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6 ways AI drives Efficiency and Savings for Fleet Operations

6 ways AI drives Efficiency and Savings for Fleet Operations

When discussing AI within the transportation industry, the common association is with self-driving vehicles. As companies like Volvo and Knight Swift venture into autonomous truck technology, investments in AI solutions for this sector continue to rise. 

So, what are the major hurdles faced by the transportation industry, and how can AI contribute to overcoming them? 

  1. Process Optimization: Often underestimated, process optimization is a critical issue. Many companies rely on a blend of manual and digital systems, resulting in poor time management and operational inefficiency. Customizable AI tools can streamline and automate processes, reducing the need for manual intervention.
  2. Remote Fleet Monitoring: Managing large or widely dispersed fleets can be challenging. Many companies with extensive fleets lack the workforce to monitor them effectively. Precisely monitoring locations and sensory data from trucks located far from the central workshop is complicated. AI tools not only assist in monitoring but also identify errors and aggregate sensory data from the entire fleet while they are on the move.
  3. Fleet Maintenance: Keeping track of the operational history of a fleet can be a daunting task. While predictive maintenance is the goal, manual processes for tracking maintenance history make it difficult to monitor metrics such as fuel consumption and failing parts. The right AI tool can identify failure patterns and track part usage frequency across the fleet, enabling faster decision-making and real-time fleet status tracking.
  4. Fleet Scheduling: Minimizing empty truck miles is a common goal for all companies. Inefficient routing plans often lead to losses, especially as fleets grow. Human schedulers struggle to manage fleet movement and predict demand accurately. AI tools help predict supply and demand by considering factors like seasonal fluctuations, fuel prices, and more.
  5. Route Optimization: Planning an accurate route based on demand, time constraints, and destinations is challenging. The Vehicle Routing Problem is notorious in the transportation industry, resulting in losses due to inefficient routing. Machine learning algorithms can plan fleet routes based on real-time data, such as traffic patterns, weather conditions, and traffic reports, and even provide alerts to drivers for route modifications.
  6. Demand Forecasting: Accurately predicting changes in demand, especially with the growth of e-commerce and delivery services, can be tricky. AI tools incorporate historical data, weather forecasts, and even public holidays to estimate demand fluctuations over time.

As more companies turn to digital solutions and AI tools to optimize their operations, it’s essential to note that there is no one-size-fits-all solution. Choosing the right tool that addresses at least 80% of your challenges is crucial. 

Adopting a tailored solution like iMarq can significantly enhance efficiency. iMarq is a cloud-mobile platform designed to digitize maintenance operations. iMarq can integrate with sensors deployed in the fleet to digitize maintenance, parts requests, and more. At Connixt, continuous improvement is deeply ingrained in our culture. With this in focus, we are launching iBot, an AI-powered assistant that seamlessly integrates with iMarq. Sign up here to be among the first to learn about iBot and its capabilities.  

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