AI in Transportation and Logistics

Today, AI is not a luxury but a necessity for organizations in transportation and logistics industry to gain and keep their position in the market.

All movement of people and cargo on a global scale results in an enormous amount of data. If any industry is in a position to take advantage of AI, it’s the transportation industry. Thanks to globalization, many providers in the sector work with the third-party suppliers from all around the world, generating invoices and other necessary documents so numerous that they are difficult for humans to handle.

Fortunately, this is where AI systems shine: working with large amounts of unstructured data to extract information and take action.


High-Value Use Cases

Below is a summary of key data science use cases in logistics and transportation.

Operational Efficiency: In general, the logistics and transportation industries are largely driven by economics: fuel cost, security measures, time to delivery, supply chain reliability, domestic distribution networks, offshoring, and so on. By linking historical activity data with consumer profiles, economic indicators, and geolocalized market data, logistics and transportation providers are able to predict demand with increasing accuracy. This allows for anticipation of daily volumes, optimization of delivery routes, and the allocation of resources accordingly.

Dynamic Pricing: The plethora of factors that impact logistical efficiency also impact end-product pricing. A change in one computational ingredient (e.g., increased fuel cost, security-related shipment delay) can have a profound impact on the overall shipping cost and, consequently, the product price. Price determination should be malleable and based on real-time cost data. When leveraging AI tools, it is possible to incorporate cost-sensitive components, often combined with external dimensional data (e.g., weather patterns and transport time), to accurately predict an optimized price.

Traffic Management Operations and Other Smart Infrastructure: Smart infrastructure put in place by the public sector will increasingly work in partnership with the technology embedded in private vehicles. These AI-based systems are not only useful for private transportation, but public transportation as well. This level of understanding, coupled with data from other sources (e.g., satellite, GPS, cellular phones), provides useful information to render transportation more efficient overall.

Predictive Maintenance: Predictive maintenance offers the opportunity to use machine learning and AI to glean powerful insights about the lifecycle of equipment being used. On top of traditional machine learning-based predictive maintenance, second-order maintenance can add another level of AI to optimize subsequent decisions about a high-value asset’s upkeep. Take, for example, a truck from a large fleet with a part identified by a predictive maintenance system as being N days away from failure. Once identified, a member of the data team should be ready to send a secondary follow-up report to the maintenance team detailing the best possible options for time and place of service.


Dataiku for the Transportation and Logistics Sectors

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. To solve the complex challenges facing transportation and logistics businesses, Dataiku offers:

  • An easy-to-use way for anyone — technicians, analysts, data scientists, managers — to interact with data, from raw format to predictive model.
  • The ability to centrally and seamlessly connect to data, whatever the form (structured or unstructured) and wherever it’s coming from (database, sensors, CSV files, etc.).
  • A simple and fast interface for ETL, including interactive data cleaning and integrated advanced processors.
  • AutoML features, including the ability to compare dozens of algorithms directly from the Dataiku interface (both for supervised and unsupervised tasks).

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