Convergent quantitative Entreprise Risk Management on Divergent Risks

Convergent quantitative Entreprise Risk Management on Divergent Risks

Dec 1st, 2021

Convergent quantitative Enterprise Risk Management on Divergent Risks is an extension of our recent discussion on business interruption risk profiles.

The example we discuss is present in:

Setting the scenario for Convergent quantitative Entreprise Risk Management on Divergent Risks

Suppose a corporation owning and operating three chemical plants sends their products to the shipping terminal as discussed in our earlier blogpost cited above.

The figure below shows the Entreprise Risk Management (ERM) system definition.

Figure: ERM System definition with its elements, namely, the three chemical plants, the logistic network (Road and Railway), the Terminal.

The company is interested in a convergent quantitative risk assessment to allow for risk informed decision-making. This will enable:

Inclusion of divergent scenario with different mitigation strategies

Buffer size as a Mitigation strategy

In the figure below displays selected scenarios identified on the p-C graph with the corporate tolerance. If all had been displayed the graph would look too crowded.

Figure: p_C graph with selected scenarios and corporate tolerance threshold. Horizontal axis: consequences (losses) expressed in M. Vertical axis annual probability. The blue dots are the one that will be the object of risk informed decision making (RIDM). Indeed, they are intolerable, either tactical or strategic risks.

We can convergently look at intolerable risks per macro-element. For instance: each operation, RR and Terminal. We will start with the assumption that no buffer is present at Terminal as in the prior blogpost .

Figure: Intolerable risks per element (operation) assuming no buffer is present at the Terminal.

From the figure above we can see that the RR and Terminal generate a greater intolerable risk portion than the operations. Using the built-in granularity, we can dive in the risk register. As a result we show below the intolerable risk per sub-elements within the operations.

Figure: ERM zoom of the intolerable risks per sub-elements (3 chemical plants (lumped-up), the RR and the terminal). Process, Reactor, Pipes, Tanks and Pumps show the aggregated risk of those elements in the three chemical plants. Unloader, conveyor belts, Buffers, loader are present in the Terminal.

This shows that a buffer in the terminal is necessary at corporate level, since it mitigates the RR risks.

If the company was to implement a four-month buffer the system’s risks would change as shown below.

Convergent quantitative Entreprise Risk Management on Divergent Risks

Figure: Simulation of four-month buffer at the Terminal and its effects on the system’s risks.

The four-month buffer reduces the Railroad intolerable risks by 3 times (27.59M$ vs 89.33M$) and changes the corporate risk prioritization of the entire system.

Divergence – Climate change

The figure below shows that:

  • despite the implementation of four months buffer, climate change divergence still impacts the RR intolerable risks at corporate level (flooding and dams (di+da).
  • As a result, the railroad risk raises at 63.27M$ with (di+da) divergence vs. 27.59M$ without divergence.
  • The implementation of four months buffer at the terminal still significantly reduces the RR risks that, even with divergence remain lower than with no buffer and “as usual” conditions. Finally,
  • as a result, RR risk are comparable to those of the terminal.

Convergent quantitative Entreprise Risk Management on Divergent Risks

Figure: Results of the study in terms of system’s intolerable risks only

Conclusion of Convergent quantitative Entreprise Risk Management on Divergent Risks

As we saw a reduction of resilience and proneness to systemic risks become not only more common and intense but also propagate further due to cascading probabilities and amplification of consequences.

Convergent quantitative Entreprise Risk Management on Divergent Risks adds value to a company, a project or a venture, while giving solid arguments to negotiate with insurers, as it allows for:

  • tactical and strategic planning of corporate operations,
  • balanced, rational and sustainable mitigation and finally
  • help rational insurance coverage

Finally, this type of deployment allows to test “out of the box” mitigative alternatives, including in some cases B2B solutions. Among these we can cite mitigation of potential Force Majeure disputes, for the benefit of all parties involved.

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Category: Consequences, Optimum Risk Estimates, Probabilities, Risk analysis, Risk management, Uncategorized

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