Optimize LTL Freight Management with Dynamic Pricing Models

As the world of logistics and transportation continues to evolve, so too must the way that businesses manage their freight operations. One key area ripe for change is how rates for Less than Truckload (LTL) shipments are handled.

LTL freight can be challenging to manage due to the numerous variables involved, such as the number of shipments, the size and weight of each shipment, the distance traveled, and so on. This means that traditional pricing models are often ineffective in predicting or optimizing LTL shipping costs.

However, dynamic pricing models can help businesses better understand and manage their LTL freight costs. Find out how in this article.

The Complexity of LTL Transportation Services

LTL transportation involves the movement of shipments that are too large for parcel carriers but too small to fill an entire truck. This type of freight is typically palletized and can weigh anywhere from 150 to 15,000 pounds.

The challenge with LTL freight is that it doesn’t fit neatly into either the parcel or full truckload (FTL) shipping categories. This makes it more difficult to find carriers and negotiate rates.

In addition, LTL shipments are often more complex than FTL shipments, as they often involve multiple stops and transfers. This can add to the complexity of the shipping process and lead to delays and unexpected costs.

Unlike in FTL shipping ( read more on dynamic pricing for FTL), where deliveries are scheduled for the following weeks, LTL shipments are usually not planned ahead. Most of the deliveries need to be fulfilled within a couple of days. This unpredictability can make it challenging to manage LTL freight costs. In addition, the complex nature of LTL shipments can make it hard to find carriers willing to take on the last-minute shipment.

Dynamic LTL Pricing Strategy

A dynamic pricing strategy can help businesses better manage their LTL freight costs and rate management. With this type of pricing, rates are constantly changing in response to market conditions. This means that businesses always know the current rates and can adjust their shipping plans accordingly. This solution can be applied by shippers, carriers, and even third-party logistics providers (3PLs).

In addition, dynamic pricing models can help businesses predict future rates and trends. This type of forecasting can be beneficial for companies that ship LTL freight regularly. By understanding the current market conditions, businesses can make informed decisions about their shipping plans and pricing strategies. Read more about pros and cons of dynamic pricing HERE.

There are several benefits to using a dynamic LTL pricing strategy:

• It can help businesses save money on LTL shipments.
• It can help companies to find the most efficient shipping routes.
• It can help businesses avoid delays and unexpected costs.
• It can help produce LTL quotes in real-time.

However, a dynamic LTL pricing strategy is not without its challenges. In particular, it can be challenging to connect the pricing strategies with logistics optimization.

How to Optimize the Process For Successful Rate Management?

One way to overcome the challenges of producing LTL quotes and pricing is to build a dynamic LTL pricing engine. This system uses artificial intelligence (AI) to monitor market conditions, planned shipments constantly, and carriers’ capacity data to adjust rates accordingly and optimize LTL shipping routes automatically. In addition, it can provide real-time quotes for LTL shipments.

Moreover, an AI-powered dynamic pricing engine can be customized to meet the specific needs of a business. This means that companies can tailor the system to their own unique shipping requirements.

Predicting LTL Freight Demand

The first and most obvious factor important for dynamic pricing models and the process of producing LTL quotes is predicting future freight demand. After all, if businesses don’t know when and where they will need to ship their goods, it will be challenging to plan ahead and find the most efficient shipping routes. In addition, without understanding future demand, businesses will be less likely to leverage that information into a profitable pricing strategy. Forecasting demand can be made by using a data-driven approach that analyzes past trends and patterns to consolidate them with current data points from market research.

Tracking LTL Carriers Supply and Capacity

The second important factor for dynamic pricing models is tracking carriers’ supply and capacity. To find the most efficient shipping routes and most attractive shipping deals, you need to understand which carriers will have available capacity and where. In addition, this information can be used to negotiate better rates with carriers through the dynamic pricing model.

Tracking carrier data can be done by using a system that constantly monitors the number of trucks available, the number of offers from carriers, as well as the number of shipments booked. This information can then be used to predict future trends and adjust rates accordingly. A successful dynamic pricing model should also accommodate carriers’ segmentation that additionally values carrier relations.

Monitoring External Market Conditions

The third important factor for dynamic LTL pricing models is monitoring external market conditions (oil cost, economic indicators, etc.). After all, if there are sudden changes in the market, it could lead to lower rates or rate increases. For example, if there is an increase in fuel prices, it will lead to an increase in LTL rates. On the other hand, if there is a decrease in the volume of eCommerce orders, it will eventually lead to a decrease in demand for LTL freight, losing long-term contracts and future sales.

Shipments Consolidation

To optimize the shipments and eventually the final price, it is crucial to consolidate shipments. This involves combining multiple small shipments into one larger shipment. Shipments consolidation is a vital part of successful LTL rate management. By consolidating shipments, businesses can reduce their shipping costs and simplify their shipping process. But how do you approach it in real-time at the shipment request stage?

It is impossible for carriers or freight forwarders (brokers) to monitor all planned shipments manually and compare them with each other and every new shipment request. This process needs to be automated. And this is where machine learning technology comes into play.

Most of the existing software solutions on the market use pre-defined rules to consolidate shipments. This means that they are not able to monitor all planned shipments and identify opportunities for consolidation in real-time. As a result, many potential savings are missed.

To overcome this challenge, businesses need to apply AI-based solutions that can automatically identify opportunities for consolidation and optimize shipping routes in real-time (matching lane imbalances, different sizes, LTL carriers’ capacity, schedules, etc.). By using an AI-powered system, companies can save time and money on their LTL shipping.

Offering Pickups Along the Route

Knowing that a particular shipment won’t fill the whole truck and that there is enough time to fulfill another load is crucial for freight forwarders (brokers) who want to offer their clients the best possible price. It allows them to find other shipments that are going in the same direction and offer a more competitive price by consolidating multiple shipments into one truckload after the initial order has been placed. But how to post and manage transportation offers and identify consolidation opportunities in real-time?

The traditional way of finding these opportunities is to manually monitor all planned shipments and compare them with each other. This is a time-consuming and error-prone process. The alternative is to use an AI-powered solution that can automatically post offers for additional pickups, identify attractive opportunities for consolidation and optimize shipping routes in real-time.

Route Optimization

Route optimization is another critical part of successful LTL rate management. By optimizing shipping routes, businesses can reduce shipping costs and improve delivery times. Targeting the route optimization problem and the aforementioned shipments consolidation problem is essential. The main reason for that is the fact that most of the existing software solutions on the market use pre-defined rules to optimize shipping routes. This means that they cannot monitor all planned shipments and identify opportunities for optimization in real-time. As a result, many potential savings are missed that otherwise would have the chance to improve pricing strategy.

For freight forwarders and carriers, this is especially important as with good shipments consolidation and route optimization solutions, they can dynamically offer lower prices when they see an excellent opportunity for consolidating multiple loadings into one truck. That way, they can win more attractive freight deals and remain competitive.

Again this cannot be adequately solved manually. Companies that want to leverage the benefits of dynamic pricing that takes into consideration route optimization need to apply AI-powered solutions.

Carrier Matching

Carrier matching is another vital aspect of LTL rate management. To get the best possible rates, businesses need to match their shipments with suitable carriers. This can be challenging as companies need to monitor carrier availability and shipment requirements constantly.

Load to carrier recommendation is a process of automatically matching load requirements with the right carrier. This is done by taking into account many factors such as carrier availability, shipment requirements, and current rates. At the LTL quote stage, businesses are not booking carriers yet. However, to produce a valid quote, they need to predict carriers’ availability and their costs/quotes to realize the shipment.

Machine learning algorithms can automatically monitor carrier availability and identify the right carriers for specific shipments. Semi or fully automated AI-based digital freight matching solutions are the future of carrier matching marketplaces.

Optimizing for Headhauls and Backhauls

When planning a route, it is important to take into account not only the current load but also future loads. This can be a challenge as businesses need to monitor their shipments and plan their routes accordingly constantly. However, by taking into account future loads, businesses can optimize their routes and save on shipping costs. Machine learning algorithms can automatically monitor shipments and predict opportunities for headhauls or backhauls for a particular unloading zip code and date/time. Similarly, considering the low probability of a headhaul/backhaul from some areas, the dynamic pricing model should suggest higher prices for the original request as part of the LTL rate management process that accommodates the possibility of an empty run.

Finding Best Time to Book Carrier

The final challenge businesses face when managing their LTL rates is finding the best time to book their carrier.

This task aims to find the optimal time to book the carrier, considering both the ship date and the delivery date. The main idea is to use machine learning algorithms to monitor carrier availability and shipment schedules automatically. Based on that information, the algorithm will suggest the best time to book the carrier and automatically start the booking process when the time comes. The dynamic pricing engine behind the solution should also consider that factor in the final price.

AI and Data Science to Compile It All Into a Dynamic LTL Pricing Engine

All of the tasks and challenges mentioned above can be solved with the help of AI and data science. By leveraging the power of data, businesses can gain a competitive advantage in the market and optimize their LTL rates through dedicated, dynamic pricing engines that not only consider supply and demand factors but also solve multiple optimization problems in LTL transportation.

There are many different software solutions on the market that claim to offer dynamic pricing capabilities. However, most of these solutions are not genuinely dynamic. They use pre-defined rules to generate prices, which means that they cannot adapt to changing market conditions in real-time. The only way to create a truly dynamic pricing engine is by using machine learning algorithms.

With the help of data, businesses can train their algorithms to adjust dynamic pricing based on changing market conditions automatically and remain competitive in the dynamic industry like LTL transport. In addition, machine learning algorithms can also be used to automatically handle logistics network optimization tasks mentioned above by connecting different factors, such as shipments consolidation, carrier matching, or route optimization.

Related case study: Delivering a dedicated IT system to manage and sell freight deals and plan transportation for LTL shipments

A logistics company approached us to create a dedicated IT system to handle their core business process - managing and selling logistics deals.

Our challenge? The key challenge in the logistics sector is cutting the time of concluding deals to an absolute minimum. The tool has to be very responsive and help in the smart matching of carriers and freight, fleet management, and other logistics operations. The platform helps shipping agents minimize fuel consumption, maximize operational efficiency, and optimize fleet performance by matching multiple loadings on a similar route with a single carrier. Read more about this case study.

Real-Time Pricing Strategy

Every day, logistics companies acquire hundred of thousands or even millions of new real-time data points about the cross-region road logistics industry. All these data points can be used to build the future of logistics marketplaces where pricing optimization, load-to-carrier recommendation, load search, and network optimization happen in real-time in an automated fashion. A true dynamic pricing engine relies not on static tariffs and fuel prices but on real-time data to generate prices that reflect the current market conditions and take into consideration opportunities for optimization.

How to Kickstart Dynamic Pricing Model Implementation?

Dynamic pricing is changing the whole logistics industry with its various applications for airline ticket pricing, container shipping, and land transportation. With massive needs for skilled workforce able to provide instant quotes the turn to automated dynamic quotes for LTL and FTL shipments based on AI solutions is just around the corner.

Data and visibility of the whole logistics network are the keys to success when it comes to dynamic pricing. To build a dynamic pricing engine, businesses need to have access to data about their own shipments or orders processed, as well as data about loads of their competitors. This data can be used to train machine learning algorithms that automatically generate prices, cutting service time-to-quote and maximizing revenue. In addition, businesses need to have visibility into the market conditions and the opportunities for further optimization to ensure they always get the best prices.

Because most software solutions on the market that provide dynamic pricing capabilities are not customized for each company, it’s vital to understand their limitations before selecting one. The best way to decide on a software solution that meets all of your company’s requirements is to speak with an expert who can understand your business needs and recommend the best approach for implementing dynamic pricing models.

If you’re looking to implement a dynamic pricing strategy for your LTL shipments, and you are a carrier, freight forwarder, or shipper, get in touch with us, and we will be happy to help you get started.