AI is truly the word of the moment. The proliferation of its use across different industries and sectors around the world has given it the title of ‘Word of the Year 2023’ by the Collins Dictionary, and in the UK, the 2023 budget committed almost £1 billion of government funding towards AI research. And for fleet and transport managers, we are on the precipice of tapping into the potential of this technology.
The fleet and transport industries continue to face numerous challenges, which I am sure you are aware of: pressure to reach ambitious sustainability goals; inflation and rising costs; driver shortages and employee well-being; and safety and security regulations. But the promising thing is, with most transportation and fleet operators already understanding the importance of data collection and data-led decision-making, the solution to these challenges is not far off just connecting the dots, with the added help from AI.
Let us split these challenges into three key themes and explore how harnessing AI-driven predictive analytics and machine learning algorithms can benefit fleet and transport management:
Rising costs and the drive to go green
Inflation, a cost-of-living crisis, and rising supply chain and production costs are causing trepidation throughout the UK at the moment. On top of this, ambitious sustainability targets were created by the 2030 Agenda for Sustainable Development, which was adopted by 193 UN member states – including the UK – at the United Nations Sustainable Development Summit in 2015. Just how are fleet and transport managers expected to navigate this pressure to achieve more, with less?
Emerging AI applications are enabling businesses to streamline their route planning processes and optimise overall fleet performance. For example, AI-driven predictive models can effectively anticipate traffic patterns, weather conditions, and other variables like terrain, to provide real-time route recommendations on the most fuel-efficient and time-effective journeys, helping keep fuel costs and driving time low. In addition to this, AI-driven predictive maintenance systems can detect potential equipment failures and schedule timely maintenance, reducing both vehicle downtime and repair costs.
With sustainability high on the C-level agenda, there is a huge opportunity for AI applications for energy management and sustainability in fleet management. Like above, through AI-enabled predictive models, organisations can identify energy-efficient routes, implement fuel-saving strategies, and adopt environmentally conscious supply chain practices. This helps reduce carbon emissions and contributes to a more sustainable and environmentally responsible fleet management ecosystem.
Workforce well-being
AI presents unparalleled opportunities to improve safety and security for logistics and transportation workforces, through the implementation of advanced driver monitoring systems and predictive maintenance protocols. By leveraging AI-powered sensor tech and data analytics, fleet managers can gain comprehensive insights into driver behaviour, vehicle performance, and maintenance requirements. This enables the fleet managers to proactively identify potential risks and minimise accidents.
An example of this type of safety feature is driver-facing AI cameras which detect high-risk or unusual behaviours, alerting both the driver and the fleet manager to potential driver fatigue or distractions which could result in an accident. Video telematics is enjoying increasing adoption, thanks partly to dashcams which use advanced machine vision algorithms that can detect and analyse various driving events in real-time.
By improving working conditions and monitoring driver behaviour, the fleet and transport industries are more likely to experience a reduction in workforce downtime, as well as improvements in recruitment success rates.
Securing the future of fleet and transport management
One drawback of data-led decision-making is the accuracy and reliability of the data itself. When it comes to AI-generated insights, this is even more critical with decisions impacting operational efficiency and driver safety.
The precision that AI-driven analytics offers is paramount to its overall success. Fleet managers can ensure the partners they are working with are dedicated to developing AI solutions that undergo rigorous testing and validation, ensuring that only accurate insights are provided for their users. Some software partners will also have implemented robust security protocols and data encryption measures to safeguard the information of users and their fleets.
Alongside the likes of sustainability, which we have covered, maintaining the privacy and integrity of sensitive information within the fleet management ecosystem should also be a top priority for businesses.
Driving change
AI has generated enormous hype this year and we are beginning to see an increasing number of success stories in various sectors. For fleet management, industry-wide pick-up is yet to come, and I fully anticipate that we will soon enable fleet solutions providers and fleet managers alike to harness the technology not just to reduce administrative work and time, but, more importantly, to achieve goals and deliverables in fleet efficiency and in sustainability, in tandem with a safer, more secure workforce.
Author: Aliaksandr Kuushynau, Head of Wialon, Gurtam.
