CASE STUDY Taking Up Space CHALLENGES: Riviana needed to optimize
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. truckloads to use as much trailer space as possible, cutting costs and number of trucks on the road. SOLUTION: Implement Auto02 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 Auto02 in Riviana’s Freeport, Texas plant.
With 100+ years of experience, Riviana is America’s leading rice company and the world’s largest marketer of wild rice. Its well-known brands include Success, Mahatma, and Carolina. The company moves more than 10,500 rice shipments annually.
The breadth of products Riviana offers and the range of pallet weights can also make it challenging to train new employees to properly load trucks. “More efficient loading and better truck utilization could cut both the number of trucks on the road, as well as transportation costs,” Phillips says. Generally, when shipping a variety of product sizes and weights, 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. Even when dealing with slight differences in weight, there are opportunities to take advantage of that difference in the load design. A GOOD FIT To help optimize truck capacity, Riviana began working with ProvisionAi. As Dale had been researching sustainability, a key initiative at Riviana, he came across information on the AutoO2 solution from ProvisionAi. “It sounded like a good fit and referenced a lot of the issues we were having,” he adds. Auto02 load building software uses machine learning to optimize shipments, while it also considers more than 300 parameters, including product and customer-specific loading rules, among others. For instance, a shipper may require that all shipments of a specific product family be placed in the tail of the truck, or that all pallets be shipped on the
narrow dimension. 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 Auto02 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 Auto 02 solution, Dale and his team sought input from multiple departments across the organization, including the
80 Inbound Logistics • April 2024
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