The subject of the future of A.I. (artificial intelligence) can often spark thought-provoking discussions, especially when it comes to how future advances may help to shape logistics operations for both light and heavy goods vehicles.

Quite often, questions such as these will come up:

• ● How does AI help in supply chain and logistics?
• ● How might AI affect transportation in the future?
• ● How will it be used alongside autonomous vehicles?
• ● What is the use of AI in logistics planning?

If you start to think about AI’s “end game”, you probably picture fully autonomous vehicles driving the streets – literally controlled entirely by the computer “brain”. This could offer:

● Huge advances in vehicle utilisation (removing the restrictions on driver’s hours). Some say an increase of nearly 50% utilisation. (†)
● Huge benefits in fuel economy (reducing over-enthusiastic braking and acceleration). Some say 10% reduction (†) or even more.
● Huge additional fuel benefits by the creation of mass convoys of autonomous trucks interconnected in “platooning” to reduce drag. Some say another 12% reduction in fuel (†) here.

(†) Source: AXA

With almost a third of the cost for freight operators sitting with the driver themselves, the million-dollar question arises for many: are we really going to get rid of drivers entirely?

That is where concerns begin, especially when we consider whether a true “driverless” delivery vehicle makes sense or not. And are we ready for them? Or could they still be many years away?

Firstly, there is the quandary of resolving what would happen if an accident were to occur. Who would be liable for responsibility? It is true that the vast majority of road accidents are currently related to human error and fatigue, both of which would be reduced substantially by using an AI based driving system. Notwithstanding that however, there could still be many examples where the ignorance of road users and pedestrians are to blame – or perhaps extreme weather and road conditions, that can potentially provide scenarios where even the very best model has yet to be trained. There are still many developments needed to cover the inadequacies of current driverless vehicle learnings.

Secondly, there is still some way to go technology wise, in regards to the investments needed to build all the right infrastructure for the vehicle to cloud. Vehicle to vehicle communications, on top of the need to totally secure that entire network in order to remove the ability for criminally minded activists to hack into the system for ill gotten gains (whether financial or terrorist activity).

And finally, while labour and driver shortages are prevalent in some economies, exaggerated further through the Covid-19 pandemic perhaps, there will no doubt be many labour related challenges when it comes to removing the driver from the operation entirely, due to the social and economic impacts of doing so. Indeed, would governments start to tax the “robot” in the way a current driver would be on their income?

Perhaps these yet unanswered questions will eventually become resolved in the years to come, but then it is already well worth considering what “autonomous” vehicles, or at least AI augmented vehicles could bring by way of immediate benefits?

Some Benefits of AI Augmented Vehicles in Logistics

● Driver assistance feedback – to help to provide the manual driver with the same inputs audibly/visually to advise on smoother driving in real-time, so that the amount of heavy acceleration and braking is reduced. The direct connection between the vehicle and the vision system also significantly enhances the debrief and telematics information that may already have been available. This can provide supporting evidence in developing better behaviour.

● When appropriate, provide stronger emergency braking support when an incident is detected, and the driver has already acknowledged it.

● Driver fatigue warnings, which are more than just regular break advice. Using in-cab vision to monitor driver body responses and provide alerts should drowsiness indicators set in. These however one could imagine could meet privacy challenges from the drivers themselves.

● Vehicle truck diagnostic monitoring utilising on-board engine management and component sensor data to prepare service and replacement part schedules to reduce the vehicle breakdown probability and lost utilisation time.

There are potentially even more capabilities to add to this list. There does also appear to be a strong argument around how we should immediately start to utilise technology in logistics, particularly AI support.

It promises to be a very interesting period over these coming months and years, as these artificial intelligence developments in logistics and supply chain become commonplace. It is inevitable with such tangible cost and environmental benefits available, that the pressure will grow to successfully transform from pilot projects to full-blown commercialisation. Collectively, we must watch this space.

Here at Compass Point Partners, we are the group of experts who know everything there is to know about the innovation of AI and machine learning in supply chain and logistics. Over the years we have helped many other logistics companies to innovate those processes and to effectively utilize AI and machine learning. If you feel your business can benefit from our expertise then please get in touch with us today, either by connecting through LinkedIn or completing the contact request form.

Author

Keith Holdsworth is Vice President for Integrated Business Planning & Supply Chain Projects of Compass Point Partners. In this role, Keith focuses on using his extensive Supply Chain experience to create or enhance the Integrated Business Planning (or Sales & Operations Planning) process and deliver operational advances that improve revenues and gross margin, working capital management, reduce cost of goods and enhance customer service and experience.

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