The significance of data collection and the utilisation of data-driven insights are well-known facts within the community of shippers, carriers, and logistics service providers. Over time, accumulated data can act as a source of intelligence, empowering these companies to elevate their strategic decision-making processes. Meanwhile, real-time data can be used to make intelligent split-second decisions – like how to correct or replan when problems occur.
Artificial intelligence (AI) is invaluable in enabling companies to extract maximum value from their data. The realm of AI encompasses various facets. For example, “Statistical AI” empowers users to delve into vast datasets, uncovering concealed patterns and facilitating astute decision-making. As well as this, companies can also use past data to programme “symbolic AI” models, which can be used for “purpose-seeking” applications, such as process optimisation. Jonah McIntire, chief network officer at Transporeon, A Trimble Company, delves deeper into this intriguing landscape.
Automation vs AI
It is generally assumed that the meaning of automation and AI are synonymous with one another. However, though they’re interlinked, this couldn’t be further from the truth. The truth of the matter is that automation involves delegating mundane, often administrative, tasks to software. In contrast, true AI extends beyond this, encompassing the delegation of decision-making authority to software.
On the flip side, AI operates within predefined parameters but has the capability to generate unforeseen insights and conclusions. Thus, users can grant varying degrees of autonomy to AI with varying degrees of freedom. But, a more cautious approach is to allow the software to calculate options and make recommendations for a human to approve. However, it’s also possible for it to reach autonomous decision-making, operating independently without human intervention.
When implementing AI, several factors should be considered. Firstly, AI is best used for decisions with concrete financial values that are easy to score and have discrete, well-known variables. Fast decision-making cycles are also important. Much like human learning, AI thrives on experimentation. Therefore, if decisions are infrequent it could take several decades for the software to accumulate enough data to provide meaningful feedback. In an ideal world, AI models should be analysing thousands of decisions per day, meaning players would not use a model trained solely on their own data but with data gathered from across the industry. This collaborative, or “platform,” approach will inevitably foster progress for all parties involved.
So, how does AI transform how companies utilise their data through autonomous procurement, real-time ETA tools and decarbonisation?
Real-time ETA tools
For years, the disconnect between shippers and carriers has blighted the logistics transportation industry. And so, to enhance visibility, transparency and efficiency, it is imperative to connect the load receivers and load givers.
Consider the perennial challenge of forecasting load arrival times, which has traditionally vexed both shippers and carriers. Factors contributing to delays, such as strikes, traffic bottlenecks, and mechanical malfunctions can often appear to be completely random to the human eye. But, when an AI model analyses years’ worth of this data, hidden patterns start to emerge. In most cases, barring truly unprecedented circumstances, AI exhibits a remarkable proficiency in predicting ETAs, meaning employing AI-assisted real-time ETA tools can empower companies to remain well-prepared for load arrivals whenever they occur.
Streamlining procurement and quotation processes
Symbolic AI is ideal in spot buying scenarios, where companies operate within defined budgetary limits and clear constraints related to lead times and carrier preferences. In this context, negotiation structures are straightforward – participants can initiate offers, await responses, counteroffer, accept terms, or terminate negotiations. This paves the way for software to pursue its goals independently, saving thousands of manual administrative hours.
Statistical AI, for instance, stands poised to revolutionise the tendering process by leveraging extensive datasets to forecast pricing dynamics. Rather than soliciting carrier bids for load tenders, AI can present the tender along with a pricing proposal to a select group of carriers. Should no carrier accept the offered terms, AI can autonomously initiate additional tendering rounds as necessary.
AI’s transformative effect on sellers of logistics services empowers them to automatically serve customers with instant, accurate pricing for spot transports based on predicted market rates. This newfound capability enables load takers to expand their quote volumes, ultimately leading to enhanced opportunities and increased business acquisition.
The logistics and transportation industry faces mounting demands to curb carbon emissions, with end-user customers urging shippers to embrace decarbonisation. Meanwhile, shippers are exerting the same pressure on carriers by contracting them based on their sustainability practices, offering longer freight contracts to environmentally responsible carriers, and even offering premiums for eco-friendly transportation solutions.
In this new landscape where sustainability has a direct impact on profitability, it comes as no surprise that decarbonisation has emerged as a primary focus for both shippers and carriers. So how can AI contribute to addressing these challenges?.
It’s essential to highlight that, unlike procurement, sustainability often lacks a single, definitive “right” answer. Companies may harbour distinct visions of the ideal strategy, meticulously weighing factors such as “cost vs. emissions” or “certainty vs. emissions”. However, once shippers, carriers and logistics service providers have decided on their risk tolerance levels, AI can emerge as a pivotal ally in helping them stick to their sustainability objectives.
Companies typically adopt one of two mentalities, one being the cap-and-trade strategy, where the company decides that it won’t tolerate more than X emissions. The second involves a carbon tax, where a company decides to offset its emissions. These strategies enhance shippers’ and carriers’ ability to assess the “price per ton of emissions” in their procurement events whilst statistical AI emerges as a valuable asset in the realm of decision-making.
The next frontier for AI in the logistics transportation industry is rooted in collaboration
We currently stand at a pivotal juncture in the utilisation of AI within the logistics transportation sector. On one hand, it holds the potential to reduce administrative burdens dramatically and propel companies towards enhanced efficiency and sustainability. On the other, achieving this depends on effective data gathering and sharing and the cooperation between industry players. To optimise the benefits for all involved parties, shippers, carriers, and logistics service providers must embrace collaborative digital platforms for data sharing, fuelling the capabilities of AI models. By adopting this forward-thinking approach, we can expedite our journey towards realising the industry’s ambitions for digitalisation and decarbonisation.
Jonah Mcintire, chief network officer at Transporeon