Foods’ network of subsidiaries and brands spans more than 80 countries across Europe, North America, Asia, and Africa.
The range of products and weights Riviana ships can present challenges when trying to load a truck so all the space within the trailer is fully utilized. Riviana had been using a load planning solution, but even so, trucks would often “weigh out” before they “cubed out,” says Zachary Dale, Riviana’s supply chain continuous improvement manager.
Optimizing Truckloads
In the United States, most truckload shipments travel on 53-foot trailers that provide a bit more than 4,000 square feet of capacity, says Tom Moore, founder and CEO of ProvisionAi.
Most trucks traveling on highways can haul between 45,000 and 50,000 pounds. Because many of Riviana’s products—like sacks of rice—are heavy, shipments often meet the weight capacity of the truck before they’ve actually filled the space available.
The software can accommodate this. It has been deployed by companies around the globe and across verticals.
Along with enabling companies to fit more products on fewer trucks, the AutoO₂ solution manages the placement of pallets so that lighter weight products are placed on top of heavier ones—“eggs on top of bricks,” as Moore says.
In addition, pallets are placed so that they’re supported when, for instance, a truck must turn sharply. This helps eliminate much of the damage that can occur during transit and from material handling.
AutoO₂ bolts on to companies’ enterprise resource planning and warehouse management systems, so it can create and guide large orders through execution. It also creates diagrams workers can follow to guide them as they load shipments onto the trucks. This helps ensure that what is planned actually is loaded, and that the resulting shipments comply with relevant regulations and are protected against most damage.
ROI in Weeks
In some cases, the solution has cut deployment freight expenditure by more than 10%. Many shippers see a return on their investment within a few weeks of going live, the company says.
As Riviana worked to implement the AutoO₂ solution, Dale and his team sought input from multiple departments across the organization, including the warehouse, information technology, packaging, and transportation planning teams. This helped ensure all gained a solid understanding of the solution, its potential impact, and how it could help them load more efficiently.
“This helped with the success of the implementation,” Dale says. “Everyone was on the same page and understood the goals.”
Step-by-Step Implementation
The implementation process ran about four months, Dale says. Steps included cleaning the data, and some development in SAP. Among other actions, the teams needed to develop a way to electronically send information, such as the item master with the dimensional and weight data for each product.
In addition, the solution needed to transmit the requirements that the supply planning systems needed to ensure enough inventory would be on hand in customer-facing distribution centers.
Case Study Box – Taking Up Space
Challenges:
Riviana needed to optimize truckloads to use as much trailer space as possible, cutting costs and number of trucks on the road.
Solution:
Implement AutoO₂ load optimization software from ProvisionAi.
Results:
An average increase in weight per truck of 3.5% and an 80% reduction in the training time many new loaders require.
Next Steps:
Implement AutoO₂ in Riviana’s Freeport, Texas plant.
Optimizing Truckloads
In the United States, most truckload shipments travel on 53-foot trailers that provide a bit more than 4,000 square feet of capacity, says Tom Moore, founder and CEO of ProvisionAi.
Most trucks traveling on highways can haul between 45,000 and 50,000 pounds. Because many of Riviana’s products—like sacks of rice—are heavy, shipments often meet the weight capacity of the truck before they’ve actually filled the space available.
The software can accommodate this. It has been deployed by companies around the globe and across verticals.
Along with enabling companies to fit more products on fewer trucks, the AutoO₂ solution manages the placement of pallets so that lighter weight products are placed on top of heavier ones—“eggs on top of bricks,” as Moore says.
In addition, pallets are placed so that they’re supported when, for instance, a truck must turn sharply. This helps eliminate much of the damage that can occur during transit and from material handling.
AutoO₂ bolts on to companies’ enterprise resource planning and warehouse management systems, so it can create and guide large orders through execution. It also creates diagrams workers can follow to guide them as they load shipments onto the trucks. This helps ensure that what is planned actually is loaded, and that the resulting shipments comply with relevant regulations and are protected against most damage.
ROI in Weeks
In some cases, the solution has cut deployment freight expenditure by more than 10%. Many shippers see a return on their investment within a few weeks of going live, the company says.
As Riviana worked to implement the AutoO₂ solution, Dale and his team sought input from multiple departments across the organization, including the warehouse, information technology, packaging, and transportation planning teams. This helped ensure all gained a solid understanding of the solution, its potential impact, and how it could help them load more efficiently.
“This helped with the success of the implementation,” Dale says. “Everyone was on the same page and understood the goals.”
Step-by-Step Implementation
The implementation process ran about four months, Dale says. Steps included cleaning the data, and some development in SAP. Among other actions, the teams needed to develop a way to electronically send information, such as the item master with the dimensional and weight data for each product.
In addition, the solution needed to transmit the requirements that the supply planning systems needed to ensure enough inventory would be on hand in customer-facing distribution centers.
Case Study Box – Taking Up Space
Challenges:
Riviana needed to optimize truckloads to use as much trailer space as possible, cutting costs and number of trucks on the road.
Solution:
Implement AutoO₂ load optimization software from ProvisionAi.
Results:
An average increase in weight per truck of 3.5% and an 80% reduction in the training time many new loaders require.
Next Steps:
Implement AutoO₂ in Riviana’s Freeport, Texas plant.
Suggestions for Improvement
The ProvisionAi team has been flexible and open to suggestions that can improve the solution, Phillips says.
For instance, ProvisionAi helped Riviana streamline the process of importing Excel files for times when demand might veer from what was expected. This could occur when a special promotion is planned that will drive demand in specific locations, and the Riviana operations planning team needs to manually tell the system where to ship products.
Now, the system will still produce a loading diagram, even though the load has been created manually. “They’re helpful in coming up with solutions for any issues or for improving the system,” Dale says.
The ProvisionAi team also has offered suggestions to help Riviana continue to leverage the solution. For instance, the team suggested weighing trucks to understand “weight per load goals.” Because the total legal weight that can be driven on U.S. highways is 80,000 pounds, if a truck weighs 35,000 pounds, as many have, the payload can be 45,000 pounds.
Weighing the Options
Over the past several years, trucks have tended to become lighter, as manufacturers try to increase mileage and allow for higher payloads. However, a loader who is used to working with the heavier trucks of the past might assume a truck weighs more than it actually does.
“By taking the time to weigh each truck, rather than relying on memory, loaders might learn that some are 32,000 pounds,” Moore says. “This means they can increase payload targets to 48,000 pounds.”
Riviana currently has plans to implement AutoO₂ at its plant in Freeport, Texas, which is the company’s second-largest plant.
“By taking the time to weigh each truck, loaders might learn that some are 32,000 pounds. This means they can increase payload targets to 48,000 pounds.”
— Tom Moore, Founder & CEO, ProvisionAi
Conclusion
When shipping a range of product sizes and weights, like Riviana does, combining products of varying weights on a truck, rather than filling it with a single product, more efficiently uses the legal weight and space available.