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.
