You’re a logistics manager. Fuel costs are skyrocketing, drivers are hard to find, and regulations keep changing. How do you keep your fleet efficient and profitable?
The American Transportation Research Institute reported a 6.6% increase in truck operating costs for 2023. This rise coincides with a significant financial burden: deadhead mileage reached an average of 16.3% for non-tank operations. Additionally, five in every 100 drivers quit. These combined pressures, coupled with low freight rates, are squeezing the entire trucking industry.
AI is helping trucking companies save money, improve efficiency, and boost driver satisfaction. The trick is knowing which tools you need and how to operate them.
Logistics leaders navigate these challenges step-by-step by identifying their biggest pain points, whether that’s inefficient route planning, reactive maintenance, data overload, or all of the above, and pairing each priority with a solution. Before taking any action, it’s crucial to look at the bigger picture and craft a data collection strategy to integrate changes into existing workflows smoothly. Check out this roadmap to get you started.
With the revolutionization of AI technology, businesses are also embracing AI-generated presentations to deliver clearer and more impactful insights. Trucking companies can similarly leverage these advanced solutions to present their strategic roadmaps dynamically. This method helps articulate complex logistics data visually, ensuring all stakeholders possess a unified understanding of operational objectives. Imagine aligning teams through captivating visuals created almost instantly, facilitating seamless communication at every stage of your AI integration journey, ultimately improving operational excellence as showcased in this roadmap.
Identify and prioritize the biggest pain points
First, freight carrier Yellow filed for bankruptcy in 2023, and now 30-year-old US Logistics Solutions Inc., a full-service logistics provider, announced in mid-2024 that it will no longer receive the funding necessary to continue operations. After decades in the industry, how did this happen?
The problem is that there is no one-size-fits-all solution. Logistics providers can each optimize operational efficiencies with big data and analytics, but their unique asset portfolios, trade lines, and customer profiles vary, meaning data parameters and algorithms need to follow suit.
A good place to start is to benchmark your performance. What is your cost per mile? Your on-time delivery rate? And how do these compare to the industry average? Analyze existing records like fuel consumption logs, maintenance reports, driver logs, and delivery times to get a quantitative perspective. But be sure to do your qualitative due diligence as well.
Keeping track of GPS data and gathering feedback from drivers on their concerns, such as inefficient routes or long wait times at loading docks, will provide your company with some direction. Engaging drivers and incorporating their ideas into your strategy boosts the likelihood of solution acceptance and helps to reduce driver turnover, too.
Next, consider which bottlenecks are causing the biggest delays, financial strain, and compliance violations. Are you meeting the latest standards? For Reliable Carriers, a big pain point was the electronic logging device (ELD) mandate, which caused the car hauling company to reexamine its operations’ technology and safety. Its digital team received regular calls from drivers needing assistance with the ELD system. Logistics leaders needed to drill down on what issues the drivers were specifically having, such as unfamiliar user interfaces, or adjustments to routines to find the right solution.
Once you’ve identified what needs improving, the tricky part is weighing up urgency versus difficulty. It’s helpful to prioritize some “easy wins” to boost team morale and motivate change. Then, you can work on addressing the more challenging pain points.
Match pain points to specific AI solutions
Every logistics company struggles to avoid transporting empty trucks or containers. One in three lorries on Britain’s roads is driving empty in an attempt to meet the demands of multiple clients with varying volume, location, and day-of-week requirements.
For efficient routing, reduced backtracking, and optimized miles, route planning software needs to incorporate additional factors on top of live traffic data, accidents, construction zones, and weather patterns. This includes:
- Historical delivery data (e.g., traffic patterns at specific times)
- Client volume fluctuations
- Driver availability and skills
This is where AI-powered load-matching can significantly improve route optimization strategies. It analyzes real-time available loads and trucking capacity data and matches deadhead stretches with suitable freight, minimizing empty miles. In simple terms, if you have an available truck in the area of an available order, you can use modern software that can demonstrate it on the map and your execution team needs just a few clicks to match those two and start generating revenue from that order.
If the challenge is less about empty trucks and more about having enough drivers to transport them, then analyzing driver performance data will be beneficial. High driver turnover leads to training costs, scheduling disruptions, and decreased productivity.
By evaluating fuel efficiency, safety records, and on-time deliveries, AI can identify areas for improvement and suggest which drivers can perform more efficient. Additionally, AI-powered driver optimization tools can improve route assignments and scheduling, ensuring there are no risks of violating hours of service (HOS) requirements, minimizing driver fatigue, and improving job satisfaction.
Craft a data collection strategy to fuel AI implementation
The trouble is that while trucking companies have a wealth of data to fuel the latest trucking software analytics, it is often scattered and challenging to organize.
What data sources do you have already? Think GPS information, onboard diagnostics and vital engine signs, fuel management systems, driver logs, and maintenance records. Determine where the data gaps are to meet your specific goals. For example, does your GPS system capture truck location, speed and stops? Or do you need another method of gauging idle time? In each scenario, what data are you capturing manually, and could these gaps be fixed with automation? Is it an easy fix or something that needs a whole set of explanations?
If your main priority is improving driver performance and retention, critical data points include driving activity, location, miles driven, ignition status, and engine hours. In this case, investing in in-cabin cameras and engine sensors will ensure non-biased data capture.
The next hurdle is structuring the various data points in a standardized manner for easy analysis. Good news is that there are trucking-specific platforms that can help integrate data into a single centralized dashboard.
After identifying the datasets you need, the gaps, and how you will reliably capture them, it’s time to put on your security hat. Transparent data policies, access controls, and responsibilities are essential to ensure data consistency, accuracy, and safety.
Navigate workflows to integrate AI smoothly
First and foremost, be open and transparent with your staff and drivers about the changes. Address any concerns about job security or increased workload. Highlighting the positive impact AI can have on efficiency and overall job satisfaction should help win their acceptance.
This transparency must be maintained throughout the integration process. Consider what communication channels you will use to keep your team informed about progress, answer questions, and address any challenges that arise. Perhaps you assign change management leaders or open an instant messaging platform with key decision-makers and users of the new systems.
With your communications in place, it’s time to launch your “easy win” pilot project. This might involve eliminating a lot of manual work, such as using generative AI to scan email requests and draft quotes, providing clients with instant responses that are aligned with market offerings at the time of request. Once comfortable with the adjustments made to your AI and workflows, gradually expand the use of AI across other departments, integrating it into existing operations step-by-step.
Every trucking company faces unusual challenges with different routes, client needs, and assets. It’s important to understand what really niggles at your unique workforce to ensure you prioritize the right strategies with a supportive team at your side. By following the roadmap, you can seize control of the chaos and streamline your operations, one step at a time.
Author of the Article: Asparuh Koev, CEO of Transmetrics
Asparuh Koev has worked in the transport and logistics sector for more than two decades. Over the years, he has established several companies including Sciant, an engineering services company later acquired by VMWare, and IntelliCo Solutions, which delivers IT digitization for the transport industry.
Koev co-founded Transmetrics in 2013 and, as CEO, he combines IT and domain expertise to grow a company that is bringing truly cutting-edge technologies to the sector.
