AI-Supported Risk Forecasts AI Instead of Crisis: Resilient Supply Chains Thanks to Modern Technologies

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Supply chains are becoming increasingly complex and vulnerable to disruptions, requiring effective risk management. Read here about the advantages AI can offer in this context.

What do Taylor Swift, homeschooling during the pandemic, and the container ship "Ever Given" have in common? They all had a significant impact on specific supply chains at times: the American singer caused a temporary shortage of bracelet kits because her "Swifties" wanted to exchange handmade bracelets during her tour. Homeschooling resulting from school closures led to a surge in demand for desktop computers and other technical equipment. And the grounding of the "Ever Given" caused hundreds of cargo ships to back up in front of the Suez Canal, with their goods arriving at their destinations weeks late.

Companies must respond to such unforeseen events as quickly as possible. Supply chains need to be made more resilient in advance. For risk management, AI-based software solutions are becoming increasingly important.

Risk Analyses Against Vulnerable Supply Chains

The challenges in supply chains have been increasing for years, as supply chains have become more vulnerable due to geopolitical conflicts, supply bottlenecks, labor shortages, rising inflation, and the impacts of climate change. According to a 2024 survey by the logistics magazine dispo, the biggest disruptive factors for companies in their supply chains were price changes (45 percent), labor shortages (34 percent), cyber threats (31 percent), shipping challenges due to geopolitical issues (23 percent), freight traffic bottlenecks (23 percent), and storage shortages (22 percent).

No wonder, then, that risk management is gaining increasing importance in companies: 29 percent of the surveyed companies rated risk management as a significant challenge, which is 8 percentage points more than the previous year. "To identify potential disruptions at an early stage and take appropriate countermeasures, forecasting risks in supply chain management is essential," emphasizes Dr. Jan Mazac, one of the managing directors of Biss GmbH.

The Oldenburg-based (Germany) company has developed cloud-based software for risk management in global supply chains. Mazac and his team are therefore aware of the pain points companies face regarding their supply chains and risk management—and also how they can effectively counteract them with precise risk analyses.

Structured And Unstructured Data from Various Sources

However, such risk analyses require various types of data. Using, for example, demand patterns, weather data, or geopolitical events, predictions about potential supply bottlenecks can be made. "To do this, however, large amounts of data from different sources need to be consolidated," explains Markus Schnüpke, also a managing director of Biss GmbH. This includes, in particular, historical sales and order data, real-time inventory information, and supplier data—including their financial stability and delivery reliability. Additionally, external data such as weather reports, political events, or market trends are relevant, as well as social media and news to identify sentiment trends.

"By integrating all such data, a comprehensive assessment of potential risks is possible," says Schnüpke. However, the analysis becomes very complicated due to the vast and heterogeneous amounts of data. Companies often develop their own programs and work with complex business intelligence processes—but this requires a lot of conceptual effort with correspondingly high costs. On the other hand, when AI-based systems come into play, they make forecasting not only easier but also faster and more reliable.

Artificial intelligence can analyze data in real-time and immediately detect anomalies or deviations from normal patterns. For this purpose, AI systems such as Biss/Caigo generate a robust and expandable data foundation from structured and unstructured information from various sources—such as supplier data, supplier connections, action catalogs, calculated risks, comments, inspection reports, certificates, chats, or emails. "The AI system creates an intelligent knowledge platform from all the data, enabling complex data sets to be transformed into actionable information," explains Schnüpke.

Such AI systems use machine learning algorithms. They recognize statistical patterns and correlations and assess the likelihood and potential impact of risks. To do this, they employ advanced analytical methods such as classical time series models or complex simulation techniques like Monte Carlo analyses.

Identify Risks Early And Proactively Counteract

With their smart features, such solutions generate predictive models upon user request to evaluate potential risks. They clearly visualize these models with dynamic tables and precise graphics in dashboards and reports. This makes the forecasts easily accessible and understandable for users. Sudden changes, such as political events, natural disasters, or trends in the news or social media, can indicate upcoming disruptions and transport delays.

AI-based software solutions can also derive forecasts regarding disruptions in the supply chain from sudden increases or decreases in product demand as well as indications of financial difficulties or operational problems among suppliers. "If companies identify such disruptive factors early, they can proactively take action and counteract," describes Mazac.

Respond Correctly Through Software Suggestions

It is advantageous if the software itself generates suitable recommendations for action to minimize the identified risks. High-quality solutions are capable of doing exactly this: For example, in the case of expected demand spikes, the software suggests inventory adjustments to respond to increasing demand with higher stock levels.

If it detects issues with a current supplier, it will suggest switching suppliers. If disruptions are expected on a transport route, adjusting the transport route is a sensible measure to avoid potential delays. With early risk detection in real-time and the proposed countermeasures, companies can respond before serious disruptions occur.

Since AI evaluates risks automatically, companies also save valuable capacity and can focus their resources on the most critical areas. "With such AI-supported software, companies gain a powerful tool for strategic decision-making and optimizing their global supply chains," says Schnüpke.

The prerequisite for such detailed and reliable forecasts as well as suitable recommendations for action is a comprehensive, coherent data foundation. "AI is not a crystal ball," Mazac states figuratively. "It cannot deliver anything that it has not learned." All forecasts are therefore based on existing data—the more extensive the data foundation, the more accurate the predictions. It is also important that data is continuously fed in and updated so that the AI can learn and improve its accuracy.

Conclusion: AI-Supported Systems Offer An Advantage for Efficient Risk Management

Unpredictable events—whether the exchange of friendship bracelets during a pop star's tour, a pandemic, or a shipping accident—can have significant impacts on global supply chains. The increasing complexity and vulnerability of supply chains due to geopolitical conflicts, labor shortages, inflation, and climate change make efficient risk management essential.

AI-supported systems offer a decisive advantage here: They analyze large amounts of data in real time, identify potential risks early, and suggest proactive measures. Companies that adopt such technologies can make their supply chains more resilient and respond to crises more quickly.

*Julia Kowal is an IT editor.

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