Modern Freight Lane Analysis: How AI Powers Transportation Data Analytics for Profit and Efficiency

The freight transportation industry is snowballing and becoming more complex as time goes on. That’s why it’s more important than ever to use modern freight lane analysis tools to optimize efficiency and profit. One way to do this is by using machine learning and deep learning algorithms to create a more accurate picture of the transportation landscape. This allows businesses in the freight industry to identify the most profitable lanes and make changes where necessary. Additionally, AI-based dynamic pricing can be used to improve lane utilization and get the most out of each shipment. The benefits of applying modern solutions for freight lanes analysis are clear, and businesses that don’t utilize them will likely fall behind in the coming years.

Freight Metrics - How Are Freight Lanes Analyzed?

Freight analysis is the process of understanding lane-by-lane movement and identifying trends. This helps businesses make decisions about where to ship, how to price their services, and how to optimize their network. There are many different ways to analyze freight data, but most of them center around a few key metrics:

• Freight cost per lane and per km;
• Fuel costs (mean values and along the route);
• The volume of transported freight per each lane (lane density);
• Capacity utilization;
• Incoming and outgoing load traffic for each origin and destination;
• Incoming and outgoing price per km for each origin area and destination;
• Truck-to-load ratio;
• Transit times;
• Reliability;
• Headhaul and backhaul opportunities and deadhead probability scores;

These days, transportation data analytics is mostly done with the help of software that collects data from multiple sources, like transportation management systems (TMS), GPS devices, and digital freight marketplaces, and then uses it to generate insights. However, the traditional approach to freight analytics is reactive, meaning that it only tells you what has already happened.

This is where AI comes in.

Applying deep learning to transportation data allows businesses to not only understand what has happened in the past but also predict what will happen in the future. This predictive analytics is essential for making decisions about lane optimization, pricing, and capacity planning. The prescriptive part of machine learning models gives businesses specific recommendations about what actions to take in order to improve their freight performance. More about predictive analytics HERE.

AI and Big Data Analytics for Freight Lanes Analysis

The freight transportation industry is under pressure to improve efficiency and reduce costs. In the past, transportation data analytics was done using static data, resulting in many inefficient lanes and missed opportunities. But with the advent of big data and AI, supply chain and freight analytics has become much more accurate. Machine learning algorithms can now take into account millions of data points from real-time data, global data sources, and historical data and analyze lanes in a more holistic way.

Data-Driven Approach to Identifying Profitable Lanes

One of the most important things that businesses in the freight industry need to do is identify the most profitable lanes. The traditional way of doing this was to look at lane-by-lane cost and revenue data. In many cases, the most profitable shipping lane is not the one with the lowest cost but the one with the highest capacity utilization. And to find these lanes, businesses need to look at data beyond just price and revenue. By leveraging the power of ML and deep learning, companies can now access and process a lot more data points and understand how different dimensions interact with each other to identify the most attractive shipping lanes.

Estimating Location Attractiveness and Predicting Deadheads

Another important task businesses in the freight industry need to do is estimate the location attractiveness of each lane by identifying long-haul lanes. This is essential for making lane optimization, pricing, and capacity planning decisions.

With the help of machine learning, businesses can now dynamically estimate the attractiveness of loading/unloading locations. Thanks to that, logistic companies can now make much more informed decisions about lane optimization. Also, by using artificial intelligence to predict deadheads - empty runs that a truck makes when returning from its destination - businesses can further optimize their lane utilization and reduce costs.

Dynamic Network Analysis With Machine Learning vs. Static Lane-By-Lane Analysis

Most businesses in the freight industry use a static approach to freight analytics. This means that they only consider data from the past and present without thinking about what might happen in the future. However, this approach is no longer accurate enough because it doesn’t consider the volatility of truck demand and how different lanes and shipments intersect with one another. It also doesn’t view all the missed opportunities - from unsold freight, poorly organized schedules and routes, unused truck space for consolidated shipments, or inaccurate pricing.

To overcome these limitations, businesses need to adopt a dynamic network analysis approach that takes into account all the different route options and how they might change in the future. This is where another ML solution comes in - prescriptive analytics. Prescriptive analytics algorithms can automatically identify patterns and relationships in data that humans would not be able to see. They can also make predictions about the future and provide recommendations about the best course of action.

Transportation Analytics for Predictive Maintenance

Predictive maintenance is a process in which data from lane sensors and telematics devices are used to detect potential vehicle problems before they occur. This allows businesses to fix the issue before it causes any downtime or disruptions.

To make predictive maintenance more accurate, geospatial data from realized shipments and track & trace data need to be incorporated. By understanding which routes are more likely to cause problems for vehicles, businesses can make sure that their vehicles are better prepared for them or have a supporting infrastructure in place.

Improving Freight Management with AI-based Dynamic Pricing

To maximize profits, transportation companies and 3PLs need to optimize not only their operations but also their pricing. In the past, freight prices were mostly based on lane-by-lane cost and revenue data and the manual work of freight brokers.

With the help of AI algorithms, businesses can now automatically adjust prices in real time based on lane demand, capacity, and network optimization opportunities that are constantly analyzed and prescripted to network managers. The sole fact of automation in RFP response time with dynamic pricing attached to it saves a lot of money and time.

Prescriptive Analytics for Freight Management

Traditional logistic companies are deep-rooted in historical data analysis. More modern solutions provide a level of forecasting options with simple predictive analytics algorithms. To be truly competitive in such a rapidly changing market, businesses need to adopt a solution that provides them with the ability to not only predict the future but also prescribe the next steps.

AI-based prescriptive solutions can give businesses real-time visibility into their freight lanes’ profitability and the whole supply chain network. They also offer recommendations about how to optimize inefficient lanes and maximize profits.

Related case study: Delivering a dedicated IT system to manage and sell spot freight deals and plan transportationA 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.

Dynamic Pricing Is the Next Step in Leveraging Transportation Analytics

As mentioned earlier, with a proper system that enables freight management and analysis, companies can take one step further and utilize this data for profit maximization, churn prediction, and network optimization by adopting a dynamic pricing strategy. It produces freight quotes and rates in real-time based on shipping lanes conditions, demand for loads, availability of trucks, and optimization opportunities. Dynamic pricing allows logistics carriers and freight forwarders always to offer the most competitive prices and win more business.

Benefits of AI-Based Transportation Data Analytics

Transportation analytics can improve the supply chain strategy, optimize costs, and solve problems that are connected with freight management. Depending on the side, it can bring more specific benefits:

Benefits for Shippers

• Freight lanes analytics can help to save on transportation costs and better plan shipping budgets;
• it can improve customer satisfaction by predicting transit times and possible delays;
• it can help to identify problems, external factors, and inefficiencies in the supply chain.

Benefits for Freight Forwarders and 3PLs

• Access to real-time data on queries, orders, and loads in-progress and the opportunity to better manage and optimize company performance;
• it can help to identify lane opportunities and improve profitability;
• the ability to automatically adjust prices in real time based on lane demand, capacity, and network optimization opportunities with dynamic pricing models.

Benefits for Carriers

• A data-driven approach can help to optimize fleet utilization and reduce empty miles;
• it can help to identify long-haul lane opportunities and proactively find loads for inefficient shipping lanes;
• it can give more control over predictive and prescriptive maintenance by identifying common problematic routes and areas;
• more accurate route optimization analysis that takes into account all the different route options and how they might change in the future;
• real-time insight into their freight lanes’ profitability and the whole supply chain network;
• it can help to improve customer satisfaction by reducing transit times and delays.

Applying modern solutions for transportation analytics has numerous benefits for all the parties involved in transportation and logistics, be it shippers, carriers, or freight forwarders. By understanding the most efficient routes and being able to predict problems, businesses can save time and money, as well as improve customer satisfaction. In a rapidly changing market, having the ability to predict the future and prescribe the next steps is crucial for success.

The Future of Freight and Transportation Management is Proactive and Prescriptive

The shipping and transportation industry is evolving rapidly. To stay ahead of the competition, businesses need to be proactive and adopt solutions that will help them keep up with the changes and anticipate them. AI-based prescriptive analytics is the future of freight analytics and shipping lane management. With such solutions, businesses can always offer the most competitive prices and win more business.

If you’re looking to apply AI-based transportation data analytics for your business, reach out to the experts at nexocode. We have a team of dedicated professionals who can help you take your business to the next level with prescriptive analytics and dynamic pricing models that will optimize logistics operations and improve profitability. Contact us today for a free consultation!