What You Need To Know About AI & Machine Learning In Fleet Management
Artificial intelligence (AI) and machine learning (ML) have revolutionised fleet operations in various fleet industries. Trying to manually manage an entire fleet (whether established or startup) can be catastrophic. Fortunately, this technology can ease the process. Here’s a look at some of the innovative ways to use AI and ML in fleet management.
AI and ML: the two peas in a pod
“Can machines think?” – Alan Turing
This is the crucial question that stirred the artificial intelligence conversation in the 1950s. Over the years, artificial intelligence (or AI for short) has been defined in a number of ways. However, Turing summed it perfectly as “systems that act like humans”. Although this simplification is still applicable today, AI is not that simple.
Artificial intelligence is an entire field that integrates computer science with robust datasets to solve problems and bring about innovation. AI subfields include machine learning (ML), deep learning, robotics and more. Together with its subfields, AI can be defined as technology that enables machines (especially computer systems) to think or act in a more ‘human’ way by gathering information from their surroundings and determining responses based on what is learned.
Commonly, artificial intelligence (AI) and machine learning (ML) are used interchangeably. However, the two are slightly different. ML emerged in the 1960s when scientists researched teaching computers how to imitate human decision-making. ML is used to teach machines to mimic how humans behave using data and algorithms so they can gradually improve their performance and accuracy.
Therefore, while AI is the tech enabling machines to display human-like behaviour, ML teaches AI how to behave. By leveraging the power of AI and ML, machines can now be trained to accurately perform complex tasks that were once deemed impossible for non-humans to accomplish.
Now that we understand AI, ML and their differences, why should we care?
Why do AI and ML matter?
From using facial recognition to unlock our smartphones to asking Siri, Alexa, and Google Assistant to play our favourite jam – AI and ML have become a part of our everyday lives. AI matters in today’s world for many reasons:
Repetitive and manual tasks can be automated
AI and ML automate repetitive manual tasks like business administration (filing, data entry, and scheduling meetings). This increases efficiency and productivity while saving time and money spent on human resources. So get ready to be replaced by a robot… just kidding! Humans are still essential to set up and operate the system, ask the right questions, confirm the accuracy of data captured, and a few other skills.
Products are more ‘intelligent’
By adding intelligence such as speech and voice recognition, and virtual assistance to existing products, AI and ML have opened up a world of advanced products and innovations. These products have upgraded the business world – from biometric systems for tighter security, to data and behaviour analysis to detect and prevent fraud.
Customer service is instant and better
Thanks to AI and ML, chatbots can quickly process requests and accurately answer questions 24/7, enabling customers to get the help they need quickly. This automation of customer service saves time on tasks, reduces costs, and frees up staff to focus on more complex tasks.
Data is made useful
Data in business is important now more than ever as it can offer a competitive advantage. Luckily, AI and ML have the ability to process large amounts of data. This data can be used to make accurate and informed business decisions.
Customer journeys are tailor-made
AI and ML provide the ability to analyse customers’ purchase history, buying habits, search history, and other data. With this, businesses can personalise products and services to best suit their existing customers and attract potential customers. A personalised customer journey not only increases customer satisfaction and brand loyalty but also increases sales.
You may already be getting ideas of how these two peas in a pod can innovate your fleet. Let’s start with fleet management basics.
What you need to know about fleet management
Fleet management refers to all the ins and outs involved in ensuring a fleet operates its tasks smoothly, from start to finish. These ins and outs include (but are not limited to) the following tasks:
In essence, the role of a fleet manager is to ensure that these tasks are completed efficiently at top productivity levels and lowest costs while producing the highest profits. It’s a lot to handle and to top it off, fleet managers still face many other challenges:
Keeping track of vehicle maintenance: To operate, fleet businesses need their fleets to be in good condition. Regularly maintaining vehicles ensures that they remain in their most efficient state and increases their longevity. However, it is difficult for fleet managers to keep track of every single vehicle’s maintenance schedule. Not staying on top of vehicle maintenance needs can lead to productivity loss due to unexpected downtime from breakdowns, and unnecessary vehicle wear and tear.
Managing fleet data: Operating a fleet business (whether established or startup) comes with piles of data to collect, track, and organise. Manually sorting, assessing and managing this data is nearly impossible. Fleet businesses cannot make informed business decisions without well-organised data to gain accurate insights into fleet operations.
Simultaneously managing teams: From dispatchers to drivers, fleet managers need to oversee various departments simultaneously, and it is impossible to be everywhere at once. This makes it difficult to track productivity, monitor driver behaviour, communicate effectively, and solve problems swiftly.
Organising fleet administration: Fleet managers are responsible for performing administrative tasks such as task management, team management, fleet reporting and analysing, as well as scheduling and optimisation. With only 24 hours in a day, fleet managers struggle to perform these tasks and as a result, the fleet may operate efficiently.
Manually performing fleet management tasks and trying to overcome challenges can be catastrophic but thanks to AI and ML, fleet management problems are solved. Let’s find out how.
How AI and ML are used in fleet management
AI and ML have opened up new possibilities in fleet management software development.This is computer software that helps fleet managers successfully oversee an entire fleet all-in-one platform. Let’s get a glimpse of how different fleet industries use AI and ML-powered fleet management software in their fleet:
Rental and leasing
The use of AI and ML in the car rental and leasing industry is growing rapidly to enhance customer experience and improve business performance. Some ways AI and ML are used in this industry include:
Customer service: Instead of physically visiting car rental and leasing companies, AI-powered chatbots and virtual assistants can help customers book rentals in advance, answer frequently asked questions, and provide personalised packages based on customers’ preferences, and rental and leasing history.
Predictive maintenance: Car rental and leasing companies can perform preventative maintenance to ensure vehicles are in the best condition for rental and lease. This preventative maintenance is possible with AI and ML algorithms that analyse data such as vehicle mileage and date of last service to predict when a vehicle may need maintenance. This way, rental and leasing companies can ensure they reduce breakdowns when vehicles are rented or leased — increasing vehicle reliability, customer loyalty, and company trust.
Competitive pricing: By using AI and ML algorithms, rental and leasing companies can offer competitive prices by relying on data (such as vehicle preferences, demand, as well as rental and lease habits) to make strategic prices, and to detect and flag fraudulent behaviour, such as stolen credit cards or fake identifications, so that losses are kept low, and prices remain competitive.
E-hailing and ride-sharing
E-hailing services such as Uber, Lyft, Bolt, and other platforms require AI and ML as crucial components to provide ride-sharing services. Here are some ways AI and ML are used in e-hailing:
Connecting riders and drivers: Suppose you are rushing to an important meeting or simply want the most comfortable way to move from point A to B, e-hailing services use AI and ML to provide the most efficient services. AI and ML in e-hailing match riders with the most convenient drivers based on factors such as location, availability, and other rider preferences (such as vehicle type, preferred gender of drivers, etc).
Optimising routes: Once a driver is matched with a rider on an e-hailing platform, the goal is to reach the desired destination in the safest and fastest way possible. AI and ML algorithms optimise routes for drivers to reach a destination in the shortest possible time (while taking into account traffic congestion, accidents, and other factors). This is a win-win as the riders pay affordable prices (calculated by the algorithm at the end of the trip) while drivers can use their time more productively, save on fuel costs and maximise profits.
Safety enhancement: Safety plays a crucial role in e-hailing services as it is important to protect passengers and drivers, and hold a trustworthy reputation. ML algorithms can help e-hailing businesses put safety first by collecting data such as driver behaviour, driver personal information, and driver ratings to identify potential safety hazards and take the necessary precautions.
Construction companies are becoming increasingly dependent on AI and ML to improve safety, increase efficiency, and reduce costs. Here are some ways AI and ML are used in construction:
Safety enhancements: Given the nature of the construction industry and its high potential for safety hazards that lead to staff accidents, injuries, and fatalities, construction companies need to follow strict safety protocols. AI and ML (such as AI-powered cameras) can detect and alert workers of potential safety hazards such as falling objects, unsafe working conditions, and people not wearing protective gear. This helps to reduce the number of worker accidents, injuries, and fatalities that can occur on construction sites.
Resource optimisation: AI and ML systems can maximise resource utilisation by scheduling machinery, vehicles, and manpower. Large construction companies that work on large projects, multiple projects or rent their assets to other companies use AI and ML systems to avoid project schedule overlaps and ensure assets are used efficiently and productively.
Quality control: Construction companies must ensure that all projects are completed to the highest standards. ML and AI can be used to monitor construction processes from start to finish. Data from sensors (e.g. temperature sensors to monitor building site temperature, humidity, and vibration) is analysed by AI and ML algorithms to detect even the slightest deviations quickly and accurately and predict potential issues before they arise, enabling better quality control.
Transportation and logistics
Due to the rise of e-commerce and quick turnaround expectations, goods are now not moving from point A to B in the supply chain, but now to your home, office, and everywhere in between.
Here are some ways AI and ML are used in transportation and logistics:
Optimising routes: Due to high demands, fast turnaround expectations, and transportation of sensitive products, the quickest and safest routes are necessary. AI and ML systems are used to analyse traffic patterns, weather conditions, and other data to optimise routes to avoid potential delays, reducing delivery times and minimising fuel consumption.
Tracking assets: Asset tracking is a crucial aspect of transportation and logistics as it ensures goods are safe from dispatch to delivery. In this case, AI and ML can be used to increase visibility to prevent cargo loss and damage. For example, AI and ML technologies such as GPS can be used to track the location and condition of vehicles and cargo, and AI and ML systems can track drivers’ performance in real-time, analysing data such as driving speed, braking patterns, and other behaviours— ensuring safety at every point of the supply chain.
Enhancing security: Cargo theft in the supply chain can cause major losses. AI and ML-powered security such as cameras installed in warehouses and vehicles to detect suspicious activity, and driver identification tags to prevent the unauthorised use of vehicles or access to warehouses can help prevent theft and loss by providing real-time alerts and notifications.
AI and ML innovations for your fleet
Cartrack is a prominent global Software-as-a-Service (SaaS) platform supplier for small, medium, and large organisations (as well as consumers). With Cartrack, you can transform your fleet operations and achieve operational excellence with the power of AI and ML fleet management solutions such as:
AI-powered cameras and live-stream technology Empower your fleet with LiveVision that has up to eight internal or external cameras to help you keep your drivers, fleet, and cargo safe. With AI-powered cameras, you can live-stream your entire fleet while cameras detect possible front collisions, smoking, distracted or fatigued driving, and mobile phone use. This, together with ML-powered alerts allows drivers to quickly correct behaviour right in the moment and ensure safety on the roads.
Fuel monitoring software Improve fuel consumption and save fuel costs with our fuel monitoring software, MiFleet that captures fuel cost data. This shows you how much fuel your vehicle uses for idle time and aggressive driving. Furthermore, it detects fuel card fraud, fuel theft, and fuel wastage, so that you can save money on fuel expenses, and only pay for what you use.
GPS vehicle and asset tracking Accurately track and get full visibility of your entire fleet in real time with GPS technology. This gathers data on where your vehicles and assets are so you can track completion rates, productivity, and efficiency, and receive informative insights (such as idling status, fuel levels, and engine diagnostics) to make informed business decisions.
Specialised sensors Our specialised sensors provide fleet managers with detailed insights into fleet operations.Cargo door monitoring systems and temperature sensors can be used in cold chain logistics to provide real-time temperature and cargo door monitoring and send fleet managers alerts the moment any temperature fluctuations occur or the cargo door is opened, allowing them to immediately check in with drivers to resolve issues or send a replacement trailer if necessary to preserve goods and prevent profit losses.
Automated preventative maintenance Our platform automates the maintenance schedules for each of your vehicles by using the vehicles’ utilisation hours and the time of the last service to establish effective maintenance plans hassle-free. This way you can cut your maintenance costs, increase your fleet vehicle lifespan and reduce unnecessary downtime.
Empower your fleet with AI and ML solutions today
Give your fleet the innovative boost and competitive advantage it needs with Cartrack’s AI and ML-powered fleet management platform. Get a free demo today.