The object of this article is to examine the rating of systemic risks of groups (supply chains) of customers, also containing thousands of subjects. In the era of the globalized and interconnected economy, it is clear that companies, banks and countries are part of a huge system of systems. As turbulence increases and the frequency of extreme events increases, the importance of a systemic view of the economy becomes apparent. In fact, due to turbulence and fragility, in highly interconnected systems the propagation of shock and contagion is very fast and can lead to a huge number of possible outcomes often surprising. This number increases with the complexity of the system. However, the idea and concept of “systemic risks”, although it became popular after the collapse of the economy in 2008, is very difficult to define. By “systemic” we naturally refer to everything that can have repercussions (damage, consequences) at the system level.
In such an environment, rarely will a company or bank fail because of the loss of a single customer or supplier. Due to the increasingly interconnected nature of the economy, this event will be predominantly caused by collapses of supply chains – all of a bank’s customers are part of a huge supply chain – or crises of customer ecosystems or even entire market sectors.
Conventional pre-2008 risk assessment, management and rating techniques were conceived in an almost turbulence-free world and are not applicable to the new situation. This paper illustrates and proposes a new approach, based on the quantification of systemic resilience, that allows to better understand the dynamics and risks of business ecosystems or supply chains.
The idea, therefore, is to provide banks with an innovative method for measuring and rating systemic risks in the context of their customers.
Systemic risks are not well defined and are generally poorly understood. This leaves the door open to regulatory discretion, which can exacerbate these risks further.
The idea is to approach the problem from a completely new perspective. Instead of trying to measure risks using complex and difficult to validate models, the goal is to directly measure the resilience of a given system. The benefit of measuring resilience stems from two key issues. First of all, resilience is a physical and real quantity that quantifies the resistance of a system in the face of shocks, and therefore is easy to understand. Secondly, the calculation can be performed without the construction of a model. All you need is trend data, sampled periodically and reflecting the functioning of the system in question. Good examples are the balance sheet, cash flow, reports or income statement data, which all companies own.
In this document we illustrate the analysis of the resilience of a very small supply chain of a decision of companies and customers of the same bank. Let’s assume that for each company the following data is collected on an annual basis. This data is readily available from a retail bank:
- Total Receivables
- Net profit
- Debts v/Banks
- Financial charges payables v/suppliers
- Net Worth
- Shareholders’ Equity/Revenues
- Acid Test
- Cash Flow
- Total Cash Flow / Revenues
- Leverage Current
- Liabilities / Revenues
The data of the companies that make up this supply chain (or that simply belong to a region or a product sector) are arranged as in the example shown below.
Next, the table above is analyzed using the OntoSpace™ management system, which produces three main results:
A measure of supply chain resilience – in this case 75%.
A map that relates the various items (trend data):
This map indicates the most important interdependencies between the various items and indicates which are the dominant ones with regard to the resilience of the entire supply chain. In the example in question, these are:
• Cash Flow / Revenues
• Profit / Revenues
• EBITDA / Revenues
Finally, the OntoSpace™ system produces a ranking of the various items according to their importance and descending order as shown in the following graph:
The graph in question indicates to the Risk Manager (of the bank that has the aforementioned companies as a customer) what are the main problems of its customers. This, in turn, could be useful in order to:
• credit rating
• cost of credit
With the same data it is possible to perform an alternative analysis, in which the impact on the resilience of the supply chain of each individual company is analyzed.
This type of analysis could incorporate hundreds of subjects. An example, relating to the small supply chain in question, is shown below. It is clear how the map indicates the most important interdependencies between a bank’s customers, helping to manage credit risk. For example, it is clear from the map how company 4, which could have a high credit rating, is ‘linked’ to company 10, which, in turn, could be in crisis. The map would indicate a potentially risky situation for the bank that today tends to analyze the credit risk of each corporate client individually and on the basis of their respective balance sheets.
Similar to the previous analysis, we therefore obtain the following results:
• Resilience map. This provides a graphical representation of the interdependencies between the components of the supply chain, as well as a structured view of the resilience and systemic risk of the supply chain (Azienda = Corporation).
• A measure of the resilience of the entire supply chain. Resilience is measured on a scale from 0% to 100%. In this case it is 78%.
• Classification of each company in the supply chain in terms of its contribution to overall resilience. This is shown below (see bar chart).
The analysis indicates that company No. 4 is responsible for 18% of the overall resilience. Companies 13, 12 and 10 each have a resilience footprint of 15% and 14%, respectively. This means that these four companies control 47% of the resilience of the entire supply chain. Evidently, these suppliers should be monitored carefully. This is because the components of a supply chain are interdependent. If a supplier struggles, it could weaken the entire supply chain and even the company he and others serve.
The example in question is related to a tiny supply chain. Today, we are able to process supply chains of tens of thousands of companies, as well as overlapping sets of supply chains. Such analyses can put risk analysis and management in a whole new light and provide new knowledge and insights, of particular interest to a retail bank.