The following is a guest post from Verun Shekhar.
The trade winds are shifting — and so is freight. As tariffs and trade policies continue to evolve, from rising tensions between nations to new import duties across key sectors, the freight market is feeling the impact. For shippers and carriers, these changes can trigger sudden lane imbalances, pricing volatility, and capacity crunches. But amid this uncertainty lies an opportunity: with the right data and analytics, companies can not only weather the volatility but strategically adapt and thrive.
At DAT, we sit at the intersection of freight and data, and in this post, we’ll explore how data science can light a path forward in a tariff-charged freight landscape.
The freight market under pressure: Impacts on shippers and carriers
Tariffs alter the cost calculus for global trade, prompting shippers to rethink sourcing strategies and carriers to rebalance capacity. For example, when tariffs rise on goods from China, some importers reroute cargo from China to alternative sourcing locations like Vietnam or Mexico. This redirection changes port activity, shifts inland distribution patterns, and impacts demand for certain freight lanes.
Carriers often need to reposition equipment to meet new demand flows, and the spot market reacts with price volatility. As capacity tightens or loosens in response to these trade shocks, shippers face challenges in forecasting transportation costs and ensuring service reliability. This is particularly critical for time-sensitive or high-value freight.
Tariffs as both risk and opportunity
While tariffs introduce uncertainty, they also create opportunities for agile players. Volatility in import volumes can increase demand for intermodal solutions or amplify pricing power on underutilized lanes. Carriers who can identify emerging hot spots early and reposition fleets accordingly can capitalize on market gaps.
Similarly, shippers who diversify suppliers or ports of entry in response to tariff changes may find new efficiencies or negotiate better rates. In both cases, the key to success lies in timely, actionable insights — something data science can deliver.
The data science edge: Turning uncertainty into strategy
At DAT, our platform ingests and analyzes millions of data points daily from freight transactions, rate movements, lane volumes, and macroeconomic indicators. Data science allows us to build predictive models that surface early signals of market change — such as a sudden drop in capacity on a port lane, or a spike in spot rates tied to trade disruptions.
For shippers, these insights enable smarter procurement strategies. With historical and real-time data, they can model “what if” scenarios to understand how tariff changes might affect costs and service on important lanes. For instance, they might compare rate trends between West Coast and East Coast ports when choosing where to bring in imports.
Carriers and brokers can use predictive analytics to optimize fleet positioning and pricing. Machine learning models can forecast where capacity will tighten, allowing for better load planning and more profitable operations. Anomaly detection tools can flag shifts in demand before they fully manifest in the market, giving users a first-mover advantage.
Example: Rerouting in a tariff-driven market
Consider a mid-sized shipper importing electronic goods. With new tariffs on components sourced from China, they shift some sourcing to Mexico. This changes their primary port from Los Angeles to Laredo, TX, and with that, their distribution strategy. Using DAT’s predictive rate tools and lane analytics, they model cost impacts, adjust routing, and negotiate more effectively with carriers.
Meanwhile, a regional carrier notices a rising number of loads posted on lanes into the Midwest from Laredo. By analyzing DAT load board trends and tools that assess and forecast market conditions, they reposition trailers from other markets and secure higher-margin freight, weeks ahead of competitors.
Recommendations
- Invest in freight intelligence tools: Real-time market data and predictive analytics help anticipate the impact of trade changes before they hit.
- Enable scenario planning: Run simulations on sourcing, routing, and capacity to understand the financial and operational tradeoffs of different tariff scenarios.
- Align data science and logistics teams: Foster collaboration between analysts and operators to rapidly implement data-driven strategies.
- Monitor early indicators: Use anomaly detection to identify subtle shifts in freight patterns tied to policy announcements or economic changes.
At a time when policy decisions can reshape global supply chains overnight, shippers and carriers can no longer afford to operate on historical instincts alone. With the freight market growing more complex and interconnected, data science isn’t just a competitive advantage — it’s strategically crucial and of vital importance. By harnessing the power of freight analytics, companies can move from reactive to proactive, turning tariff-driven disruption into a platform for innovation and growth.
