Fleet Management with Machine Learning

The word Fleet in simple terms can be understood as “a group of vehicles”. Fleet management is a system designed for increasing productivity and efficiency, and reduce the cost of operations for companies dealing with fleet. Companies that deal with the transportation business are familiar with fleet management and the challenges it brings as they often struggle to effectively manage their fleets. Companies want to put management mechanism in place, which ensure they prevent costly breakdown that results in delays in services and cause financial losses. IoT along with machine learning provides that smart solution which help them to maintain fleets better that help in increasing productivity and efficiency of fleet companies. Following steps show how a fleet management IoT solution integrates machine learning with sensor-driven automation into the entire supply chain. This includes collecting telematics information and sensor data, as well as device and application management capabilities:-

Establish KPIs & Manage Data Security: - IoT includes the devices that communicate with each other through Internet. Fleet management with IoT systems introduce an entirely new framework which enables Fleet Managers to collect, analyse and share greater amount of data at a faster speed. It is important to firstly identify which data sources are valuable based on business objectives and KPIs.For example certain data never leaves vehicle or directly get stored in cloud in silos which is limiting the insights gathered from that data. Securely accessing and integrating critical data is very important so Fleet Management need to have some protocols with the data, for example as if data being gathered is sensitive then evaluate the risks associated with the device being hacked and the data being exposed.

Data Driven Decision making: - A fleet management IoT solution enables proactive monitoring of various sensors and the corresponding analysis of the collected data. Turning vast amount of data into actionable business insights is one of the largest challenges. However, using advance machine learning techniques we can overcome these challenges and help prevent potential issues from impacting service. With Pre designed algorithm based on fleet best practices help rapidly identify opportunities in fleet operations. Through real time visualization fleet managers can leverage these insights to efficiently organize and day to day operations of their fleet services, for example they can use the most optimized routes using route optimization algorithm which helps in reducing fuel costs and vehicle idle time.

Reactive to proactive with automation: - As IoT is new source of observational data for Machine Learning so Fleet management with IoT also helps organizations to move from reactive to proactive decision making. One example is predictive maintenance solution driven by Machine Learning which can help to prevent losses due to unplanned maintenance as with integrated Fleet management solution it predicts and alert around the specific maintenance well in advance. It highlights previously hidden equipment issues that can lead to more serious problems. Identifying these issues early on helps to improve the quality, availability and reliability of the fleets.

Figure 1 Benefits of adopting ML driven Fleet Management 

IoT is set to become the backbone of the fleet management industry but at the same time there is explosion of big data so to utilize the full potential of IoT it is important to integrate Machine Learning in the Fleet Management system, It is something which is getting used with fleet companies who were early adopters of telematics, and with significant impact on the business more and more fleets are seeing the value in it. With ML driven automated fleet management fleet companies can proactively take action and make data driven decisions that positively impact their organizations by increasing efficiency, effectiveness and safety.