Rising costs, unpredictable disruptions, and geopolitical changes – all these factors have contributed to companies rethinking their strategies regarding procurement and manufacturing networks. Nearshoring or reshoring is the magic word, meaning the relocation of supply chains to geographical proximity. Systemically important industries in particular (such as energy, health, and food) have been and continue to be encouraged to (re)locate their production and procurement to Europe. What impact does this trend ultimately have on supply chain financing? This is a question we want to explore explicitly here, with a focus on Europe.

A look back: From the 1990s until 2020, global procurement strategies were all the rage. Companies focused on lower labour costs and benefited from tax incentives, taking advantage of advances in information and communication technology. The result was highly efficient and optimised supply chains to China and other Asian countries; products that were assembled in several countries and then sold in a completely different region. The downside: companies became more vulnerable to supply bottlenecks and production downtime, such as those caused by the coronavirus pandemic or the war in Ukraine. The deglobalisation and nearshoring that have been taking place for several years were and are a logical consequence of this: the aim is to relocate production and procurement back home in order to increase resilience.

It’s getting more expensive, but safer

rates and labour costs are comparatively high in Europe, and significant investments must be made in setting up new production facilities, infrastructure, and workforce training. Legal and regulatory frameworks often present additional hurdles. Ultimately, nearshoring transitions can take years and cannot be implemented “just like that.” However, it should be noted that nearshoring is not always and not necessarily an “either-or situation”: many companies simply expand their portfolio of suppliers from nearby regions so that they can step in if their main partner encounters difficulties. Or nearshoring only takes place for a specific subgroup of products (segmentation of the supply chain). Basically, nearshoring is more suitable for highvalue products with high margins, where transport costs are also high, or for innovation-driven segments with frequent product changes.

There is no doubt that in today’s increasingly volatile business environment, it is important to reevaluate procurement locations. Nevertheless, nearshoring should not be seen as a panacea. Rather, it is important for company management to keep an eye on the big picture. In general, nearshoring reduces risks due to greater political stability, fewer logistical challenges, and lower currency risks. However, new risks arise in turn. These include the concentration of regional risks (suppliers from the same geographical region are simultaneously affected by a crisis, etc.), dependency risks from new suppliers, and data risks. The latter arise because information gaps must first be closed when establishing new partnerships.

In the course of nearshoring, supply chain flows are generally becoming even more digitised. This also presents an opportunity from the perspective of SCF providers, as they benefit from improved data quality and transparency. More accurate data allows, among other things, more precise predictions to be made regarding potential risks. 

From the perspective of SCF providers, nearshoring is both a blessing and a curse. It is a blessing because it reduces political instability, exchange rate risks, and logistical disruptions. And it is a curse because it involves new supplier relationships, generally requires more flexibility and, above all, creates a certain pressure to be efficient due to higher costs. In other words, you have to look even more closely, analyse even more precisely, and go into even more detail. This is where higher data quality comes into play: in combination with advanced, AI-based predictive analysis models, it can be used to create various risk scenarios, which in turn provide an optimal basis for decision-making.

From an SCF perspective, the greatest risks include payment defaults and delayed payments by suppliers. As we have already discussed, these risks tend to be reduced by nearshoring. However, for the reasons already mentioned (cost pressure, new supplier relationships), these factors remain significant variables from an SCF perspective. In a predictive model, payment behaviour can be continuously monitored to send early warning signals to the SCF provider. Ideally, such systems provide a comprehensive, multidimensional overview of hidden risk factors.

Are there any significant deviations from the normal payment pattern? Can statistical outliers and behavioural anomalies be detected in the transaction data? All these findings should ultimately lead to insights that help you distinguish genuine deterioration from false alarms. In other words, you will be able to ask SCF customers the questions that really matter: Why is your company currently performing particularly well or particularly poorly compared to the industry as a whole? This allows you to identify impending payment difficulties or fraudulent intentions in good time.

AI-based predictive models also reduce manual checks by SCF providers, increase their productivity, and, in the best case, are scalable – meaning they are designed for use in both small and large companies. To reap all the benefits mentioned above, such models must be trained with real SCF customer data and integrated into the respective customer ecosystem. This is not something that can be done overnight; it requires comprehensive implementation planning on the part of the SCF provider.

Due to increased economic and political volatility, nearshoring has become an alternative to the globalisation that has prevailed to date. The aim of this trend, which can also be observed in Europe, is to achieve greater resilience among companies. In concrete terms, this means a shorter supply chain for companies, with lower transport costs and increased security. On the other hand, there are sometimes high initial investments in new production and procurement systems, as well as higher labour costs and strict regulations in Europe.

From the perspective of SCF providers, the focus should also be on customer payment behaviour in nearshoring. After all, this is where the greatest SCF risks still lie. In this context, AI-based prediction models offer the opportunity to actively manage potential risks on the customer side. The resulting efficiency gains are important success factors for SCF providers.

Sources:
• Bain & Company: Nearshoring: Overcoming the Obstacles, 2025
• LBBW: Has Offshoring had its day? 2022
• SAP: Evaluating nearshoring? Here are five realities, 2024
• KPMG: Trends in Supply Chain & Friendshoring, 2025
• Eurystic: Nearshoring: How It Affects the Supply Chain and How to Anticipate It with Predictive Models, 2025
• Huszarconsulting & efcom: AI assistants in factoring: Evolution or disruption?