Trade sanctions in the global economy

In the research program CoyPu we work on early detection of economic risks in global supply chains together with Siemens, Infineon and DATEV, among others, as well as strong partners from research, such as DIW, TIB and InfAI.

For early detection of economic risks on supply chains, trade sanctions are analyzed as part of economic sanctions.

Based on open data on trade sanctions, a graph between countries, products and trade sanctions was built. A distinction is made between countries that impose trade sanctions and those that are targeted by these sanctions. Each trade sanction has a start date so that a representation of when the sanction goes into effect is possible.

For a representation of what happened, an exemplary time series was determined and visualized using Gource. Since the complete visualization is very large, here is a small section:

On the left side of the visualization, there is a ranking that lists the most frequent pairs of countries that are linked due to trade sanctions. Further to the right, it shows which sanctions countries are imposing on certain products (e.g., export ban on certain metals).

It is noticeable that there are points in time with particularly high regulatory intensity. Without more detailed knowledge of simultaneous or preceding crises or political events, the time points are difficult to explain ex post. Beyond an explanation of the geo-political context of a sanction, knowledge of a sanction should be relevant for those actors whose supply of raw materials or intermediate products or export opportunities could be affected.

With the chosen treatment of trade sanctions as a network of the global economy, remote events can be determined on industries or even individual companies. Here, we are researching the propagation of events in networks to also predict ripple effects for specific industries or companies.

Potential applications for supply chain monitoring include:

  • Automated warning of newly detected import restrictions.
  • Automated warning of announced export restrictions
  • Prediction of direct supply restrictions
  • Prediction of indirect supply chain constraints due to ripple effects

With automated warnings and prediction of indirect impairments, risks can be sighted, evaluated and managed earlier in the future due to smart early detection, before supply bottlenecks or disruptions occur.

For further questions about the project and the application of analyses and data feel free to contact us at hello@implisense.com.

Identify global risks earlier

Implisense customers will be able to benefit from the first results from the CoyPu research program (https://www.coypu.org) in the near future. In this ambitious research program, we are working on early detection of global risks in worldwide supply chains together with Siemens, Infineon and DATEV.

The basis for an initial assessment of possible risks in global supply chains is provided by public data, for example from the WHO, UN and dedicated crisis monitoring services. From this, basic estimates for the probability of risks as well as the expected ability of the country to deal with a risk can be measured.

Implisense has analyzed public data and stored it in a graph database in order to be able to assess all analyzed entities, such as companies or critical infrastructures, with regard to their geopolitical risk in the future. In the future, customers will be able to have the list of their suppliers automatically checked to determine whether new risks are apparent and risk management must become active.

A particular focus is on the expected consequences of climate change and the associated crises and adjustments.

Analysis of the most affected countries due to expected climate change (WorldRisk Index data).

Intended use cases

  • Identify existing suppliers with high risks
  • Qualify new suppliers faster for risk management
  • Plan sourcing to include new risk assessments


Do you have questions about the project and use cases? We welcome messages at hello@implisense.com

Big data analysis of financial indicators in SMEs

We are pleased that Investitionsbank Berlin-Brandenburg is funding the development of a big data analysis of financial indicators in the German SME sector. The project, which is designed to run for one year, is aimed at the automated collection and comparison of key financial figures with the goal of identifying unusually deviating financial figures. This should also simplify the work for those who rarely study balance sheets and need corresponding assistance to identify important changes with their customers, suppliers or competitors. The technical challenge is to identify relative outliers compared to similar companies. For this purpose, a solution based on in-memory databases is being developed to create several thousand peer groups based on industry, region, size and age of companies, which are then used to put the level of certain financial ratios (such as turnover or net profit) into perspective and to identify real outliers. In-memory databases allow these comparisons to be analysed in a much shorter time with significantly more key figures. The first results from this work are expected from Q3 2021. Interested parties can gain an early insight into the ongoing development.

International company holdings

International company holdings of Deutsche Telekom AG

As the largest economy within the European Union, the German economy is particularly linked to the world through trade relations. For the analysis of global supply chains, it is helpful to better understand the international networking of companies through participations or associated companies abroad. For this purpose, work on an automatic extraction of international company shareholdings from annual financial statements was started at the beginning of 2020.

The particular technical challenge was to recognise the international shareholdings mentioned in annual financial statements, interpret them correctly and convert them into a standardised form to support subsequent analyses.

The extraction component, which has now been successfully developed, is being transferred to productive operation at Implisense GmbH. This makes it possible to analyse new documents with information on changed shareholdings within a few seconds.

Would you like to benefit from the results of this work for your application? Then we would be pleased to receive your contact request.

AI project CoyPu: Early detection of indirect risks in global supply chains

Early detection of indirect risks (red) in complex Supply Chains (blue)

In recent months, consumers and businesses alike have sometimes painfully experienced how interconnected and dependent the global economy is in the supply of critical goods. Even remote events can lead to unexpected consequences in the German economy in a very short time. In parallel, the German government and the European Union are working to increase regulatory requirements for compliance with social and environmental standards in supply chains.

Operational work for a new data and analysis platform has been launched with leading companies and research organisations in Germany. The planned platform will provide easier access to monitoring global supply chains for new risks and thus contribute to future resilience in the German economy.

This development is funded by the German Federal Ministry of Economics and Technology in the third call of the AI Innovation Competition. Implisense will provide the strong consortium with detailed data on companies, markets and events so that the partners are able to perform novel graph-based modelling and simulation of events for globally connected companies as quickly as possible.

We look forward to working with:

The project website can be found at https://coypu.org/.