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Leadership boosting competitiveness with data driven decision making

Leadership boosting competitiveness with data driven decision making

Mar 6th, 2019

Leadership boosting competitiveness with data driven decision making occurs when risk assessment databases, risk data are well structured and ready to advanced analyses.

Leadership boosting competitiveness with data driven decision making

It is then possible to carry out risk triage, bundling and develop mitigation road-maps. We have discussed at length in earlier blogposts the steps necessary to develop a quantitative, convergent, updatable risk assessment.

How to start risk triage, bundling and develop mitigation road-maps

To perform a solid quantitative risk assessment you should have in your hand:

a) a probability-consequences graph and

b) an explicit tolerance function.

The graph below shows the probability-consequences graph and the risk tolerance curve. Probabilities are annual probabilities.

Their high-low bounds are respectively:

The graph can use monetary units, or any metric selected by the client/analyst to express consequences, losses.

The risk tolerance threshold is a line, or a band, expressing uncertainty on its definition. It describes which losses you are ready to tolerate with their respective probability.

Risk are “points” in the graph. Actually the points should show-up as “bubbles”, i.e. include the uncertainties. In the graph below, to avoid overcrowding we are only showing the “points”.

Also, from the graph below, we can immediately visualize:

  • tolerable (green)/intolerable (yellow and red) risks,

  • tactical (intolerable but manageable, i.e. the yellow ones) and

  • strategic (intolerable and unmanageable, I.e the red ones) risks.

That constitutes a first triage of risks. You can also plan a bundling of risks, aiming for example to create a well balanced captive insurance portfolio, using the same graph.

An example of raw risk assessment results

The graph covers an example developed for an agri-business company. NB. We have altered numbers and system elements to preserve confidentiality.

The difference between tactical (intolerable but manageable, i.e. the yellow ones) and strategic (intolerable and unmanageable, i.e the red ones) risks lies in a simple characteristic.

  • If mitigative investments can lower the probability of a risk below tolerance before the credibility limit (10-6, i.e. the bottom of the graph) is reached, then the risk is a tactical one.

  • Strategic risks cannot be reduced below tolerance before reaching the credibility threshold. Hence they require a system change, aka strategic shift.

This information is paramount to assessing at screening level mitigation road-maps, discuss at screening level their feasibility and sustainability, in other words develop tactical and strategic planning.

This risk assessment is a convergent quantitative one. Convergent means that all hazards potentially present, i.e. technical, man-made, natural, etc. are considered simultaneously in the aggregate risk evaluations. It is paramount for a ERM deployment to be convergent, in order to avoid self-blinding results.

Convergence requires accounting for all detectable hazards and multidimensional consequences at each location (element) of the system.

Leadership boosting competitiveness with data driven decision making

At this point the fun really starts.

Raw data are evaluated and aggregated, then displayed in easy to read, customizable graphs allowing detailed Risk Informed Decision Making (RIDM).

We can compute the intolerable part for each risk and compare them among themselves. For example transportation risks in Can, US for various modes.

Leadership boosting competitiveness with data driven decision making

Then we can aggregate the transportation risks in Can, US.

Leadership boosting competitiveness with data driven decision making

Leadership boosting competitiveness with data driven decision making may require, for example, a quantification of resources that need to be spent per issue.

In a ORE ERM deployment we can of course query this and easily suggest an optimized resources allocation.

Management may also require to see the risks for all their products (named A-E) at each corporate operation locations.

This view allows management to understand that US Trucks is a significant, quantified source of intolerable risks for three out of five products, and seek way to mitigate it.

In a ORE ERM deployment we can drill the data to understand more precisely how the risks affect operations.

Leadership boosting competitiveness with data driven decision making

Here we see the risks partition per hazardous sector for product E at Transport Hub 1

The source of hazard is very interesting when looking at possible risk transfers mechanisms, such as insurance. We can also look at the same data but order them by type of consequences.

Risks partition per type of consequence for product E at Transport Hub 1

Further benefits

The graph above clearly depicts the reputational part of the intolerable risk at a given location, for a specific product of the client. That information is paramount to maintain a Social License to Operate (SLO) or help understand the buffer needed to maintain a interdependent business interruption low.

Environmental, Health and Safety losses are very important to foster and maintain a solid Corporate Social Responsibility level.

The corporate dashboard allows a bottom-up aggregation of risks delivering at each corporate echelon, on a need-to-know basis, the risk information required for decision-making support.

Category: Consequences, Hazard, Mitigations, Optimum Risk Estimates, Risk analysis, Risk management

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