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In this tech blog, Varun Chitale, Staff Engineering Manager Artificial Intelligence, will dive into how the machine learning team at sennder is tackling the challenges of load pricing in road logistics. He will discuss the critical role of load pricing, and the challenges involved. Later posts will talk about the actual solutions.
In the road freight forwarding industry, pricing loads refers to the process of determining the cost of transporting goods from one point to another. Shippers and carriers must negotiate rates that are fair and competitive in order to ensure that goods move efficiently and at a reasonable cost. However, pricing loads can be a complex and time-consuming task, especially in the volatile road freight market where most of the pricing happens manually.
To address this challenge, the machine learning team at sennder developed algorithms using data-driven insights to optimize load pricing. These algorithms generate accurate and competitive rates by analyzing a range of variables, including the type of goods being transported, the distance of the route, and the current market conditions, benefiting both shippers and carriers.
However, there are several challenges that must be overcome in order to effectively apply machine learning to the task of load pricing. In the next sections, we will explore some of these challenges in more detail and discuss how the team at sennder is addressing them.
Limited data availability Limited data availability is a common challenge that is not limited to the freight forwarding industry. In many cases, pricing decisions are made based on a relatively small amount of data, which makes it difficult to identify patterns and trends that inform pricing decisions.
Imagine that a freight forwarding company like sennder wants to expand their services to a new lane in Europe, such as a route between Jelenia Góra, Poland and Graz, Austria. There may be limited historical data available about shipments along this lane, as it is a new and a relatively untested route. This makes it challenging to accurately predict market demand, carrier availability, and pricing trends.
Without sufficient data, a human may be forced to rely on estimates and assumptions, which can lead to pricing models that are inaccurate and inefficient. Machine learning algorithms help address this challenge by analyzing existing data from similar routes and creating predictive models to estimate pricing trends for the new lane. The ML pricing model helps sennder develop more accurate and effective pricing strategies, even in situations where data availability is limited.
Human variability Human decision making plays a significant role in the pricing process. However, human behavior can be highly variable and difficult to predict, which makes it challenging to develop pricing models that are both accurate and robust.
A practical example of human variability in European load pricing is the negotiation of rates for transporting wine. Let’s say a shipper wants to transport a shipment of wine from Bordeaux, France to Milan, Italy. The carrier may have a different perspective on what rate is fair based on their experience, available equipment, and capacity.
One carrier may be willing to accept a lower rate because they specialize in transporting wine and have existing relationships with vineyards and wineries in the region. Another carrier may charge a higher rate because they have a reputation for providing premium service and handling fragile cargo with extra care.
Furthermore, the shipper may have different priorities and preferences when selecting a carrier. They may be willing to pay a higher rate to work with a carrier who has a proven track record of delivering wine on time and in good condition. Or, they may be more focused on keeping costs low and opt for a carrier with a lower rate, even if it means sacrificing some level of service.
These varying perspectives and priorities lead to a wide range of pricing outcomes, making it difficult to develop a one-model-fits-all strategy for pricing.
Dynamic market conditions The freight forwarding industry is highly dynamic, with market conditions constantly changing. This makes it difficult to develop adaptable pricing models that react to new information in real-time and provide accurate rates that reflect current market conditions.
Brexit is one example of dynamic market conditions affecting the freight forwarding industry. The United Kingdom (UK) decision to leave the European Union (EU) had a significant impact on cross-border trade and transportation, leading to a shift in market demand and carrier availability.
After Brexit, there was a surge in demand for cross-border transportation services between the UK and the EU as businesses scrambled to adjust to the new regulatory landscape. This increased demand led to a shortage of available carriers and equipment, driving up prices and causing delays.
At the same time, the UK government implemented new customs regulations and procedures, which added additional complexity and cost to cross-border transportation. These regulatory changes made it more difficult for freight forwarding companies to accurately predict market demand and develop pricing strategies that reflected the evolving market conditions.
Complex pricing structures Freight forwarding pricing structures are highly complex, with a wide range of factors influencing the final rate, including:
Toll fees: Many countries in Europe have toll roads, bridges, and tunnels that are used for transportation of goods. Toll fees vary based on the type of vehicle, weight, and distance traveled. These fees can significantly impact the overall cost of transportation and must be factored into pricing structures.
Fuel surcharges: Fluctuations in fuel prices also impact pricing structures. To mitigate the risk of rising fuel prices, many freight forwarding companies include a fuel surcharge in their pricing structures. These surcharges are often based on market indexes and vary depending on the route and the carrier.
Accessorial charges: Accessorial charges are fees that are not included in the base rate and are charged for additional services such as detention, waiting time, and loading or unloading. These charges are complex and vary by country and carrier, making it challenging to accurately predict and incorporate them into pricing structures.
Seasonal variations: Some routes experience significant seasonal variations in demand and carrier availability. For example, in the summer months demand for transportation of goods to popular tourist destinations increases, leading to higher prices. Conversely, the winter months may see lower demand and lower prices in those same destinations.
Capacity constraints: During peak seasons, carriers may face capacity constraints, which impact pricing structures. For example, a carrier may charge a premium rate during peak season to compensate for the higher demand and limited capacity.
The traditional European road freight forwarding industry is complex, with dynamic market conditions, limited data availability, and varied pricing structures. At sennder, we face the same challenges in accurately predicting market demand and developing pricing strategies that reflect these complexities.
However, as a digital freight forwarder we have access to vast amounts of data through our platform. We leverage machine learning algorithms to analyze that data, identify patterns and trends in pricing, and develop predictive models that accurately estimate pricing trends. This approach helps us navigate complex market conditions, optimize pricing structures, and provide more accurate and competitive pricing to our customers.
Machine learning is a powerful tool that enables sennder to remain at the forefront of the industry, delivering innovative solutions that help our customers achieve their goals. By leveraging this technology, we develop more accurate and effective pricing strategies that reflect the complex market conditions and provide our customers with cost efficiency.
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