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3 Problems that logistics and transport companies are solving through Artificial Intelligence

Transport and logistics companies are facing a host of challenges: here's how they have been using AI to solve these problems.

The transport and logistics industries are seeing a rapid adoption of artificial intelligence to solve their core challenges. Through our work, we've seen three areas where we see the highest impact and adoption of AI in these industries: improving capacity forecasting, optimizing maintenance, and setting better pricing. We give you the details of each of these cases below.

Problem 1: Improving capacity forecasting

Understanding and forecasting the capacity usage of your fleet is one of the main challenges that transportation companies face. Because of fluctuations of demand, delays in the network, unexpected breakdowns, it's often extremely hard to predict how much capacity is needed at each moment in time. Furthermore, many transportation and logistics companies cannot change their capacity at a moment's notice - getting ships, trains, trucks and planes and the required staff from point A to point B takes time and effort. Furthermore, contractual obligations and fixed network fees often limit the ability of transport and logistics companies from making rapid changes.

As such, short term predictions, which have been within the reach of human experts, have not been extremely useful in an operational sense. However, Artificial Intelligence has made large inroads in using very large datasets of historical data to make accurate long term forecasts that are extremely valuable for these companies. These forecasts allow companies to make long-term plans that take into account fluctuations in capacity to optimize their network.

Problem 2: Optimizing maintenance

Managing maintenance is a complex topic in the logistics and transportation industry. On one hand, well-maintained assets like trucks, ships, planes and trains have a longer life-span, are safer, and reduce the risk of costly breakdowns. However, deciding when to maintain an asset, and which parts of the asset to maintain is a difficult topic. Most companies generally perform maintenance tasks at regular intervals, to simplify this process. Nevertheless, with a growing number of sensors on most assets, and thanks to a large history of breakdowns and types of maintenance that were executed that most companies possess, it is possible to make this process radically more efficient.

By feeding this information to an AI algorithm, it's possible to predict which asset needs to undergo which type of maintenance, often cutting costs significantly. Furthermore, it is possible to take into account moments of peak demand to better plan moments of maintenance on critical assets, not just impacting costs, but also revenues.

Problem 3: Setting better prices

Setting prices in a changing environment is notoriously hard. Because most transport companies' cost basis is often very influenced by factors that are difficult for a human to forecast, such as fuel prices, the usage of their fleet, and the speed at which they can transport goods or people, etc. Furthermore, the ability to set prices efficiently is made more difficult by changing competitor's prices and fluctuations in customer demand.

Most transportation and logistics companies deal with this complexity in one of two ways. The first is to simplifying their pricing, which adds risks in case of fluctuations in their cost basis, such as a sudden surge in fuel pricing or a under use on one of their routes. Furthermore, this static nature of pricing often misses opportunities to leverage seasonal and event-focused peaks of demand into higher prices, and as such, higher revenue. The second, and more classical way, is to set up a team of pricing experts with a narrow focus on a geographic area or specific type of transport. This approach often scales poorly, as these pricing experts require extensive training and expertise. This means that the long tail of the logistics and transport industry (small ticket sales) don't have enough margin to justify these pricing expert's time. Furthermore, more complex sales, that span the area of expertise of more than one pricing expert, requires complex coordination that adds a layer of cost.

Digital-first transportation companies like Uber, have long invested in algorithmic pricing solutions, such as surge pricing, but we see more and more traditional companies applying similar strategies, using AI to leverage their vast historical data to better set prices. These algorithms use both data of sales, operations, markets and competitors to either fully automate the long tail of sales (small ticket sales), or to augment pricing experts by proposing insights pricing tips. The return on investment of these solutions have generally been very high, showing that AI can indeed solve real, painful problems, at scale.

And beyond the core challenges...

AI also shows great impact in a number of fields that are shared by many business, such as reducing customer churn, reducing personnel turnover, etc. At this point, it's clear that AI has an important role in the transport and logistics industry, and is quickly transforming the industry, both in its core and in general business challenges.

If you're curious to find out more about AI in the transport and logistics industry, feel free to contact us!

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