If you ask a logistics stakeholder about their intention to master advanced technologies, you’re likely to receive positive reactions, but mostly only when it comes to troubleshooting. However, for now, the logistics industry lacks a comprehensive vision of both the technology landscape and the evolving potential for the business management environment. This creates doubts and naturally interrupts the industry's evolution.
Advanced fleet management is frequently touted as a range of disparate technologies intended to address a specific issue, while the major challenge of logistics is to instantaneously handle multiple unforeseen issues. AI, cloud computing, and 5G can produce the required result only within a competently balanced integrated solution – under the heading of IoT.
Why Is IoT a keystone for a fleet management?
IoT transfers physical objects into digital reality, thus expanding areas of human-free management. By connecting multiple sensors to assets, such as trucks or ships, IoT creates the basis of the value for any modern technology – the data. Having data at the core means other tasks can be strung to the solution – how to connect assets, where to transfer data, and where to store it. About 30% of a company’s overhead costs are due to incompetent data management resulting in low quality and insufficient data, as well as data loss. Such a case can totally sabotage fleet management.
How do IoT solutions produce clear and high-quality data? First, hardware advancements and second, established mechanisms for data filtering. An IoT-based On-Board Diagnostic Device with anti-vibration frames will easily withstand bumps, skidding, or other vulnerabilities that might interfere with proper data collection. Plenty of methods for data collection allow for the perfect combination and maximized initial value. Accelerometer, gyroscope, lidar, temperature, pressure, inclination, vibration, and other sensors can be incorporated into the vehicle to measure its characteristics. Data filtering, in turn, allows you to extract value from data for further insights, such as predictive analytics.
One more crucial point is IoT-provided flexibility. It allows for the building of a modular solution under data collection, scalability, and availability requests, which is a basis for comprehensive IoT fleet management. IoT provides a required amplitude of flexibility to meet the ever-changing economic and political context.
IoT fleet management for major logistics issues
Supply chain management is an ongoing struggle for sustainability facing multiple unpredictable challenges. The major IoT value, though, is to comply with risk management through total transparency, availability, and predictability. Today, logistics should seek to increase manageability given the following challenges:
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Global crisis and conflicts: Hardly anyone could predict wars in Ukraine and Gaza, low water levels in the Panama Canal, or the surging fuel prices the world experiences now. However, we can clearly say that more transparency in the supply chain would accelerate the switch to alternate modes, cost optimization, and 3-party interaction.
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Shortage of driving staff: For now, less than 10% of European drivers are under 30, while the majority of drivers opt out of this position after a year of work. Utilizing IoT can alleviate the driving process through the support of routing tasks, route planning, and driving support, as well as get rid of maintenance-related activities.
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New regulations on ecology & sustainability: For now, emission reduction could be a required, desired, or prospective regulation for a particular region, but the world is gradually moving to net zero emissions. Thanks to permanent data collection, IoT is a basis to ensure rigorous emission control of the fleets, as well as reporting, and strategy-creating support for end-to-end emission management.
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Increasingly complex networks: Complexity itself increases uncertainty and the risks of things going wrong. IoT provides for transparent real-time operational management protecting against fleet downtime or cargo loss. By collecting the analytics on the suppliers, you will also be able to competently “filter” the network leaving the most reliable ones.
IoT-based real-time monitoring creates a core to address multiple logistics challenges. The ultimate goal is to efficiently handle this data to gain constant support in risk mitigation. For now, all the cutting-edge technologies railed around the IoT to elevate the value data produces.
Promising trends of IoT fleet management
IoT predictive capabilities
Thanks to the AI evolution, data continues to tell us more about what is happening and what is about to happen in the vehicle. Where is the limit? The answer: When the world will be able to go without human intervention. Machine Learning-enabled tools for predictive maintenance increasingly utilize convolutional or recurrent neural networks that provide unprecedented results in pattern definition. Since a fleet is mostly homogenous, there is no need for a specific AI module for each vehicle, which provides substantial cost-efficiency. Engine temperature, vibration levels, oil conditions, battery condition, brake performance, and exhaust emissions are continuously monitored to detect tiny deviations in the behavior of vital and complementary components that will lead to malfunction shortly.
Advantages:
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Drivers are released from additional tasks caused by an unexpected breakdown.
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Costly downtimes are eliminated.
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Maintenance costs are optimized.
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.Delay expenses to third-parties are eliminated.
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The vehicle lifecycle is extended.
AI at the edge
A growing number of Edge AI solutions have identified a demand for real-time intelligent systems to support complex decision-making. In the context of IoT fleet management, these are ADAS capable of instantly capturing objects, processing the data, and advising the driver on upcoming obstacles or preferable routes. Unmanned vehicles are built on the same technology, but involve much more complicated algorithms for precise calculation and vehicle operation adjustment.
Why is Edge AI gathering speed? There are more and more cases of successful implementation of AI directly within devices in contrast to traditional server deployment. Hence – reduced latency, minimized delays within data transfer, and more balanced IoT ecosystems. Thus, we gain next-gen computing tools powerful enough to enable both data acquisition and complex analysis in one device.
Advantages:
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Real-time driver support.
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Autonomous vehicles for last-mile delivery.
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AR/VR fleet maintenance tools.
5G Involvement
5G is one more advancement that promotes near-instantaneous fleet management in real-time. 5G made a huge step forward from 4G, which allows us to talk about extended time-critical applications through reduced latency, increased speed of data transmission, and increased bandwidth. In contrast to 4G and LTE, 5G enables 100 times more devices, fleets, and other assets to be connected within one network without sacrificing throughput while decreasing energy consumption.
Advantages:
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Instant rerouting considering multiple data sources.
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Direct manager-to-driver support & assistance.
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Real-time adjustment of operational efficiency and proper use control.
Hybrid deployment
Logistics networks are generally highly distributed and complicated involving multiple third parties, intermediate and destination points, as well as shared asset management. Such a heterogeneous nature of logistics suggests the need for multi-level data sharing for IoT fleet managers under strict security measures.
Hybrid deployment involves local data centers, a private cloud for isolated use within one organization, and a public cloud hosted by service providers, such as AWS, Azure, or Google Cloud. Establishing the relevant delivery of data flows around the IoT fleet management ecosystem provides transparency for the customers, and flexible joint data management in real-time for all the stakeholders involved. Moreover, such an architecture provides for a wider opportunity for multiple applications to freely share the data within one system. IoT fleet management, inventory management, ERP, and predictive maintenance can converge for optimized workflow and more valuable insights generation. A comprehensive logistics management tool can be used to ensure time and cost-saving, as well as next-gen analytics.
Advantages:
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Enhanced security for sensitive business data.
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Extended service opportunities.
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Enhanced manageability for all operations.
A new milestone in IoT fleet management
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The first task with IoT fleet management adoption is a complete embrace of all the opportunities it provides and the operation it affects to define the optimal technology combination covering benefits in a business case.
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Real-time IoT-based monitoring with high-available sensors and cellular connectivity provides full transparency for the fleet allowing it to track its performance, emissions, driver behavior, and deterioration.
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To alleviate staff-related concerns, focus IoT tools on driver’s routing optimization, advanced route planning automation, and real-time support, as well as predictive maintenance to eliminate the need for the driver’s engagement in the repair.
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AI deployment has the biggest impact on predictive maintenance applications through substantial cost reduction and optimized inventory management. The revolution is expected for ADAS through the rapid evolution of the edge AI technologies.
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The more applications you connect under one system, the more opportunities for data management you unlock. Think out the hybrid environment for the most rapid, structured, and secure IoT fleet management.
Julia Seredovich is business operations manager at PSA. She has a decade of experience in the IoT development industry. Professional Software Associates is based in Clearwater, Florida, while Julia is based in Poland.