Quote from sachinm on 7 October 2023, 9:24 am

The potential complexity, interconnectivity and enormity risks of E, S, and G, can be crippling when trying to develop an effective risk response strategy.

So, where do we start? Most organisations have responded by Balance Scorecard reporting...

Banks, Insurers and Asset Managers have been reporting Environmental, Social and Governance (ESG) metrics for several years, whether Task Force on Climate-Related Financial Disclosures (TCFD) under listing rules, voluntarily under frameworks such as Global Reporting Initiative (GRI), the standards published by the Sustainability Accounting Standards Board (SASB) or EU disclosure requirements like Sustainable Finance Disclosure Regulation (SFDR).

Separately, many industrial organisations voluntarily declare their ESG performance in their annual reporting and set out specific areas of action within the fields of:

• Water Resources
• Land Use
• Waste Management
• Pollution Prevention
• Energy Management
• Sustainable Procurement
• Travel Planning
• Global Impact Contributions
• Diversity, Equity, and Inclusion

But does this ESG scorecard reporting allow for proactive operational measures to be taken on-the-ground, and allow for transparency in the complex interactions between operational site decisions, and the consequential ESG impact.

Take the following simple scenario: Delay in delivery of wet concrete from production site to building site can lead to a reduction in its quality as delay can reduce its suitability and lead to waste, and site inefficiencies.

Could this simple scenario, and consequential ESG impact, be managed by your current risk management processes? What could help?...

1. Use of cognitive, real-time digital twin technology on how concrete is deployed during construction.
2. Use of digital technology to monitor and improve the operating efficiency of worksite plant and machinery, to identify where assets are working below capacity to cut emissions, accelerate works programmes and reduce costs.
3. Create digital site diaries and reporting software tailored to the construction industry to minimise the use of manual site diaries to remove communication lags between site and office, as well as reducing the use of paper.

How else have you closed the gap between ESG reporting, and operational decisions on-the-ground, to allow real-time feedback to correct cognitive bias and provide feedback on the consequential impact (positive/negative) of decisions being made?

Quote from sachinm on 11 November 2023, 4:09 pm

Companies can build upon existing risk identification methodology by using the Global Risk Assessment and Strategic Planning (GRASP) and the Task Force on Climate-related Financial Disclosures (TCFD) framework for reporting climate related information, by assessing the associated threats to Strategic
Outcomes. This can be used to make an organisation’s disclosure of how it's strategies might change to address potential climate-related risks to better understand the potential implications of climate change on the organisation.

The Task Force on Climate-related Financial Disclosures (TCFD) are structured around four thematic areas that represent core elements of how organisations operate: governance, strategy, risk management, and metrics and targets.

A key recommendation of Task Force on Climate-related Financial Disclosures (TCFD) is the use of scenarios in assessing climate related issues and their potential financial implications, and to include this forward-looking information in financial filings.

Quote from sachinm on 26 November 2023, 8:31 pm

A great challenge for Risk Managers in managing E, S and G risks is their interconnectedness and interdependence. Think about the example of the Nova Kakhovka dam collapse in 2023 during the Russian and Ukraine conflict, which led to massive subsequent Societal risks as downstream towns became flooded. Indeed, within the Russian and Ukraine conflict, we have seen cyber attacks co-ordinated with missile attacks in order to enhance the feeling of 'chaos' such as the Ukraine power grid attack (2023), where Sandworm Hackers caused another Blackout in Ukraine during a missile strike.

So, how can Risk Managers assess this degree of interconnectedness, and causality.

In mathematics, fuzzy sets (a.k.a. uncertain sets) are sets whose elements have degrees of membership. Fuzzy sets were introduced independently by Lotfi A. Zadeh in 1965 as an extension of the classical notion of set.

Fuzzy logic is a form of logic based on the concept of a fuzzy set. Membership in fuzzy sets is expressed in degrees of truth—i.e., as a continuum of values ranging from 0 to 1. In a narrow sense, the term fuzzy logic refers to a system of approximate reasoning, but its widest meaning is usually identified with a mathematical theory of classes with unclear, or “fuzzy,” boundaries. For example, at age 45 a man is neither very young nor very old. 

Qualitative Comparative Analysis (QCA) embraces causal complexity is considered as one of the main advantages of QCA. Causal asymmetry suggests that although the presence of certain conditions may lead to a certain outcome, their absence does not necessarily imply outcome absence. Conjunctural causation suggests that a condition can influence the outcome, only if it is combined with other conditions. In other words, there are combinations of conditions (configurations) that lead to any given outcome and therefore, QCA allows the examination of combinatorial effects, rather than net effects, as is the case with conventional correlational methods.

Ragin, C. C., & Pennings, P. (2005). Fuzzy Sets and Social Research. Sociological Methods & Research, 33(4), 423-430. https://doi.org/10.1177/0049124105274499

The analysis begins with listing and counting all the combinations of variables observed in the data set, followed by applying the rules of logical inference to determine which descriptive inferences or implications the data supports.

In the case of categorical variables, QCA begins by listing and counting all types of cases which occur, where each type of case is defined by its unique combination of values of its independent and dependent variables:

• For instance, if there were four categorical variables of interest, {A,B,C,D}, and A and B were dichotomous (could take on two values), C could take on five values, and D could take on three, then there would be 60 possible types of observations determined by the possible combinations of variables, not all of which would necessarily occur in real life.
• By counting the number of observations that exist for each of the 60 unique combination of variables, QCA can determine which descriptive inferences or implications are empirically supported by a data set. Thus, the input to QCA is a data set of any size, from small-N to large-N, and the output of QCA is a set of descriptive inferences or implications the data supports.
• In QCA's next step, inferential logic or Boolean algebra is used to simplify or reduce the number of inferences to the minimum set of inferences supported by the data. This reduced set of inferences is termed the "prime implicates" by QCA adherents.
• For instance, if the presence of conditions A and B is always associated with the presence of a particular value of D, regardless of the observed value of C, then the value that C takes is irrelevant.
• Thus, all five inferences involving A and B and any of the five values of C may be replaced by the single descriptive inference "(A and B) implies the particular value of D".