Fuel Analytics in Action: How AI is Reshaping LTL Logistics

Artificial Intelligence (AI) is reshaping businesses by delivering measurable results. Take demand forecasting for example – integrating AI has been shown to significantly enhance accuracy across various industries. In fact, McKinsey reports that AI-driven forecasting can reduce supply chain errors by 20% to 50%, leading to a reduction in lost sales and product unavailability by up to 65%.   

At their core, AI and machine learning excel at processing vast amounts of data to uncover trends, recognize patterns, forecast demand, and generate insights that drive smarter, faster decision-making. But data and AI alone don’t deliver transformation. To unlock AI’s full potential, especially in complex, variable environments like Less-Than-Truckload (LTL) logistics, it takes more than algorithms. It takes deep industry expertise.  

LTL is filled with nuances: partial shipments, dynamic routing, fluctuating capacity, and razor-thin margins. Without human context and domain-specific understanding, even the most advanced models risk optimizing for the wrong outcomes. 

“No AI runs outside human interference. For example, we’ve built an AI-driven LTL Optimizer product that is meant to work with a dispatcher. It can run as it’s by itself, but it really shouldn’t,” says Dr. Adi Raz, Titan Cloud’s Head of Data Science. “Because in real life, the dispatcher will know something the algorithm won’t. It might say a truck is available, but the dispatcher knows that truck went into service yesterday and is not available. There always needs to be human intervention.” 

For the retail fuel industry, informed use of AI translates into tangible business improvements, from smarter fuel replenishment strategies to reduced variance losses and optimized delivery planning. The benefits come at a time when the stakes are high: Research shows poor fuel inventory management and inefficient fuel logistics can cost retailers millions of in annual losses or increased costs.  

Here are three ways AI and data science are making a difference for fuel retailers today: 

Universal Dispatch 

AI systems today enable automated order creation and load planning by collecting data from multiple sources and combining those feeds for real-time data exchange. Incorporating information streams from ATGs, API feeds, driver updates, etc., a central data hub is used to determine load optimization, route planning, auto-generate orders for haulers, and delivery status to get ahead of any issues.  

In practice, this means a fuel operator can simply select a destination for the delivery, and the system’s AI-generated algorithms will optimize loads and routes using real-time traffic data and HAZMAT considerations. Loads and orders are automatically dispatched across drivers and haulers to hit their optimum ETA window, streamlining and expediting the process.  

The AI technology is flexible in its optimized output, whether the goal is to reduce transportation costs, maximize tank wagon utilization, or balance multiple KPIs. It supports split loads, Less-Than-Truckloads (LTL), and full-truckloads with notable benefits in complex LTL scenarios.

“The way it’s currently done in the industry, LTL is extremely computationally heavy. It can take hours and hours to run, and the more complexity and restrictions you add on, the more likely it is to not return an optimal solution,” says Dr. Raz. “AI can process massive layers of data—from how many orders need to be scheduled, to the priority of the orders, and whether the forecasting is tight enough in response to the priority of the order, etc.—to streamline processes needed to reduce logistics cost.” 

Going even further, AI-driven technology can address load specifics like short-truck deliveries, differing compartment configurations, and multiple product types.  

“Route optimization isn’t the hard part. It’s the load optimization that goes with the route optimization, given the huge number of constraints. For example, right now, we’re working with a customer who’s losing millions of dollars to driver overtime,” says Dr. Raz. “By integrating our LTL Optimizer, we can work with any combination of restraints to maximize their truck utilization, reducing hours needed and bringing overtime costs down to a fraction of what they are today. That’s the true power of this solution—minimizing the three major cost drivers: Trucks, fuel, and labor. 

Accurate Forecasting 

Using real-time data to strike the right balance between fuel supply and demand, retail fuel operators can avoid runouts as well as overstocking that ties up capital in excess inventory. Smart forecasting tools look at patterns like historical consumption data, upcoming weather, and local events to predict future demand. Combined with current inventory and delivery timing, these insights help prioritize deliveries based on Must Go, Should Go, Could Go, so fuel gets where it’s needed most, without waste or guesswork.  

By continuously refining these predictions with machine learning, the system becomes more accurate over time, enabling proactive decision-making and smarter logistics planning. This not only reduces operational costs and improves service reliability but also empowers fuel operators to respond swiftly to unexpected changes in demand or supply chain disruptions. 

Traditional forecasting methods rely heavily on historical averages, linear trends, and basic rule-based logic such as “in the summer – fuel demand increases by 5%”. While these traditional methods work, they are rigid and reactive – meaning they struggle to cope with new trends and patterns as they evolve. AI based forecasting, which relies heavily on machine learning models, is dynamic as it continuously learns from new data. It also considers far more inputs than traditional methods, allowing forecasts to be more nimble. AI can detect subtleties humans and rules will miss and it can adapt to changes in near real-time.   

Predictive Maintenance & Alarm Management 

Integrated AI-driven systems collect historical performance data from critical components including storage tanks, filters, pumps, and more. Advanced algorithms then analyze that data in real time to detect patterns and anomalies and produce actionable insights. Anticipating short and long-term future needs through AI-driven analytics enables fuel retailers to shift from reactive to proactive maintenance planning, reducing operational disruptions and downtime. 

On a day-to-day basis, for example, AI can refine ATG alarm protocol by pinpointing only those scenarios that require immediate attention. ATG alarms can often be triggered by a faulty sensor, fuel system testing, a wiring problem, or system connectivity issues. While these are important to address, they aren’t necessarily urgent.  

The numbers add up: Titan Cloud performed a 60-day assessment of one fueling chain comprising 700 sites. Nearly 30,000 alarms were detected. Of those, only 17,000 were compliance-related issues, 2,000 were actionable, and only about 350 required a field service dispatch—about 1% of all the alarms detected. By proactively identifying and prioritizing these events, AI-driven alarm response systems save fuel operators lost time on unnecessary investigations and service call costs.  

Where Innovation Meets the Real World 

At Titan Cloud, innovation is a commitment to solving real-world problems with purpose-built technology. Our investment in data science and applied AI continues to evolve alongside the needs of our customers and the fuel industry. LTL is the next example of this commitment.  

While scholarly articles on AI in LTL logistics are filled with theoretical success stories, many rely on simplified scenarios and controlled environments. But real-world downstream fuel operations are anything but simple. The unexpected is expected, processes shift daily, and constraints are plentiful.  

That’s where Titan Cloud stands apart. We’ve taken the complexity of LTL and built an Optimizer grounded in operational realities. The result? An AI-powered solution that drives real impact across routing, scheduling, and delivery—turning complex logistics into measurable results. 

Ready to see how AI can optimize your LTL strategy? Get in touch and let’s walk through what it could look like for your business.