Transportation Optimization as a Competitive Edge – Material Handling & Logistics


It’s harder and harder to find truck drivers today. With increased regulations and volatile markets, what used to be a profession of wanderlust and freedom of the road is struggling to attract top driving talent. And this has been going on for years. The American Trucking Association estimates that the U.S. will be short of 239,000 truck drivers by 2022.

The economy depends on truck drivers to move freight; as demand for goods grows, more truck drivers are needed, especially as more people order online for fear of the coronavirus. With projections for a more robust U.S. economy and growth, more and more truck drivers will be required to deliver freight, either directly to consumers or manufacturing plants, distribution centers and retail outlets.

So how do you find drivers for your fleet? The best drivers are those that take effective, evasive action to avoid accidents, take responsibility for work assets, maintain self-control in difficult and stressful situations, provide excellent communication and customer service, respect customer regulations and safety procedures, and demonstrate pride in being a truck driver. The paycheck of the best drivers is tied to your company’s success. When you hire great drivers, your entire operation gets better.

Once you’ve hired the best drivers, you need to ensure that you use the best driver for the trip. Optimization technology can help here, typically in conjunction with an intelligent transportation management system (TMS). Optimization engines within a TMS can match drivers with truck assets while incorporating hours of service (HOS) so that drivers who are chosen can take the trip and still follow HOS regulations.

TMS Optimization and the Driver Shortage

Optimization is defined as a quantitative and systematic methodology to search for the best answer to a specific problem based on numerous possibilities applied to given constraints. Optimization can apply to both product and process design, but in this article, we will only focus on process optimization, particularly the transportation process. Optimization uses algorithms to generate answers, a minimum or maximum value of an objective function subject to constraints.

Optimization uses deep mathematical techniques that often come out of operations research. These deep mathematical techniques help to provide concrete computational tools for solving a diverse set of problems. In our daily lives, optimization is used by transportation carriers to create delivery schedules for delivering directly to consumers, by airlines to support reservations, and by manufacturing companies for production planning.

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In transportation, optimization analyzes shipments, rates, lanes and constraints to generate realistic load plans offered at the best cost across the most efficient transportation network.

Intelligent TMS systems feature smart load selection, trip planning, driver selection and tour planning to lower costs by 30% to 40%, improve efficiencies and productivity, and increase customer satisfaction. An optimized TMS also helps trucking firms choose the most profitable loads and shows the best loads for drivers, considering their hours of service and personal preferences.

These features in an intelligent TMS help combat the driver shortage:

Smart load matching leads to more profitable loads. It helps to evaluate each driver and look at the load detail, see where the driver is located near the load, and then incorporate hours of service and see the load’s profit. The system considers thousands of potential shipments from load boards, inserts these into the driver’s route, assesses whether this load can be carried by the driver, and calculates the driver’s revenue per mile or per day. The system then recommends the best loads for carriers based on all this information.

Smart trip planning generates accurate trip plans for drivers that meet hours of service restrictions and provide more precise arrival times. The TMS generates an hour-by-hour plan for the driver considering available work hours, pick-up and delivery time windows, travel times, fuel stops, rest stops, and weigh stations. As each driver moves on the road, the trip plan and estimated arrival time are automatically updated.

Smart driver selection assigns the best drivers to loads using hours of service data. Drivers need to be selected based on what trucks they have experience driving. HOS regulations for the driver need to match the load. For example, if a driver has operated for 11 hours a day, the driver cannot take another trip that day and must rest for at least 8 hours. But if you have a load that requires him to drive 6 hours, then realistically, he has an additional 5 hours of driving he can do for the day. Do you have another load he can pick up and complete before his HOS limit kicks in?

Driver planning helps carriers create better driver routes with advanced optimization and scheduling. With optimization, your business runs fewer empty miles and better utilizes existing network capacity. You can prioritize turn runs so that drivers can come home every evening or more frequently, which improves driver retention. Drivers can get more miles, which increases their net pay, making them happy and willing to work more. With optimization in driver planning, you can move more freight with fewer drivers.

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Linehaul Planning Optimization

Linehaul transportation refers to freight movement along specific routes, moving goods from one warehouse or location to another. Freight can vary in volume and weight, from small packages to heavy pallets. Linehaul operations are very complex and include a network of stakeholders and infrastructure, including drivers, trucks, depots/terminals, hubs, distribution centers, warehouses, and sorting facilities.

Optimized load plans and schedules help reduce linehaul handling costs and improve service with better on-time performance. Automated linehaul planning solutions reduce plan creation time from days to hours. If the plan changes, using automated solutions, the plans can be quickly altered. For high-volume operations, plans can be created with endless day-of-week and week-of-month scenarios.

With an optimization engine built into the linehaul planning solution, you can perform what-if scenario analysis, such as modeling the impact of complex operational changes, such as gaining or losing a customer or switching zip codes or facilities. You can optimize the linehaul network by redesigning and optimizing break locations and service product offerings to save money and increase competitiveness. Linehaul plans can be adapted to include weather and terminal disruptions to mitigate service impacts.

Advanced algorithms can analyze the differences in demand by day-of-week or month so that you can make smarter decisions based on real-world variability. Some linehaul planning solutions create a digital twin of a network, allowing for sophisticated modeling of the optimal network. Proprietary time-space network simulations bring detailed insights and enhanced decision-making for transportation planners.

Route Optimization

Another function of optimization in the transportation industry is route planning and optimization. An optimized route considers how to deliver freight at the lowest cost while meeting the constraints and requirements of the order. Many businesses plan routes manually—on paper. But this method can’t possibly take into account all the possible ways to move the freight. An optimized route considers all the possible options, accounts for known constraints, and creates the best possible route for delivery at the lowest cost.

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Route planning is complicated and requires optimization engines that can analyze a large number of parameters, including:

• Single delivery point or multi-stop route

• Clearances of bridges, the width of streets and other limiting structures

• Potential times of traffic jams and street closures

• Road closures due to sporting events, school drop-off times, parades, races and more

• Time windows for pick-up, delivery and drop-off

• Labor requirements to help with loading/unloading trucks

• Limited truck zones or one-way streets

• Construction areas

• Load details, such as refrigerated or dry and whether any items need to be separated from each other, such as bleach

• Pallets or containers—do you need to return them to the provider?

• Truck length, weight, height, etc., which can limit which road a driver takes

• Drop off location parameters—docks, roof clearance, or any other limiting structures

• Type and size of gates at delivery location—can the truck fit through the gate? Is the gate locked, and how do you unlock it? Do you have to check in with security, which takes time?

• Historical pick-up density to ensure delivery routes are tuned to end at strategic locations at ideal empty times so that drivers can begin doing pick-ups

• Driver availability, including hours of service constraints

• Service times for each customer.

Because there are so many variables and constraints that need to be considered for this complex process, transport teams will benefit significantly from using an automated routing optimization solution. These software products help streamline the planning process to produce realistic and achievable plans that ensure costly transportation resources are used as efficiently as possible.

Some of these solutions offer continual re-optimization of the plan to save time and reduce friction on the road. These technologies continuously re-sequence the remaining stops based on time commitments, current traffic and new pick-ups so that drivers can ensure more accurate on-time deliveries. The results are a reduction in mileage and fuel and more satisfied customers—and drivers, too.

Belinda Rueffer is director of marketing with Axele, a provider of transportation management system (TMS) cloud software for truckload carriers. Christian Beatty is senior marketing manager with Optym, an optimization software company in the transportation industry.



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