Automated Oracles

Early warning systems buy time and provide a basis for prompt action.

Traffic doesn’t always flow as smoothly as at this intersection in Cologne, Germany. Logistics early warning systems help drivers to avoid construction sites and congestion, and help companies to safeguard their supply chains. Photo: Jörg Greuel/Getty Images

Preparation is the buzzword for efficient action. If you know about a traffic jam, you can drive around it; when a drought is imminent, you can save water. The coronavirus revealed a world unprepared. By mid-2020, more than half a million people had died from Covid-19 infection. And the question arises: Why is there no early warning system for pandemics?

Early warning systems are already standard in a number of other contexts. On December 26, 2004, the ocean floor off the coast of northern Sumatra shifted. This underwater earthquake was one of the strongest in a century, measuring 9.3 on the Richter scale. Its tremors caused waves to surge and slam ever higher against the island and many other coastlines of the Indian Ocean. Nobody was prepared, and more than 25,000 people died. A consortium led by the German Research Centre for Geosciences (GFZ) in Potsdam then set up an early warning system in the Indian Ocean. A network of seismometers detects earthquake epicenters, and satellites measure movements of the earth’s surface via GPS. If the system were to respond to every vibration, there would be many false alarms. GPS buoys and pressure sensors on the ocean floor help measure each wave following a quake. The data are transmitted to a processing center and compared with previous records. This wealth of data yields models that can inform operators within minutes of the speed, direction, and magnitude of potential tsunamis—in other words, whether they are harmless or hazardous.

Analysis via machine learning

Early warning systems can also help companies. One example lets them prepare for disruptions in their supply chains. Resilience360 Supply Watch is the name of a system used by the German logistics company DHL. The program defines around 140 risk categories, including financial, environmental, and social factors. Are media outlets reporting criminal activity in a certain region? What is the incidence rate of quality complaints? How do inventory levels look—do warehouses have sufficient stock? The DHL system analyzes data from up to 30 million online and social media posts and makes the results of its risk analyses available to clients. The program itself continuously evaluates the relevance and potential consequences of its data. It can do this with the help of machine learning.

Read the full article in Porsche Consulting Magazine