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5 Ideas How AI Helps in Fleet Cost Reduction. Creating Machine Learning-Based Fleet Management Software

Fleet management software is a critical component of fleet cost reduction. More than ever, companies are looking for ways to reduce operating costs and increase efficiency to stay competitive. AI-based fleet management solutions can help relieve pressure on company budgets by optimizing fleet scheduling, driver behavior, routing decisions, and more. This blog post will introduce you to 5 ideas on how artificial intelligence (AI) helps reduce your fleet costs.

The Actual Costs of Fleet Management

Fleet management is a complex task that requires time and effort. Fleet management aims to optimize the use of company resources, including vehicles, drivers, and fuel. There are many factors to consider when making decisions about fleet management, such as:

  • How many vehicles do we need?
  • When should vehicles be repaired, when should parts be changed?
  • When to replace vehicles for safety and highest resale value?
  • Which routes should be used for deliveries/pickups?
  • When and on which routes should the vehicles be used?
  • How to mitigate the risk of accidents?
  • How can driver behavior be optimized to save on fuel economy?
  • How to enhance operational visibility?

These are just some of the questions that need to be answered to run a cost-effective fleet operation. And it’s not just about reducing costs — there are also other important considerations such as environmental impact and customer service.

Challenges faced by fleet managers

Machine Learning for Fleet Management: AI Can Help You Increase Efficiency, Safety, and Reduce Costs

Fleet management is a process that involves the coordination and operation of vehicles and drivers for business purposes. Typical fleet operations costs include:

  • Fleet vehicles acquisition cost,
  • Maintenance and repair cost,
  • Fuel cost,
  • Insurance cost,
  • Driver salaries or wages.

The total cost of fleet management can be reduced by implementing strategies to optimize each of these categories. The expenses of the vehicle fleet are significant, and it is essential to find ways to reduce them as much as possible. More than ever, companies are looking for artificial intelligence solutions that can help optimize their fleets to stay competitive. AI-based fleet management can have significant payoffs by reducing operating costs and increasing efficiency at your company.

Reducing Overall Mileage — Optimized Routes and Improved Fuel Economy

Machine learning-based fleet management software can optimize routes to reduce mileage. By reducing the total number of miles driven, you can save on fuel costs, maintenance costs, and even driver overtime.

A recent study by McKinsey showed that optimized routing could reduce mileage by up to 20%. This would result in significant cost savings for companies with large fleets. This is the most effective way to reduce fleet costs. By optimizing your route planning through machine learning, artificial intelligence can account for variables and historical data like traffic congestion and optimal speed to reduce mileage without impacting customer service levels. AI-based fleet management solutions take into account real-time traffic data, telematics data, road closures, and other factors to create the most efficient routes possible. They also learn and adapt over time, always providing the best results possible. Mileage reductions suggested by AI systems translate into tangible savings on fuel and maintenance costs.

Related case study: Implementing AI model to optimize routes and timelines of deliveries

A company from the logistics sector approached us to create a custom AI model that optimizes routes and scheduling of deliveries.

Our challenge? The key challenge here was to prepare a dedicated AI-based system designed for carriers to optimize delivery time depending on the destination address. Thanks to the model we managed to reduce failed and late delivery rates by 30%. Reach out to our experts to get a detailed case study of this project.

Intelligent Freight Matching — Making Fleet More Efficient

Most fleet management software focuses on matching the right truck to the correct route. However, artificial intelligence can go even further by optimizing freight loads picked up or delivered, reducing fleet expenses and fuel efficiency.

By using machine learning-based solutions, AI provides you with a better understanding of what your current capacity looks like and how it changes over time (e.g., weekends vs. weekdays; times during the day when truckload carriers have more available space). This allows fleets to make smarter decisions about which jobs should be assigned to specific vehicles — maximizing vehicle utilization while minimizing empty miles driven.

This will help reduce overall costs significantly because you’re assigning load types to vehicles according to their capabilities and availability rather than just looking at proximity to fill up a truck.

Freight matching is essential for fleets that are delivering goods to retailers. Using machine learning can ensure that the correct freight is delivered to the right store at the right time. This will minimize back-hauls and wasted miles — resulting in significant cost savings.

Advanced systems also allow logistics companies to manage and coordinate thousands of vehicles simultaneously, which means they can better allocate resources, decrease fuel consumption, and improve vehicle utilization.

AI technology in transportation management systems uses machine learning models to predict customer demand to match it with available transport capacity and join some of the deliveries together. This also allows logistics companies to plan their deliveries smartly and accurately by considering deviations from historical trends or sensing traffic volumes.

Related case study: Delivering a dedicated IT system to manage freight and plan transportation

A major Polish logistics company approached us to create a dedicated IT system to handle their core business process — managing and selling freight and organizing transportation.

Our challenge? The tool allows for smart matching of carriers and freight, optimizing fleet management, and other logistics operations. The platform helps shipping agents minimize fuel consumption, maximize operational efficiency, and optimize fleet performance by matching multiple loadings on a similar route with a single carrier. Read more about this case study.

Predictive Maintenance — Reduced Maintenance Costs and Increased Vehicle Availability

One of the most significant expenses for transportation companies is fleet maintenance. By using AI, service managers can predict when a vehicle needs service or repair. This allows you to take proactive steps rather than reactive steps -preventing unplanned vehicle downtime and reducing maintenance expenses over time.

Predictive maintenance is done by analyzing data collected from sensors on vehicles and weather and traffic data. AI uses this data to identify patterns that indicate a problem with a vehicle before it becomes a significant issue. By using big data analytics, AI models can learn from millions of data points about a particular part, such as the age of its components or how many hours it runs per day on average — all while taking into account outside factors like weather conditions and road surfaces that might impact wear and tear more than usual.

Data from sensors on fleet vehicles and additional data costitute to predictive maintenance systems for logistics companies

Artificial intelligence technology has also been used to design artificial neural networks that can predict the lifespan of a given part without actually having to test it. This provides significant savings because repairs or replacements are often scheduled after vehicles have run for several thousand miles or in strict periods — which is well past the point where problems become apparent or, in some cases, earlier than potentially needed.

Once an issue has been identified, the AI system will generate a recommended course of action (e.g., replace the part, take the vehicle in for service, etc.). Predictive maintenance helps reduce downtime for vehicles, which leads to increased productivity and decreased costs associated with repairs.

Predictive and preventive maintenance of fleet vehicles and asset maintenance costs associated with passing time, usage and failure rates

Preventive fleet maintenance technology is beneficial for companies with mobile fleets operating in remote areas away from service stations or repair shops where they cannot be easily reached quickly if something goes wrong. Predictive maintenance allows you to avoid unpredictable costs associated with unexpected breakdowns.

Predictive Visibility and Vehicle Tracking Systems — Efficient Resource Planning With Actionable Insights

Predictive fleet visibility is a must-have artificial intelligence solution for fleet managers. Today’s vehicles are connected and equipped with a range of sensors, cameras, and GPS devices that transmit real-time data to the company network.

Fleet managers need to be able to see their entire fleet at all times to make strategic decisions about routing and load matching. This is where vehicle tracking systems with 360 visibility of telematics data come in. With this technology, operators can track the location of every fleet vehicle in real-time and have access to data such as speed, direction, and freights.

This wealth of information makes predictive analytics valuable in helping fleets reduce costs. Using AI solutions like predictive fleet management makes it possible to better plan resources and highlight any upcoming issues before they arise, such as higher demand for transportation services on a particular route, and plan freights accordingly. Predictive workforce planning makes it possible to earn more from the higher demand for transportation services.

Predictive analytics helps the fleet manager answer how many vehicles will be needed, when, and where. What artificial intelligence for fleet operations does is make it possible to reduce the fleet size that is required for a given company. You can do this by identifying which routes or vehicle types have lower customer demand and then adjusting your fleets accordingly, all while maintaining service levels.

This information is essential for optimizing routes and ensuring that vehicles are used efficiently. This helps in general fuel efficiency and helps to reduce fleet costs. In addition, it allows managers to identify potential problems before they become major issues and will enable them to take proactive actions.

With artificial intelligence playing an increasingly important role in fleet management, transportation and logistics companies can’t afford not to have a fleet management solution in place. By using AI-based fleet management systems, companies have 360 visibility of their entire fleet in real-time and predictive and prescriptive visibility on how the fleet is predicted to behave in the future.

From reactive and real-time visibility of the fleet, cargo details, schedules, etc., to predictive and prescriptive modeling for fleet and route optimization.

Invest in Intelligent Driver Safety Solutions — Reduced Insurance Charges and Safety Costs

Using artificial intelligence for fleet management, you can monitor driver behavior and reduce the number of accidents on company property.

Drivers are usually prone to heart conditions, high blood pressure, and other medical conditions that can cause them to lose control of their vehicles. To prevent accidents from happening due to driver behavior issues such as drowsiness or distraction, AI-based fleet management solutions are invaluable.

Machine learning solutions that track multiple life parameters help identify which drivers need further physician check-ups before they pose a danger to others on the road. Using solutions like vehicle tracking systems with behavioral analytics, you can monitor individual drivers on your network and identify those who might be at risk for causing an accident. By considering this data, fleet operators can direct drivers at risk for an immediate break, reducing the odds of incidents occurring.

AI is also used to help fleet managers understand how their vehicles are being driven. By using machine learning as part of your vehicle tracking system, you can gain greater visibility into individual driver specifics and identify risky driving practices such as fast acceleration or hard braking before they become problems for both drivers and the company’s bottom line. Identified drivers can be directed to additional training programs to challenge driver accountability.

Driver safety solutions improve safety and reduce insurance premiums for companies. By investing in artificial intelligence-based driver safety solutions, fleets can save money on their overall transportation costs.

Key Benefits of Applying AI to Fleet Management

Artificial intelligence brings multiple benefits to fleet management, including:

  • Reduction of fleet costs
  • More efficient delivery schedules
  • Optimized routes for increased efficiency
  • Allocate resources more efficiently and reduce fleet costs based on actual vehicle usage data
  • Better fuel efficiency and carbon dioxide emissions reductions through better mileage management practices
  • Reduced fleet maintenance and repair costs
  • Reduced insurance charges, premiums, and safety costs
  • Greater visibility into individual drivers and performance trends
  • Improve driver safety training programs,
  • Reduce accidents by identifying drivers at risk and problematic driving patterns

Each of these benefits can significantly impact a company’s bottom line, so it’s essential to carefully consider how artificial intelligence can be used to improve fleet management processes. By using machine learning solutions, fleet managers can save money while also ensuring the safety of their employees and reducing environmental impact. In today’s business world, that is something we should all be striving for.

The Future Work of Fleet Managers

Fleet managers have a lot of work on their plate, and the job will only get more complex in the years ahead. With artificial intelligence playing an increasingly important role in fleet management, those in charge of fleets will need to be prepared to use this technology and accelerate their digital transformation if they want to stay competitive.

Fortunately, AI-based solutions are becoming more and more user-friendly, so even those without a technical background can learn how to take advantage of them. In addition, there are plenty of resources available online and from third-party providers that can help fleet managers get up to speed on the latest AI advancements.

If you are looking for ways to reduce your fleet’s expenses, be sure to contact nexocode. We offer AI-based fleet management software that can help optimize routes, predict maintenance needs, and more. Our solutions are tailored to meet the specific needs of each client we work with — so you can rest assured that you’re getting the best possible service. Contact us today to learn more!

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Originally published at on January 10, 2022.



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