How Machine Learning Helps To Improve Fleet Operations

Machine Learning (ML) – a subset of Artificial Intelligence (AI)-transforms almost every business vertical. It helps accelerate the digital transformation while disrupting the conventional processes.

For the transportation and logistics industry, it has slowly started to gain traction with fleet management. AI helps them make managers’ role more productive and streamlined. It helps them address the business needs while supporting the new technology adoption.

The heavy use of smartphones and telematics devices helps companies to leverage powerful AI-based applications. Innovative solutions help prioritize driver safety without compromising on cost and efficiency. It enables them to keep track of all the fleet operations and make timely decisions. They offer route recommendations to road risk analysis and even provide coaching to drivers. The combination of GPS and artificial intelligence makes drivers’ lives easier.

Suppose you’re wondering how it will offer value to your business and enhance your operations and safety. In that case, this article is for you.

What Will We Cover;

• Critical aspects of fleet management that AI (ML) can optimize
• How ML helps to resolve major challenges of the fleet industry
• Benefits of adopting ML-driven Fleet Management
• How can ML Improve Fleet Safety?
• How is AI (ML) Integrated with Fleet Management?
• How ML-based Fleet Management Will Shape the Future of Transportation

The use of this modern technology helps streamline the work processes for any manager by gradually eliminating human error within the transport process. The recommendations help fleet drivers, service managers and mechanics to make improved business decisions.

Here are some key aspects to consider:

Real-Time Fleet Management Analytics

Insights-driven decision-making gathers data based on traffic, road condition information, real-time weather, environmental hazards for predictive analytics. It helps identify the most optimal route to their destination. This enables operations managers to choose better roads, create a proper route, schedule maintenance while improving the overall business activities and outcomes. Having a fleet management data analytics tool helps track vehicle status, maintenance history, and more.

Faster Repair & Maintenance Decisions

Utilizing advanced technologies help improve and offer accurate self-diagnostics & solutions to faults. The evolution of data analytics, internet of things (IoT) and predictive maintenance are revolutionizing vehicle repairs. It is about forecasting the potential defects before they even happen. This helps detect fault long before it eventually occurs. A vehicle with a diagnostics system can anticipate an engine problem and report it on time. It helps optimize the overall maintenance cost.

Integrated Operations

The large-scale operations involve monitoring of the moving parts within the system. The exchange of enormous information happens within several departments. And to sync this data within all other areas of operations is time and resource-intensive. An AI system can seamlessly integrate the data across various departmental operations. This saves time and costs on planning, maintenance & monitoring procedures since all data on those processes are entirely accessible.

Safety

AI-powered solutions help detect risky driving behavior such as driver fatigue, sleepiness, inattentiveness, rule violation, speeding and more. The driver’s face can be monitored in real-time with analytics identifying the state of the mind while driving. This provides managers an opportunity to take the driver’s physical condition into account while planning or to intervene in actions if a safety risk is flagged. It also enables them to assess the driver’s performance and conduct training programs.

Inefficient Fleet Management Process

Manual tracking processes, functional issues, inefficient operations, and fuel theft are prominent reasons that affect the transportation & logistics industry. The solutions help track these issues and data in one place. This helps make more informed, automatic, fast, optimized and reasonable decisions.

Machine Learning in fleet management uses predictive analysis to prevent potential accidents and alert at-risk drivers.

You can build a predictive model with a rich and complete set of historical data. It involves analysis of the behavior that resulted in accidents. Implementing the right machine learning technology ensures mitigation of risks, accident prevention and reduction in insurance claims. Understanding, choosing and then implementing the right solution is critical to a successful and accurate prediction. Combining a centralized data management software with dedicated tools enables fast information gathering, making predictions & managing possible exceptions.

The sheer volume and velocity of data from on-vehicle sensors and the wider IoT range demand the integration of much more intelligent management systems to help keep pace. The dedicated and integrated software is a unified system made up of several components and apps such as the Internet of Things (IoT), predictive data analysis, machine learning, HD cameras & sensors, Wi-Fi and more.

Let’s try to understand the individual role each of these components play as part of the holistic solution;

Machine Learning

This technology enables taskforces to learn from data collected over time and make managed improvements based on that data. It builds intelligent systems in which the system can learn & improve upon decision-making capabilities to handle practical situations more effectively.

IoT

It includes a network of actuators and sensors that collects data continuously. IoT app ensures that enough data is taken for analysis while promoting the seamless sharing of information between all stakeholders of the supply chain, including retailers and manufacturers.

The dedicated application for operations management uses three vital technologies:

• Wireless communication to convey relevant information
• GPS to track the real-time location
• Onboard Diagnostics scanners for self-diagnostics and reporting

HD Cameras

This device ensures that video data can be captured, analyzed and accessed at any time anywhere. It leads to a better analysis of driver behavior, road conditions or hazards.

An AI-enabled Fleet Management system can perform the following activities:

• Gather precise road data and transfer it to other devices
• Transfer data across every arm in the supply chain
• Analyze data in real-time and recommend driver on the best outcome
• Identify distracted or drowsy driving behaviors before they lead to traffic accidents and serious injury
• In case of accidents, capture the entire video footage from different angles

While the automotive vehicle industry faces challenges as listed below that affects operations and business profitability, AI can potentially address these problems to create a better future for transportation.

• Resource efficiency and arranging
• Dangerous driving behaviors that lead to accidents
• Road risks
• Data collection and analysis
• Cost containment
• Compliance risk

AI and ML can streamline manual processes, deliver valuable insights and help businesses of all sizes become more productive with;

Valuable Insights Available at Fingertips

The high volume of data collected from onboard sensors, satellite tracking, and IoT devices may confuse the operators of larger fleets to find the most relevant data. With ML-enabled telematics systems, they can get the appropriate input and even guide their employees in real-time.

Automated Variance Detection

ML-enabled systems that feature advanced dashboards can offer visual displays of the data collected, allowing fleet managers to identify anomalies and drill into data easily. It would include exit misses, yawning habits, count blink frequencies and various signs of uncertain behavior. The inclusion of more parameters into this dataset would explain the real scenario and why the changes are occurring. These signals then reach the business managers in real-time, enabling them to take corrective measures at the right time.

Improved Maintenance Decisions

AI-based predictive technology can also learn to make intelligent predictions about the weather changes and detect changes in the environment. It would include fog, rain, cyclone before the driver reaches that destination. AI in Fleet Management can also help managers save costs through fuel economy and predictive maintenance.

Apart from this, we provide a range of services to deliver a comprehensive package of solutions that comprise:

• KPIs and Data Security Management Module
• Insurance & Compliance Management System
• Third-party API Integration
• Fleet Tracking Mobile App Development & Support
• Testing & QA and More

Fleet management is a vital aspect of running a successful business. The exciting nature of AI applications in operations management will make the future of the transportation industry more promising than ever. It would most certainly help ensure that tackling natural situations like unpredictable road conditions with related business scenarios like driver retention challenges doesn’t affect/increase the operational costs doesn’t affect the organization’s growth. The right solution with advanced technology can easily manage assets and add substantial profits to the organization.

Rishabh Software provides custom Machine Learning-based fleet management systems to enable businesses to increase profits and achieve high ROI on investments.