Minimizing the Risks Associated with Extreme Weather

Weather — once a predictable risk for global supply chains — is growing more erratic, striking with greater frequency and intensity

According to the National Centers for Environmental Information, the 1980–2019 annual average of weather events in the U.S. causing more than one billion dollars in damages was nine events. The annual average for 2020–2024 was 22 events. That’s a tenfold increase in five years; one that exacts significant direct and indirect impacts from the first to the last mile.

What makes weather impacts even more challenging for supply chain stakeholders to predict, and therefore prepare for, is that the type of event varies.  Consider the major disruptions in the past three years alone.  In 2022, Hurricane Ian’s landfall on the Florida coast in September caused shipments to drop 75% from previous weeks. Later that year, a Christmas-week freeze in Buffalo, New York, cut shipment volumes by 40% from previous weeks. During the Canadian wildfires in June 2023, deliveries in Chicago and New York City were delayed up to two days due to poor visibility. And in 2024, remnants of Hurricane Helene caused extensive flooding in North Carolina, severely impacting transportation infrastructure and halting operations at critical facilities. 

The Role of Advanced Weather Modeling in TMS

To mitigate these challenges, logistics organizations need to take proactive steps. Since they can’t stop weather events, they need to integrate advanced weather modeling into their transportation management system strategies, leveraging predictive climate models, hyperlocal forecasts, and decision-support tools, in order to build resilience and maintain efficiency. 

Traditional TMS platforms are built to focus on certain operational tasks: optimizing routes, managing freight loads, facilitating better carrier relationships and improving overall efficiency. However, without advanced weather modeling, TMS platforms can’t anticipate hyperlocal disruptions and respond to them proactively. Integrating weather intelligence into a TMS gives logistics companies the ability to change course based on actionable insights into how weather events may impact supply chain operations.  

Hyperlocal forecasts alert companies to sudden weather shifts. They enable logistics providers to make proactive decisions rather than reacting to unfolding events, in real time. Using hyperlocal weather intelligence, logistics companies can dynamically reroute shipments ahead of severe storms, reducing downtime and preserving service reliability. They can also improve safety conditions for drivers by identifying hazardous conditions, and suggesting alternative routes ahead of time.

Watching Out for Ice and High Winds

Weather intelligence delivers more than accurate forecasts; it also informs decision-makers of the potential impacts so that they can make confident operational decisions. For example, weather conditions are responsible for 23% of all roadway non-recurrent delays, costing trucking companies up to $3.5 billion dollars per year. Often, road delays are due to road conditions on a specific — and often short — stretch of the road, caused by multiple environmental factors including road temperature, vegetation and geography. 

And while most people think of icy, slick spots as the ultimate road risk, rain and wet roads are the leading cause of truck crashes, accounting for a significant portion of weather-related incidents. Our own DTN research has found that slush road conditions are a substantial cause of accidents. These conditions reduce traction, making it harder for trucks to stop and maneuver, and can also lead to hydroplaning, especially at high speeds. 

A hyperlocal forecast that takes these variables into account helps trucking companies avoid certain areas where they’re more likely to encounter unsafe roads. By integrating weather intelligence within existing fleet management systems, a truck company can map out routes that avoid dangerous conditions, and still deliver the goods at an acceptable time. 

High winds are a significant contributor to truck crashes, increasing the risk of rollovers, loss of control, and other accidents. The size and height of trucks make them particularly vulnerable to strong winds, which can cause them to sway, tip over, or even lose control. Additionally, improperly secured cargo can become airborne in high winds, posing a hazard to other vehicles. If a gust pushes a truck off course and causes a crash, a lawsuit by the other affected party could cost the trucking company significant amounts in increased insurance costs or verdict payouts.

Weather intelligence can be used to make strategic operational decisions. Predictive climate models using historical and real-time meteorological data offer the ability to forecast potential disruptions weeks or months in advance. These models help logistics providers anticipate seasonal risks — such as hurricanes, draughts or severe winter storms — allowing for strategic inventory planning. They also give companies insights to adjust long-term logistics strategies based on climate trends, reducing vulnerability to disruptions.   

Looking Ahead

As extreme weather events become more frequent and more disruptive, weather intelligence has become a strategic asset for logistics companies. They can move from reacting to forecasts to proactively and strategically anticipating the threats these events pose. Integrating advanced weather intelligence into your TMS outlook provides a financial and competitive edge that is essential for weather resiliency, logistic optimization and long-term profitability.

Renny Vandewege is general manager of weather and climate intelligence at DTN.