Minimizing Fuel Costs With Custom Fleet Fuel Management System and ML

Fuel costs are a significant expense for logistics and transportation companies, making it essential to find ways to minimize these costs as much as possible. One effective strategy for achieving this goal is the use of a fleet fuel management system, which helps to track and optimize fuel consumption for a fleet of vehicles. By using real-time data analysis and machine learning algorithms, these systems can help companies make informed decisions about routing, scheduling, and other factors that can impact fuel consumption.

In this article, we will discuss the benefits of using a fleet fuel management system and how machine learning can help optimize fuel costs for logistics and transportation companies.

What Is a Fleet Fuel Management System?

The fleet fuel management system facilitates tracking fuel consumption and related costs. Aside from monitoring fuel usage, it can also point out ways to reduce it, making fleets activity more cost-effective, but also environmentally friendly. Based on algorithmic estimations, such a system is capable of issuing quite precise recommendations that will help you maintain the same level of productivity while wasting less gas.

By improving the visibility of fuel usage, fleet fuel management software helps you plan your expenses better. Planning fuel stops also becomes much easier, as you can automate them based on the available data regarding routes and vehicle-specific fuel consumption.

Key Features and Capabilities of Fuel Management Tools

Fuel fleet management systems may differ in terms of features and predictive capabilities. The most advanced ones heavily rely on artificial intelligence, which takes advantage of the data available for the purpose of performing calculations and identifying cost-saving opportunities. In order to be capable of that, they need to be integrated with various data sources inside and outside of the vehicles.

Equipped with sensors, vehicles can transfer crucial information in real-time. The system also collects route-related data relevant in terms of fuel consumption to estimate the usage and suggest alternatives if there’s such a necessity.

How Machine Learning Can Help Optimize Fuel Consumption

Fuel consumption depends on various factors, often associated with each other in an unexpected manner. While in sea transport, the equation is simple, coming down to cruising speed and ship size, things get much more complicated in sea transport. In a nutshell, we can divide relevant variables into:

• vehicle-related: such as tire pressure, engine performance, current fuel levels, and load weight.
• fuel-related: most of all, the type of fuel and its quality.
• route-related: the topography of the route (flat/hilly, smooth/bumpy, etc.), the speed limits, the quality of the road, and the frequency of traffic lights.

Machine learning enables fleet managers to predict how these variables will impact their fuel economy and environmental imprint by combining them all in the estimations. When carrying out such calculations manually, it would be challenging to come up with reliable insights, particularly considering that a big part of relevant data is collected in real-time.

Also, the more you use the machine learning models, the better they get, which is not always the case with human, error-prone expertise. We can, of course, identify the correlations between fuel use, fuel costs, and particular variables, but that takes incomparably more time and energy. Fleet owners can use machine learning algorithms in their fuel management systems for predictive purposes as well as to automate processes that contribute to effective fleet management.

Route Optimization and Real-Time Data Analysis

Routes are one of the most significant factors when it comes to fuel economy. Most TMS systems focus on finding the fastest one, putting customer satisfaction first. Using fuel fleet management software, you can keep this focus without compromising cost-effectiveness.

In order to achieve that, your route planning algorithm has to take real-time data into account, such as traffic density, accidents or other traffic incidents, and weather conditions. That, however, is not enough to optimize fuel consumption, at least when it comes to land transport.

Optimization models for overland fleets also should also involve topography details in their estimations, particularly if your services cover mountainous regions, rural places with no access to highways, or highly urbanized areas. These aspects are often not taken into account by basic route planning algorithms, while they make a huge difference in terms of fuel usage.

Fuel Stop Planner

In logistics, an efficient refueling plan is just as essential as route mapping. When planning an optimized route, the algorithm should involve not only the loading and unloading spots or delivery destination, but also the fuel stops. In order to deliver a fully optimized fuelling plan, the model should have access to real-time data. This way, you can update it dynamically as the vehicle travels along the route.

Aside from route-related variables and data gathered from the vehicle in real-time through sensors (with the fuel levels as the main one), it can take into account the fuel prices at particular gas stations or fuel cards. Such a function requires the integration of priced-related data sources with your internal system. We have described the development of such a solution in one of our previous articles on fleet management.

Load-To-Carrier Matching

Machine learning can automatize the process of linking the carriers to the load, finding the most optimized combinations based on the locations of particular vehicles and their capacity. Having access to the GPS data acquired in real-time, as well as the information regarding the vehicle’s route, load/offload stops, driver’s availability and resting hours, etc., the algorithm is capable of finding the best match based on the load’s specifics and delivery requirements.

Shipments Consolidation

The issue of empty miles is well familiar to sea and land transportation companies. You can look at it from various angles; first - it generates financial loss as the carriers do not use their vehicle’s total capacity. Second - it increases their environmental imprint for nothing. At the same time, it also affects delivery time as many vehicles are circulating half-empty.

The shipment consolidation solves that issue, allowing the carriers to find the most efficient combinations that will let them fully take advantage of their fleet’s capacity. In order to find them, the artificial intelligence algorithm needs to combine shipment-related data (size, shape, format, weight, transport requirements) and vehicle/container capacity data.

Predictive Maintenance Solutions

Any technical disruption in the logistics chain equals fuel waste. Vehicles that can no longer continue their ride have to be replaced, and other vehicles have to take over their load, which may involve detouring from their optimal path. Meanwhile, all the route they already have passed goes for nothing, and with it - the liters of burnt fuel. Machine learning can easily solve this problem.

Although you cannot predict unexpected events such as accidents or blockages, you can estimate when a particular element of the vehicle will come to the end of its useful life or when the inspection will be necessary. Proactive vehicle maintenance algorithms are there to help you improve fuel economy, improve safety, and lower maintenance problems, by issuing reminders before the problem affects the effectiveness of your fleet or causes downtimes and adjusting fleet management to predicted disruptions.

Driver Behavior Analysis to Reduce Fuel Usage

When it comes to fuel usage, there are various factors that the driver has no control over, from the traffic density to the quality of the vehicle. However, their behavior also impacts fuel consumption. Among driving behavior that can make your fuel spend skyrocket, we can point out the following:

• accelerating too quickly (as engines are designed for very specific speed limits, accelerating leads to wasted fuel)
• stopping too suddenly
• other sudden moves
• driving at a too-low gear
• long time idling, keeping the engine on long stops (more than 60 seconds), for instance, in the traffic jams

Having analyzed the fuel consumption data and data on driver behavior collected by the sensors in the vehicle (in the engine, speedometer, etc.), an artificial intelligence-powered system may identify inefficient driving habits and issue recommendations of good practices for a particular driver that will contribute to fuel-usage reduction.

Identifying Fuel Fraud

Fuel fraud is another common problem in the logistics industry. Fuel theft has a few possible scenarios here. Employees may:

1. fill the tank of their personal car with the fuel bought with the company’s money
2. resell the fuel bought at the company’s costs
3. use a tanked company’s vehicle for personal purposes.

The bigger the fleet is and the more people the company hires, the more difficult fuel theft becomes to detect. High rotation in logistic firms adds another layer to the fuel monitoring issue, making it harder to control the habits of the employees. A trained algorithm in fuel reporting software can help you find atypical fueling patterns that could indicate fuel theft, such as tanking more often than the other drivers with similar vehicles or stopping for fuel outside the usual route.

Benefits of Introducing Fuel Management Software

An effective fuel management software can help you identify cost-cutting opportunities and increase operational efficiency while:

• making your fleet management more environmentally friendly
• reducing the carbon print of your fleet
• helping you adjust to increasingly demanding requirements regarding the CO2 emissions
• prevent the increase of your business costs despite the rising costs of fuel and taxation
• purchase fuel based on gas station locations and prices
• improve fuel efficiency of your routes
• deliver the shipments even faster
• reduce the vehicle damage and probability of downtimes (recommendation for drivers and predictive maintenance)
• produce detailed reports fur further analysis and greater control

Most of that benefits equal cost savings, so, in the end, it all comes down to a crucial advantage - financial safety, which is so vital in the current unstable economic landscape.

Custom vs. Off-The-Shelf Fuel Management Systems

When choosing the right fleet fuel management system for your business, you may find yourself facing the dilemma: should I cut corners and pick an off-the-shelf solution or go for the custom one? In the end, it is all a matter of priorities, but if you want advice - we will always advocate for custom. Having worked with various logistic companies and fleet managers, we could see with our own eyes how a huge impact on the design of the systems the little differences in their processes have. It could be the type of load they tend to work with, the covered area, the specifics of the vehicles they use - anything.

Thus, we will never agree that a universal solution doesn’t have to mean compromises, particularly in logistics. Your ML models will always provide you with more accurate insights and recommendations if they are designed specifically for your process. We can help you with that, having extensive expertise in machine learning-based logistics systems. Drop us a line if you have an idea or project to work on!