Freight procurement is a critical process for both shippers and brokers. Every year, companies work to secure transportation at the right price, with reliable carriers, while managing uncertainty. But the traditional ways of doing this — relying heavily on past rates, spreadsheets, and manual decision-making — are becoming less effective in today’s fast-moving freight market.
With shifts in trade policies, market cycles, and economic uncertainty, procurement has become more challenging. But it also presents an opportunity. As a Data Scientist at DAT with a background in supply chain management, I believe that data and technology can help us make better, more informed decisions when it comes to freight procurement. In this article, I’ll walk through how shippers and brokers can use data-driven strategies to approach procurement more effectively.
Challenges with traditional freight procurement
Historically, freight procurement has been based on a combination of relationships, experience, and rate history. While these elements are still important, they don’t always capture real-time market dynamics. Common challenges include:
- Using outdated rate data that doesn’t reflect current market conditions
- Limited insights into how different lanes behave over time
- Difficulty comparing proposed rates to what the market is currently paying
- Lack of tools to simulate how market changes might impact future freight costs
How data can help
Data science allows us to go beyond gut feeling. With the right tools, we can understand market trends, carrier performance, and lane behavior at a much deeper level. Here are some ways that shippers and brokers can apply data in the procurement process:
Understanding lane types and behavior: Not every lane in a network behaves the same. Some are consistent and predictable, while others are more volatile. It is often recommended to segment lanes into categories like:
- Core lanes: High volume and consistent
- Volatile lanes: Prone to capacity swings
- Seasonal lanes: Active only at certain times of year
- Segmenting lanes this way can help teams decide when to go to bid and whether to use contract or spot rates.
Rate Benchmarking with DAT iQ: With DAT iQ’s Benchmarking tools, shippers and brokers can compare their rates against anonymized market data. This includes contract and spot rate trends, and historical rate movement.
Having access to this kind of data gives you confidence when evaluating carrier bids or preparing for negotiations. Although there are no real market rates, pricing model like the DAT IQ’s benchmarking model, backed by real data science, can help shippers and brokers to get actionable market rates.
Forecasting with DAT Ratecast: Timing matters in procurement. DAT Ratecast uses machine learning to forecast rates on specific lanes up to a year in advance. Understanding where rates are headed can help you decide when to go to bid or when to adjust contract terms or even compare spot rates to contact prices.
Evaluating Carrier performance: Beyond cost, service quality is a key factor. Analyzing carrier scorecards — including on-time delivery, tender acceptance, dwell time and other KPIs — can help identify which carriers are most dependable. Over time, this helps build a more resilient network.
Scenario planning: Using DAT’s RateView Analytics, companies can simulate different scenarios:
- What happens if fuel prices spike?
- What if volumes shift from one region to another?
- How would a change in import patterns affect my Midwest freight lanes?
These simulations make it easier to prepare for uncertainty.
Example use case
Let’s say a mid-sized shipper is preparing for their annual bid cycle. Instead of using just last year’s rates, they use DAT iQ to benchmark their current pricing and identify which lanes are under- or over-market. They use Ratecast to forecast where prices are heading and decide to delay bidding on volatile lanes. For their core lanes, they use performance data to select high-performing carriers. The result is a procurement plan that is more aligned with the current market and better prepared for future shifts.
Key takeaways
Freight procurement is becoming more complex, but that also means there are more opportunities to do it better. With the right data and tools, shippers and brokers can make smarter decisions that lead to stronger partnerships and better outcomes.
At DAT, we’re building solutions that bring market intelligence, predictive analytics, and deep freight insights to the teams who need them. As data becomes more central to logistics, it is exciting to see how our industry continues to evolve.
On a closing note, I say that freight procurement is not just about buying and selling freight. It’s how you engineer the bid with the right data and tools to get the competitive edge you need.
