Short distances with great effects: AI-controlled transport logistics reduces expenses and minimizes delivery problems

Closely interlinked networks increase the demands on supply chain management, but also the risks along the supply chain. The winner will be the one who makes fast, effective and predictive data-based decisions.

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April 27, 2023
minutes read
Decision Intelligence
Supply Chain

The days of traditional, often linear supply chains are long gone. In the modern global economy, they create a highly complex, worldwide network of sometimes tens of thousands of suppliers, manufacturers, distributors and logistics companies that are connected to each other and work closely together. Along with the advantages of such small-mesh networking, however, the demands on supply chain management are also increasing the dangers for the supply chain, such as the dreaded bullwhip effect.

That's why more and more companies are turning to paretos' decision intelligence platform. It enables them to make fast, effective and predictive data-based decisions with the help of AI-based technologies and predictive analytics. This offers them competitive advantages, especially in the area of transportation logistics with its special challenges: from bad weather or road conditions to traffic jams and strikes to the effects of epidemics and wars - the more anticipatory and thus faster a company can react in such cases, the lower the negative impact on the business.

AI guides the way

Via its modern API, paretos first collects all data relevant to the company from available internal and external sources, including transportation management systems, sensors in vehicles, weather forecasts, traffic data and social media. These data are then analyzed using algorithms and machine learning to identify patterns and trends. Decision making can then be based on the analysis, gradually adaptable from manual to fully automated, depending on the desired AI autonomy and the defined rules of decision intelligence.

In transport logistics, companies benefit from real-time analyses and optimized forecasts from the paretos platform, for example, by learning which route is the fastest or which mode of transport is the cheapest, which vehicles carry out which deliveries at which time, or how transport capacities can be optimally used. The data-based process automation contributes significantly to cutting costs and delivery time while significantly increasing efficiency and customer satisfaction.

Real-life example: drought dries up river and supply chain

The past year saw many examples around the world where companies without AI-controlled transport logistics lost out. Examples include the truck blockades in Ottawa, Canada, in February, the warning strike by dockworkers in Bremen and Bremerhaven, Germany, in July, and the prolonged drought in the Midwest of the United States in October, which caused the level of the Mississippi River to drop to negative levels, bringing freight shipping to an almost complete standstill.

To illustrate the last example, let's look at some numbers: The second largest river in the U.S. is one of the country's most important trade routes for goods such as coffee, coal, steel and manufactured goods. About 60 percent of all grains exported from the U.S. alone, and nearly 500 million tons of annual goods, are transported via the river. As a result, the impact of the heat wave on companies that relied on the Mississippi River for their supply chain was devastating: freight prices rose a whopping 218 percent over 2021 due to heavy regulations on the number of permitted cargo vessels, their capacity and the volume of traffic. The short-term alternatives weren't rosy either: since a Mississippi freighter can carry as much cargo as 16 train cars or 70 trucks on average, increased costs were compounded by time delays and a much more negative carbon footprint.

The AI-based decision intelligence platform developed by paretos covers the entire supply chain of a company, meaning that the Mississippi River would have been identified in a paretos analysis for transport logistics as a neuralgically important factor that would have had to be considered with greater priority. By analyzing all relevant data - in real time wherever possible - the algorithm would have identified potential hurdles in transportation logistics at an early stage, taken alternatives into account and, based on the results, generated maximum-precision forecasts with efficient action alternatives. This would have made it possible to minimize costs and efforts by taking measures at an early stage, such as emergency shifts to other types of transport, precisely planned freight routes, and optimally managed vehicle fleets. Companies whose supply chain is particularly dependent on just one type of transport benefit greatly from the paretos solution.

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