How Optimizing Trucks at Planning Time Reduces Cost
How far can you go on $500 worth of diesel fuel? Sounds like a trick question, doesn’t it? The answer is it depends. As fuel prices continue to rise, supply chain planners can’t afford to use the same old unsophisticated rules-of-thumb to optimize truckload shipments. Volume and weight for example, do not provide true truckload utilization or footprint. And that’s not the only challenge facing the marketplace. Return of overweight transfers. Hazmat regulations considerations. Driver shortages. Customer delivery expectations. Tariffs and regulations, not to mention potential load damage. Planning and logistics drivers are many and complex, yet some logistics planners continue to utilize non-optimized systems for planning and execution of transportation logistics. With truckload optimization technology, you can maximize shipments for your end-to-end supply chain before commitments are made for downstream transportation planning requirements.
A Shift in How to Optimize Truckload Utilization
The traditional way of loading trucks is to consider truckload utilization in the execution phase of the logistics plan, which leads to underutilization of trucks. A paradigm shift has taken place however, with demand-driven load building—using demand data to build fuller truckloads that meet customers’ needs in the most cost-effective way.
The case of a large, North America consumer packaged goods company perfectly illustrates the challenge inherent in the old traditional way of truckload building. The organization’s shipment planning process was both manual and time-consuming, which led to a very conservative approach to what constituted a full truckload. There were instances of trucks that were penalized or sent back for being overweight, when in fact the truck wasn’t overweight, but rather the products were loaded on an axle. This manufacturer then dialed back their definition of an acceptable truckload—less than 90% capacity. That equaled lost revenue, lost opportunities, and increased transportation costs.
Deploying a load building application automated their entire process, and provided the ability to build larger trucks, increasing the volume on each truck, while reducing transportation spend by a conservatively estimated 4%.
The consumer goods company had significant volume and was able to ship 9,000 fewer trucks per year, eliminating 6.2 million miles, a major cost reduction but also a significant reduction from a carbon emissions perspective. They’ve seen approximately 60,000 metric tons of CO2 reduced by leveraging a load building solution in their planning operation.
Prioritize and Maximize Truckloads with the Right Solution
It’s important to remember that every time you deliver a suboptimal truck at 80% or 70% full for example, you’re still paying for that freight. Obviously, it makes sense to maximize your freight, but perhaps even more important, is to prioritize what’s on the truck, whether it’s promotion replenishments, expedited replenishments, normal turn-business replenishments or back-fill trucks with pull-ahead replenishments. A typical transportation management system (TMS) utilizes 90-95% of a truck’s capacity but the right truckload optimization solution can create a shipment up front that will utilize 98% or greater of a truck’s capacity. For companies with a high volume of truck loads, this improvement in utilization will have an immediate impact on their bottom line.
Reduce COGS with a Demand-Driven Logistics Plan
The on-demand economy continues to affect multiple industries and the transportation and logistics elements of the supply chain must also be demand-driven to enable growth and profitability. As planning cycles and decision-making timeframes continue to shrink, the benefits of truckload optimization not only allow you to maximize your resources at the highest levels but is also necessary to meet customer expectations.
- Reduce freight cost 4-8%
- Increase interplant, supplier freight utilization 7 – 15%
- Increase order delivery fill rate 5-10%
- Increase outbound pick/pack thru-put 30-40%
- Build not theoretical loads but feasible and executional loads
- Reduce greenhouse emissions