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IoT Connectivity & the Significance of Fleet Management in Logistics Industry

13:03 22 July in Blog

This is the era of IoT (the Internet of Things). One where every sector around us, right from transport, retail and manufacturing to automobiles, is undergoing a major transformation. Fleet management is the latest to join this revolution.

Today, the science and study of fleet management is transforming the way traditional fleets were managed. The confluence of connected gadgets, networks, and its consequent by-product of big data is enhancing the impact of fleet management.

With smarter vehicles and innovative technologies that connect vehicles and drivers to the enterprise system in real time, not only the operators but construction, automobile, cargo companies and several other industries that employ drivers and delivery personnel, are beginning to see the many advantages that optimal fleet management solutions can provide in their asset utilization.

While the fundamental prerequisite for effective fleet management is the availability of fleet, it includes a range of functions such as fleet tracking, fleet maintenance, fleet financing and health and safety management. It all boils down to having the right technology tools to manage fleet, which can provide a huge competitive advantage for businesses belonging to any industry.

The Evolution of Fleet Management

Today, companies that rely on fleet are starting to collect and analyze data from on-board systems. That’s because fleets nowadays have a range of vehicle monitoring sensors embedded in them, which enables us to track the vehicle condition, GPS location and more. This data is gathered by the Internet of Things (IoT) devices. These devices can transmit data on fleet conditions in a wireless manner to fleet management centers, which then collect and process the data.

Firms that have fleet management centers can view the data gathered from the entire fleet on dashboards and process it in real-time using analytic tools to unlock the hidden patterns on vehicles and fleet. Artificial intelligence and machine learning algorithms dissect, examine and understand the collected data to predict the optimal fleet paths and suggest optimal vehicle conditions to run to improve the operational efficiency. The power to optimize this gathered data lies in working with Machine Learning models that can replicate real-time scenarios.

The Benefits of Fleet Management Optimization

  • By using fleet management optimization techniques, logistics companies can identify time-consuming activities and work on improving operational KPIs.
  • It also allows flexible route planning, which can reduce operational costs, increase productivity, ROI and reduce environmental impact.
  • Fleet managers can track and collect data via IOT devices on vehicle condition. This helps them to eliminate redundancies and prevent problems before they arise.
  • Fleet managers can schedule proactive maintenance check-ups by coordinating with suppliers and purchasing personnel to keep the fleet in top shape.

Fleet Management Optimization Done Right at BizAcuity

At BizAcuity, we enjoy expertise in solving complex analytical problems using AI/ML models, analytics and other sophisticated technological approaches. By working with people transport companies, we have gathered insights and developed proficiency in devising efficient solutions using advanced optimization techniques. Here’s one such case study.

We were assigned with the task to find out how many dedicated cabs – working 12 or 24-hour shifts – could be deployed in a specific city. Additionally, we needed to plot the number of billable vs dead km for each of these dedicated cabs. The current scenario is that each cab makes one trip in the morning and in the evening, and we had to optimize this.

While doing the data analysis to solve this problem, we stumbled upon three major areas of optimization: in-travel hours, connecting hours and dead hours. We then plotted that data during the peak hours in the mornings and evenings and used that to train our model. But, since the number of API calls would have gone up exponentially if we would have used any of the pre-existing models, we developed one in-house taking these limitations into consideration. The model was further developed and trained by feeding it vehicles’ features and trips’ data.

We then integrated the model to handle the vehicle requirement for each trip based on the respective seater capacity at the start point of the trip. If the requirement of higher seater capacity was not met, the model was trained to look for lower-seater capacity vehicles and arrange the trip accordingly. Additionally, to improve efficiency, the model was trained to pick up the closest vehicles available at the start of every trip.

In order to minimize the dead hours for a vehicle and optimize the fleet capacity utilization, we developed the model to consider the number of hours of a vehicle being idle after its last trip. The vehicles with more idle hours were prioritized over the other vehicles nearby. While taking into consideration the future requirement of vehicles, we’ve trained the model to analyze the number of trips and the distance traveled by each vehicle before assigning the trip to a particular vehicle. Besides, to ensure all this was effective, we used the real-time distances between the locations using Distance Matrix API, to create real-time scenarios. This way, we ensured that the entire fleet was being utilized appropriately.

The initial model, when deployed, showed great results. When based on a 12-hour shift, the top 5 vehicles were allotted up to 100 km of travel and the number of vehicles travelling over 100 km per day rose to over 38. The model also determined that 920 vehicles were needed to be assigned in a specific city.

After further refinements and enhancements, our model showed even better results:

  • Top 5 vehicle’s total distance (trip distance + connecting distance) traveled per day was approximately 140 km
  • Vehicles travelling over 100 km per day increased to 90+
  • The total number of vehicles assigned to the one specific city unit decreased to 770 vehicles from 920 earlier.

The way ahead

While the evolution of technology has played a key role in fueling its growth and making it more efficient, the need for smart fleet management today has become vital. This evolution of intelligent fleet systems will help save fuel, increase efficiency, reduce traffic fatalities, ensure smooth operations and an overall healthier organization.

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