ISO 31010:2011 Risk Assessment Techniques – VI

Popular Techniques

As far as quality and safety are concerned, the topic of risk management and its associated activities should be a key strategy in overall quality planning. Quality engineering is a discipline concerned with control of existing quality levels as well as quality-enhancing strategies. Risk by its very nature is anti-quality so that quality engineering activity is risk-mitigating by nature. In modern industrial enterprises, because the broader concept of risk is very much currently in center stage, it is recommended that quality improvement experts, quality and reliability engineers, and quality managers add knowledge and skills from the increasingly more important risk management arena to their skill set. ISO 31010 is standard describing the various Risk Assessment Techniques globally utilized. The combination and integration of at least 4-5 risk assessment techniques per Risk Assessment cycle, is extremely useful in thinking through preventive as well as reactive measures to respond to risks. When an organization first looks at its potential risk responses it must consider whether there is any ability to control the occurrence of the risk event or not, and if so, the steps that could be taken to reduce the likelihood of a risk occurring. And then alternatively, when there is little ability to control a risk, the focus need to be shifted to the potential impacts of the risk event and what could be done to prepare.

As to ISO 31000:2011 standard, it’s techniques and methodologies of risk assessment are enormous and draws from many sub disciplines, including probability and statistics, quality and reliability theory, operations research, discrete mathematics, simulation modeling, and psychology, among others. The perspectives that one can adopt on risk also vary greatly differs in organizations, depending on the position one has within an organization. There is thus a lot of variation about the concept and use of risk management principles. The ISO 31000:2011 is explaining various methods of risk assessments which was under discussion for last five articles and this is the last article describing latter part of methods given in the ISO 31000:2011 standard. All these explanations are extracted from reputed publicly available data which you can expand and improve yourself while trying to practice them. However, these explanations are not comprehensive and it gives only a simple explanation.

27. FN Curves

Societal risk is defined as the relationship between frequency and the number of people suffering from a specified level of harm in a given population from the realization of specified hazards. Societal risk evaluation is concerned with estimation of the chances of more than one individual being harmed simultaneously by an incident. The likelihood of the primary event (an accident at a major hazard plant) is still a factor, but the consequences are assessed in terms of level of harm and the numbers affected (severity), to provide an idea of the scale of an accident in terms of numbers killed or harmed. Societal risk is dependent on the risks from the substances and processes located on a major hazard installation. A key factor in estimating societal risk is the population around the site, with regards to its location and density. For example, the more (occupied) buildings in any given area, the more people could be harmed by a toxic gas release passing through that area. For an installation with a population located in a specific compass direction, the chance of a toxic gas release would depend on the probability of drift in that direction.

The slope of the societal risk criterion (when plotted on a log-log basis) is equal to -a and represents the degree of aversion to multi-fatality events embodied in the criterion. When the F-N curve slope is equal to -1, the risk criterion is termed ‘risk neutral.’ The FN-curve can be seen as an exceedance curve with a related probability density function (p.d.f.) of the number of deaths. The p.d.f. of the number of deaths Ndij given an accident for activity i at place j can have many forms. A few types are presented here to stimulate further thinking. The first conditional p.d.f. is the Dirac, that limits the outcomes to exactly N fatalities. Other possibilities that allow a larger variation in the outcome, are the exponential and the log-normal p.d.f. The probability of exceedance curves of the number of fatalities, that can be derived from these two forms reflect to some extent the FN-curves found in practical quantitative risk assessment (QRA) studies. A fourth is the inverse quadratic Pareto distribution. The Pareto p.d.f. has no finite standard deviation unless the right tail is truncated.

Analysis of F-N curve to estimate societal risk, various hypothetical events will be assessed. Each of these events will have a predicted frequency of occurrence, f, and a predicted number of persons harmed, N. Consistent with the focus of these Guidelines, N in this discussion will be the predicted number of fatalities associated with the event. The associated frequency of occurrence will be expressed in events per year. Many analysts believe that societal risk results are most effectively presented in graphical form. Such plots are normally log-log plots with the x-axis representing the consequences and the y-axis representing the frequency of occurrence. Log-log scales are used because the range of values for f and N can span multiple orders of magnitude.

Societal risks may be plotted in either of two fashions

1.   Non-cumulative frequency basis - f-N curves are plotted and the value plotted on the y-axis is the discrete frequency of experiencing exactly N fatalities.

2.   Cumulative frequency basis - F-N curves are plotted and the value plotted on the y-axis is the cumulative frequency of experiencing N or more fatalities where societal risk data and criteria are more commonly expressed in terms of cumulative frequency. To construct the F-N curve, a list of all the events (Ex) and their associated frequencies (fx) and consequences (N,) is compiled and sorted by decreasing value of N. 

28. Risk Indices

Risk indices, which summarize risk using numbers or categories such as words, letters, or colors, where these indices are used to communicate risks to the public, understand how risk is changing over time, compare among different risks, and support decision making. Given the different methods to construct risk indices, including flawed methods such as risk matrices.

The impact of the risk event is commonly assessed on a scale of 1 to 5, where 1 and 5 represent the minimum and maximum possible impact of an occurrence of a risk (usually in terms of financial losses). However, the 1 to 5 scale can be arbitrary and need not be on a linear scale.  Nonetheless, the probability of occurrence is likewise commonly assessed on a scale from 1 to 5, where 1 represents a very low probability of the risk event actually occurring while 5 represents a very high probability of occurrence. This axis may be expressed in either mathematical terms (event occurs once a year, once in ten years, once in 100 years etc.) or may be expressed in common terms (event has occurred here very often; event has been known to occur here; event has been known to occur in the industry etc.). Again, the 1 to 5 scale can be arbitrary or non-linear depending on decisions by subject-matter experts. The composite risk index thus can take values ranging (typically) from 1 through 25, and this range is usually arbitrarily divided into three sub-ranges. The overall risk assessment is then Low, Medium or High, depending on the sub-range containing the calculated value of the Composite Index. For instance, the three sub-ranges could be defined as 1 to 8, 9 to 16 and 17 to 25.

29. Consequence/Probability Matrix

The probability matrix which is called stochastic matrix is a square matrix used to describe the transitions of a Markov chain where it’s all entries are nonnegative real numbers representing a probability. Risk assessment basically involves the calculation of the magnitude of potential consequences (levels of impacts) and the likelihood (levels of probability) of these consequences to occur. 

Risk = Consequence x Likelihood; where: (i) Likelihood is the Probability of occurrence of an impact that affects the environment; and, (ii) Consequence is the Environmental impact if an event occurs.

The C × L matrix method therefore combines the scores from the qualitative or semi-quantitative ratings of consequence (levels of impact) and the likelihood (levels of probability) that a specific consequence will occur (not just any consequence) to generate a risk score and risk rating. Essentially, the higher the probability of a "worse" effect occurring, the greater the level of risk.

This C x L risk assessment process involves selecting the most appropriate combination of consequence and likelihood levels that fit the situation for a particular objective based upon the information available and the collective knowledge of the group (including stakeholders, academics, managers, industry, researchers and technical staff) involved in the assessment process. 

30. Cost-Benefit Analysis

As its name suggests, Cost-Benefit Analysis involves adding up the benefits of a course of action, and then comparing these with the costs associated with it. The results of the analysis are often expressed as a payback period – this is the time it takes for benefits to repay costs. Many people who use it look for payback in less than a specific period – for example, three years. Jules Dupuit, a French engineer and economist, introduced the concepts behind CBA in the 1840s. It became popular in the 1950s as a simple way of weighing up project costs and benefits, to determine whether to go ahead with a project. You can use the technique in a wide variety of situations. For example, when you are:

Deciding whether to hire new team members.

Evaluating a new project or change initiative.

Determining the feasibility of a capital purchase.

However, bear in mind that it is best for making quick and simple financial decisions. More robust approaches are commonly used for more complex, business-critical or high cost decisions.

Steps of a CBA

Step One: Brainstorm Costs and Benefits

First, take time to brainstorm all the costs associated with the project, and make a list of these. Then, do the same for all the benefits of the project. Can you think of any unexpected costs? And are there benefits that you may not initially have anticipated? When you come up with the costs and benefits, think about the lifetime of the project. What are the costs and benefits likely to be over time?

Step Two: Assign a Monetary Value to the Costs

Costs include the costs of physical resources needed, as well as the cost of the human effort involved in all phases of a project. Costs are often relatively easy to estimate (compared with revenues). It's important that you think about as many related costs as you can. For example, what will any training cost? Will there be a decrease in productivity while people are learning a new system or technology, and how much will this cost? Remember to think about costs that will continue to be incurred once the project is finished. For example, consider whether you will need additional staff, if your team will need ongoing training, or if you'll have increased overheads.

Step Three: Assign a Monetary Value to the Benefits

This step is less straightforward than step two, thus firstly, it's often very difficult to predict revenues accurately, especially for new products. Secondly, along with the financial benefits that you anticipate, there are often intangible, or soft, benefits that are important outcomes of the project. For instance, what is the impact on the environment, employee satisfaction, or health and safety? What is the monetary value of that impact? Here, it's important to consult with other stakeholders and decide how you'll value the intangible items.

Step Four: Compare Costs and Benefits

Finally, compare the value of your costs to the value of your benefits, and use this analysis to decide your course of action. To do this, calculate your total costs and your total benefits, and compare the two values to determine whether your benefits outweigh your costs. At this stage, it's important to consider the payback time, to find out how long it will take for you to reach the breakeven point – the point in time at which the benefits have just repaid the costs. For simple examples, where the same benefits are received each period, you can calculate the payback period by dividing the projected total cost of the project by the projected total revenues: Total cost / total revenue (or benefits) = length of time (payback period).

CBA adds up the total costs of a programme or activity and compares it against its total benefits. The technique assumes that a monetary value can be placed on all the costs and benefits of a programme, including tangible and intangible returns to other people and organizations in addition to those immediately impacted. As such, a major advantage of cost-benefit analysis lies in forcing people to explicitly and systematically consider the various factors which should influence strategic choice.

Decisions are made through CBA by comparing the net present value (NPV) of the programme or project’s costs with the net present value of its benefits. Decisions are based on whether there is a net benefit or cost to the approach, i.e. total benefits less total costs. Costs and benefits that occur in the future have less weight attached to them in a cost-benefit analysis. To account for this, it is necessary to ‘discount’ or reduce the value of future costs or benefits to place them on a par with costs and benefits incurred today. The ‘discount rate’ will vary depending on the sector or industry, but public sector activity generally uses a discount rate of 5-6%. The sum of the discounted benefits of an option minus the sum of the discounted costs, all discounted to the same base date, is the ‘net present value’ of the option.

31. Multi-Criteria Decision Analysis (MCDA)

Multi-Criteria Decision Analysis (MCDA) is a general framework for supporting complex decision-making situations with multiple and often conflicting objectives that stakeholders groups and/or decision-makers value differently. A typical example of a decision-making situation assisted by MCDA methods is determination of an appropriate water regulation policy, which has a variety of economic, ecological and social consequences regarded as desirable by some stakeholders (e.g. downstream farmers) and undesirable by others (e.g. recreational fishermen). MCDA is an “umbrella term to describe a collection of formal approaches which seek to take explicit account of multiple criteria in helping individuals or groups explore decisions that matter”. It is rooted in operational research and support for single decision-makers. Recently the emphasis has shifted towards multi-stakeholder processes to structure decision alternatives and their consequences, to facilitate dialogue on the relative merits of alternative courses of action, thereby enhancing procedural quality in the decision-making process.

The basic idea of MCDA methods is to evaluate the performance of alternative courses of action (e.g. management or policy options) with respect to criteria that capture the key dimensions of the decision-making problem (e.g. ecological, economic and social sustainability), involving human judgment and preferences.

MCDA problems are comprised of five components:

1. Goal

2. Decision maker or group of decision makers with opinions (preferences)

3. Decision alternatives

4. Evaluation criteria (interests)

5. Outcomes or consequences associated with alternative/interest combination

Multi-Criteria Decision Analysis, or MCDA, is a valuable tool that we can apply to many complex decisions.  It is most applicable to solving problems that are characterized as a choice among alternatives. It has all the characteristics of a useful decision support tool: It helps us focus on what is important, is logical and consistent, and is easy to use.  At its core MCDA is useful for:

Dividing the decision into smaller, more understandable parts

Analyzing each part

Integrating the parts to produce a meaningful solution

When used for group decision making, MCDA helps groups talk about their decision opportunity (the problem to be solved) in a way that allows them to consider the values that each view as important. It also provides a unique ability for people to consider and talk about complex trade-offs among alternatives.  In effect, it helps people think, re-think, query, adjust, decide, rethink some more, test, adjust, and finally decide.