4
Risk Management Tools and Techniques
4.1 INTRODUCTION
The management of risk is currently one of the main areas of interest for researchers and practitioners working in a wide range of projects because of the benefits of the process. Risk management is one of the key project management processes. Numerous techniques are available to support the various levels of the risk management process.
Risk management is a tool which is increasingly used in organisations and by public bodies to increase safety and reliability and to minimise losses. It involves the identification, evaluation and control of risks. Implicit in the process is the need for sound decision making on the nature of the potential socio-technical systems and their predicted reliability. The need for safety measures and guidance as to where they should be displayed are, in theory, the natural products of combined probabilistic risk assessment/human reliability analysis (PRA/HRA) studies. In an ideal world, good assessment should always drive effective error reduction.
This chapter describes the tools and techniques used in the assessment of risk, both qualitative and quantitative, and country risks which are often considered a major factor in risk assessment. The tools and techniques described can be used at corporate, strategic business and project levels.
4.2 DEFINITIONS
French and Saward (1983) describe a tool as any device or instrument, either manual or mechanical, which is used to perform work.
Distinguishing between a tool and technique is difficult. For the purpose of this book the present authors define tools as:
The methodology which employs numerous techniques to achieve its aim.
For example, risk management (tool) employs numerous techniques such as sensitivity analysis, probability analysis and decision trees. Value management (tool) employs such techniques as functional analysis, optioneering and criteria weighting.
4.3 RISK ANALYSIS TECHNIQUES
There are two main categories of risk analysis techniques: qualitative and quantitative. Qualitative methods seek to compare the relative significance of risks facing a project in terms of the effect of their occurrence on the project outcome. Simon et al. (1997) suggest that the information obtained from qualitative analysis is nearly always more valuable than that from quantitative analysis and that the latter is not always necessary. Thompson and Perry (1992) recommend qualitative analysis for developing an initial risk assessment.
Quantitative techniques attempt to determine absolute value ranges together with probability distributions for the business or project outcome and, consequently, involve more sophisticated analysis, often aided by the use of computers. According to Simon et al. (1997), to achieve this, a model is created of the project under consideration. It is then modified to quantify the impacts of specific risks determined by an initial assessment using qualitative techniques. The model will include all the elements which are relevant to the risk analysis and, against these elements, uncertain variables can be entered (rather than fixed values) to reflect areas of significant uncertainty.
4.3.1 Choice of Technique(s)
According to Norris (1992) and Simon et al. (1997) in determining which of the available analysis techniques is most suitable for application to a particular investment, management should consider:
• the availability of resources for analysis – human, computational and time
• the experience of the analysts with the different techniques
• the size and complexity of the project
• the project phase in which the analysis takes place
• the available information
• the purpose of the analysis.
In any analysis or assessment where data are required then the data should be considered as follows:
• Accuracy: are data accurate?
• Adequacy: are they adequate for the purpose of project?
• Relevancy: are they relevant to the subject?
• Coherence: has the information been classified in an orderly and meaningful way?
• Impartiality: has the analyst remained unbiased?
• Direction: does the analytical procedure lead to conclusions/ decisions?
• Logicality: is the reasoning sound?
• Validity: are comparisons, interpretations and implications valid?
The following provides a brief overview of some of the analysis techniques in use.
4.4 QUALITATIVE TECHNIQUES IN RISK MANAGEMENT
4.4.1 Brainstorming
Originating in Madison Avenue in the 1950s, brainstorming was long considered the preserve of those wild and wacky folk in advertising. In more recent years, however, it has spread into the mainstream and is now used by businesses of all kinds, not to mention civil servants, engineers, project managers and scientists or, indeed, anyone with a problem to solve.
The optimum size for a brainstorming session is 12 people and the ideal length of time is between 15 and 45 minutes, though sessions can last all day (Sunday Times 2001). The basic rules can be summarised as:
• imposition of a time limit
• a clear statement of the problem at hand
• a method of capturing the ideas, such as a flipchart
• somewhere visible to leave the ideas and let them incubate
• adoption of the principle that no idea is a bad idea
• suspension of judgement
• encouragement of participants to let go of their normal inhibitions and let themselves dream and drift around the problem
• encouraging quantity rather than quality (evaluation can come later)
• cross-fertilisation by picking up group ideas and developing them.
Chapman (1998) states that ‘the brainstorming process, borrowed from business management and not specifically created for risk management, involves redefining the problem, generating ideas, finding possible solutions, developing selected feasible solutions and conducting evaluation’. However, Bowman and Ash (1987) believe there is a tendency for groups to make riskier decisions than individuals because of factors such as dispersed responsibility, where influential members of the groups have more extreme views and moderate members remain silent.
4.4.2 Assumptions Analysis
Assumptions analysis is an intuitive technique and is where assumptions typically made in project planning are identified. They are then assessed as to what impact their proving false will have on the project outcome. Assumptions to which the outcome is seen to be sensitive and which have a likelihood of proving false will form the basis of a list of risks (Simon et al. 1997). However, there is a danger that not all assumptions will be identified since a large number of them will be implicit.
4.4.3 Delphi
This is a technique for predicting a future event or outcome, in which a group of experts are asked to make their forecasts, initially independently, and subsequently by consensus in order to discard any extreme views. In some circumstances subjective probabilities can be assigned to the possible future outcomes in order to arrive at a conclusion.
Delphi is an intuitive technique and was developed at the RAND Corporation for technical forecasting. Merna (2002) stated that the technique involves obtaining group consensus by the following process:
• Respondents are asked to give their opinion on the risks pertaining to a project or investment.
• A chairperson then collates the information and issues a summary of the findings to the respondents requesting that they revise their opinion in light of the group’s collective opinion.
• These steps are then repeated until either consensus is reached or the chairperson feels that no benefit will result from further repetitions.
The respondents are isolated from one another to avoid conflict and interact only with the chairperson. The Delphi process tends to take place through either the postal service or electronic interactive media.
Chapman (1998) cites that benefits from the Delphi Technique include that participants are free from group pressures and pressures of conformity, personality characteristics, and compatibility are avoided.
4.4.4 Interviews
This intuitive technique is used where information requirements need to be more detailed than a group can provide, or where group work is impractical. Interviews provide a means of soliciting information from individuals. Often corporate-level personnel will request interviews with project personnel to elicit information regarding potential risks at the project level which may affect the commercial viability of the project and thus affect the financial stability of the SBU undertaking it.
4.4.5 Hazard and Operability Studies (HAZOP)
‘HAZOP’ is an inductive technique and was developed by Imperial Chemicals Ltd for risk identification in chemical process plants. It is a type of structured brainstorming whereby a group systematically examine the elements of a process and define the intention of each (Ansell and Wharton 1995). Frosdick (1997) cites guidewords such as ‘not’, ‘more’ and ‘less’ to be used to identify possible deviations from the intention. Such deviations can then be investigated to eliminate their causes as far as possible and minimise the impact of their consequences.
The HAZOP approach is flexible and can be used to identify potential hazards in facilities of all kinds at all stages of their design and development. Alternatively, a review of contingency plans at an existing facility could be more comprehensively informed by a HAZOP exercise, which could identify hazards not previously planned for.
4.4.6 Failure Modes and Effects Criticality Analysis (FMECA)
FMECA is an inductive technique and undertaken by a single analyst with a thorough knowledge of the system under investigation. This technique may focus either on the hardware involved, with a concentration on potential equipment failures, or on events, with an emphasis on their outputs and the effect of their failure on the system. Every component of the system is considered and each mode of failure identified. The effects of such failure on the overall system are then determined (Frosdick 1997, Ansell and Wharton 1995). This technique uses a type of weighted score to identify areas of a project most at risk of failure. In a routine situation FMECA is generally used at strategic business and project levels, it highlights areas of concern and it effectively points resources towards the perceived problem areas. The technique is often used for auditing company hardware (computer) and equipment.
4.4.7 Checklists
Checklists are deductive techniques derived from the risks encountered previously and provide a convenient means for management to rapidly identify possible risks. They take the form of either a series of questions or a list of topics to be considered. Organisations may generate checklists for themselves or make use of standard checklists available for their particular industry or sector.
4.4.8 Prompt Lists
These are deductive techniques and classify risks into type or area groups, for example financial, technical and environmental, or the task groups with which they are associated, for example design, construction and commissioning. They may be general, industry or project specific.
4.4.9 Risk Registers
A risk register is a document or database which records each risk pertaining to a project or particular investment or asset. As an identification aid, risk registers from previous, similar projects may be used in much the same way as checklists.
The risk register enables the data collected during the risk management identification process to be captured and saved, for review and as a data container for information on the choice of risk software. There are a number of ‘prerequisite’ data items necessary within the risk register, as follows:
• The title of the project. This should briefly describe the project.
• The project ID. This allows identification of specific projects where multiple projects are being developed.
• The activity ID.
• The activity acronym.
• The team leader’s name, and the names of the individual teams. This information is necessary should any further investigation be needed or any queries in regard to the original risk assessment be raised.
• Activities. This column is a list of activity descriptions, preferably in order of sequence. The register may be used for network or spreadsheet models.
• Procedure. This is important for network-based risk software packages. It identifies the linkage between the activities from start to finish.
• Most likely. Estimated by the expert for the activities, this is a value used in the risk software package around which the optimistic and pessimistic values operate. This is commonly referred to as a threepoint estimate.
Figure 4.1 Typical summary of a risk register output
Figure 4.1 illustrates a template for the summary of a risk register output that can be used at corporate, strategic business or project levels.
Risk measure charts can be developed from the risk register. The goal of a risk measure chart is not to solve the risks, but to assign tasks to the responsible party. For example:
• scenario – change in government
• action – foster political neutrality; predict scope or contract changes by new officials.
From these tasks, the responsible party can in turn perform risk analyses in further detail.
4.4.10 Risk Mapping
This involves the graphical representation of risks on a two-dimensional graph where one axis relates to the potential severity of a risk eventuating and the other to the probability of it doing so (Figure 4.2). Risks are considered in turn and plotted on the graph. Iso-risk curves drawn on the graph connecting equivalent risk with differing probability/severity serve to guide the analysts in determining the relative importance of the risks which they plot (Al-Bahar and Crandell 1990).
Figure 4.2 Risk mapping concept
4.4.11 Probability-Impact Tables
Probability-Impact (P-I) tables are used to assess the relative importance of risks. As with risk mapping, the probability of occurrence and the potential impact of a risk is determined by selecting from a range of low/medium/high, for example. The numerical meaning of each of the scale points should be predetermined for the project and investment.
P-I scores are then derived for each risk by multiplying their probability scores by their impact scores, allowing direct comparison of the risks – the higher the P-I score, the greater the severity of the risk (Simon et al. 1997). An example of P-I tables is shown in Figure 4.3. Probability impact grids will be discussed later in this chapter.
4.4.12 Risk Matrix Chart
The risk matrix chart is often used to segregate high-impact risks from low-impact risks. Figure 4.4 illustrates how the risk matrix chart partly qualifies the probability and impact of a risk, and is often used in risk management workshops where risks are identified and then assessed in terms of their impact and probability. For example, the risk of employees being late for work would be classed as a kitten since little attention is needed because employees finish their work in their own time. Rain in Manchester is highly probable but has little impact on construction work since operatives are trained to take specific measures to deal with such events. This would be classed as a puppy. Flooding of business premises could have a low probability due to its location but should flooding occur it would have a major impact on the businesse’s profits. This alligator is managed by ensuring that flood protection is in place or by storing finished goods in a water tight structure. In the drug development phase of a pharmaceutical product the side effects of ‘first in man’ tests are highly probable and may have a high impact. This tiger is often mitigated by keeping the tests down to a small sample and by ensuring volunteers are insured against long-term effects.
Figure 4.3 Probability-impact tables (Adapted from Allen 1995)
Figure 4.4 Risk matrix chart
Typically the tigers and alligators are mitigated before the puppies and kittens.
4.4.13 Project Risk Management Road Mapping
Table 4. illustrates the overall processes and applications that may be considered in the choice of a risk management system.
Each category of the road map in the table presents, firstly, the simplest techniques, followed by gradually increasing levels of work and complexity. It is important to focus on the added value which is provided by the subsequent level when you are trying to identify the appropriate level for a particular situation.
Many of such qualitative analysis methods are used at corporate and SBU levels in the early stage of project definition when little detailed information is available.
4.5 QUANTITATIVE TECHNIQUES IN RISK MANAGEMENT
Quantitative techniques are used when the likelihood of the investment or project achieving its objectives within time and budget is required – typically for budget authorisation or presentation of the project’s status to the board of directors.
It should be borne in mind that the output from quantitative analysis is only as good as the input information, so adequate time should be allowed for its collection and validation.
4.5.1 Decision Trees
Management are often faced with multiple choices, which in turn are faced with many options. In many cases management only have the resources to opt for one, which presents management with the problem of opportunity cost. However, deciding to adopt an option can be difficult and a useful technique to assess options is the decision tree. This technique explores various investment options available to the decision-maker under risk and uncertainty which are graphically represented in the form of sequential decisions and probability events (Merrett and Sykes 1983).
Table 4.1 Risk management (RM) road map
PMBOK (1996) describes decision trees as diagrams that depict key interactions between decisions and associated chance events as they are understood by the decision-maker. Decision trees show a sequence of interrelated decisions and the expected outcomes under each possible set of circumstances. Where probabilities and values of potential outcomes are known, they are used as a method of quantification which aids the decision-making process.
The aim of the decision tree is to produce an expected value for each option which is the sum of the probabilities and their weighted values. The diagram begins with a decision node at the top of the sheet and consequential chance events and decisions are drawn sequentially as the decision-making process proceeds from top to bottom. Decisions are depicted as square nodes. These are linked by labelled straight lines or ‘branches’ which denote either decision actions if they stem from decision nodes or alternative outcomes if they stem from chance event nodes (Hertz and Thomas 1983, 1984, Gregory 1997).
Figure 4.5 illustrates a typical decision tree. The example forecasts possible outcomes from opening or not opening a new factory. The example takes account of competitor reaction and the state of the economy, and the decision of whether to go ahead or not is expressed statistically as return on capital employed (ROCE).
According to Thompson and Perry (1992), this technique can help clarify and communicate a sequence of choices and decisions. The technique has been used in industry to decide methods of construction, contractual problems and investment decisions. In theory the technique could be used in any situation where there is an option, or opportunity cost, and a decision is needed.
4.5.2 Controlled Interval and Memory Technique
The controlled interval and memory (CIM) model provides a mathematical means of combining probability distributions for individual risks.
Figure 4.5 Typical decision tree (Adapted from Marshell 2000)
According to Simon et al. (1997) this technique has largely been superseded by simulation techniques and is not widely used.
4.5.3 Monte Carlo Simulation
This technique derives its name from its association with chance or uncertain situations and its use of random numbers to simulate their consequences. Simulation is an art and science of designing a model which behaves in the same way as a real system. The model is used to determine how the system reacts to different inputs. Four important steps are required as follows:
1. Assign a probability distribution to each variable which affects the IRR/NPV (see below).
2. Assign the range of variation for each variable.
3. Select a value for each variable within its specific range. This is done in such a way that the frequency with which any value is selected corresponds to its probability in the distribution.
4. Carry out a deterministic analysis with the input values selected from their specified distributions in random combinations. Each time a new value is generated for each variable, a new combination is obtained – hence a new deterministic analysis is done. This is repeated a number of times to obtain a result. The number of combinations of probability distributions required is usually between 200 and 1000. The greater number of iterations used will result in increased accuracy. The diagrammatic output of a Monte Carlo simulation in the form of a cumulative probability distribution diagram is shown in Figure 4.7. A brief assessment of the strengths and weaknesses of Monte Carlo simulations is shown in Table 4.2.
Table 4.2 Monte Carlo simulation strengths and weaknesses
| Strength | Weakness |
|---|---|
| Stochastic – easier to compute for multiple inputs | Probability distributions are assumed based in part on previous experience |
| Allows a probability distribution to be used avoiding single point estimations | Risk profiles are often underestimated, due to excluding the tails of the distributions |
| Provides a more representative prediction of risk, provided initial assumptions are reasonable | Most Monte Carlo packages, with the exception of the high end ones, do not allow for interdependence of input variables |
| Relatively fast with modern computing technology, brute force approach to calculation | Use of historical data can propagate previous erroneous assumptions |
| Subjective judgement is typically used to come up with starting points | |
| Can become too complex and unwieldy |
4.5.4 Sensitivity Analysis
In any project or investment, the data used at the planning stage are bound to vary and are therefore subject to risk. Sensitivity analysis is used to produce more realistic values, supported by a range of possible alternatives that reflect any uncertainty and provide some means of validity of the assumptions. Sensitivity analysis is carried out to identify the most sensitive variables affecting the project’s estimated worth, usually in terms of net present value (NPV) or internal rate of return (IRR) (Norris 1992).
Sensitivity analysis is used to determine the effect on the whole project of changing one of its risk variables. The technique aims to identify the risks which have a potentially high impact on the cost or timescale of the project.
A major advantage of sensitivity analysis is that it shows the robustness and ranking of alternative projects. It identifies the point at which a given variation in the expected value of a cost parameter changes a decision. Then, the range of change for each variable is defined and a picture of the possible range of minimum and maximum effects on the project’s outcome is gradually determined as each of the important risks is investigated. The weakness of the method is that risks are considered independently and without their probability of occurrence.
There are several ways in which the results of a sensitivity analysis can be presented. Most practitioners tend to present the data in either a tabular or diagrammatic form. However, if several variables are changed, a graphical representation of the results is most useful; this quickly illustrates the most sensitive or critical variables. Norris (1992) and Skoulaxenou (1994) state that a ‘spider diagram’ of percentage change in variables versus percentage change in outcome value is the most popular means of expressing the results.
Sensitivity analysis is usually adequate and effective for projects during the appraisal process when comparing options and for preliminary approval, where only a limited number of identified risks are assessed.
Figure 4.6 illustrates the sensitivity analysis of a project’s economic parameters; these are cash lock-up (CLU), payback (PB) and net present value (NPV) in relation to the internal rate of return (IRR). Although Figure 4.6 is generated on the basis of economic data, sensitivity diagrams can also be used at both corporate and SBU levels. For example, a sensitivity diagram may be used at the corporate level to show the sensitivity of a number of SBUs when considered against specific risks occurring, such as demand and market changes.
Figure 4.6 Typical sensitivity analysis diagram
Similarly SBUs can use a spider diagram to show the effects of risk, say delay, to a number of projects in its portfolio. Sensitivity is normally considered in terms of change to IRR, NPV and time.
Figure 4.7 represents the uncertainty in a project in terms of IRR. In this example the project has a 40% chance of the IRR being less than 7.5% and a 60% chance of it being greater than 7.5%. Similarly the project has an 80% chance of the IRR being less than 10% and a 20% chance of it being greater than 10%, with a 50% chance of it being less than or greater than 8%.
Figure 4.7 Cumulative probability distribution
As with sensitivity analysis, cumulative distribution curves can be used to illustrate the probability of both SBUs and a portfolio of projects. It is important to note that the steeper the curve, the less the uncertainty in the investment, since the range of possibilities for values of IIR, in this case, is more certain.
4.5.5 Probability-Impact Grid Analysis
When the impact parameters for a risk (cost, programme, performance) have been established, a broad-band rating system may be used to rank the risk based on the probability-impact grid (PIG) method (Kolluru et al. 1996). The ranges of the impact bands are often determined at SBU and project levels and defined in the risk management plan (RMP).
The ‘most likely values’ for cost and programme gathered during the identification phase are applied to the band ranges in determining the level of impact, for instance low, medium and high. An example of a weighted factor can be seen in Table 4.3. The weighting of the impact scale serves to focus the risk response on high-impact risks with less weighting being given to probability. The P-I score can be determined by multiplying the impact scores (Table 4.3) and the probability scores (see Figure 4.8).
A threshold for the P-I score may be set in a resulting matrix as shown in Figure 4.8. In this case a 5 by 5 matrix is shown. A 3 by 3 matrix is, however, more commonly used.
The cost and programme impacts may fall into different levels of severity for any particular risk. In this event the worst case result is used for overall ranking.
Table 4.3 Impact weighting factors for PIG analysis
| Impact score | PIG factor (weighted) |
|---|---|
| Very low | 0.05 |
| Low | 0.1 |
| Medium | 0.2 |
| High | 0.4 |
| Very high | 0.8 |
Figure 4.8 Probability-impact grid
The result of this assessment is a ranking order for all risks within the project register. They may be ranked in terms of cost, schedule and/or performance, for example answering the question of what are the top 10 risks. It will also indicate which risks should be prioritised when generating the risk response plans or allocating project resources.
4.6 QUANTITATIVE AND QUALITATIVE RISK ASSESSMENTS
Figure 4.9 illustrates a typical cumulative cash flow curve for a project. The usage of qualitative and quantitative techniques is also illustrated.
Figure 4.9 Typical project cumulative cash flow and the types of risk management techniques used throughout the life cycle of a project
At the start of a project the risk management techniques tend to be more qualitative. However, as the project moves through its life cycle the risk management techniques tend to become quantitative the more project information and detail there are available.
4.7 VALUE MANAGEMENT
Over the past decade, there has been a trend towards applying value management techniques at ever earlier stages in a project or investment life cycle. Ganas (1997) states that value management has become a blanket that covers all value techniques whether they entail value planning, value engineering or value analysis. However, there is no universally accepted definition of value management, and a number of different definitions have arisen to describe the same approach of application.
The ICE design and practice guide (1996) states that:
Value Management addresses the value processes during the concept, definition, implementation and operation phases of a project. It encompasses a set of systematic, logical procedures and techniques to enhance project value throughout the life of the facility/project.
Table 4.2 Typical qualitative and quantitative risk assessment techniques (Burnside 2007)
| Risk analysis techniques | |||||||
|---|---|---|---|---|---|---|---|
| Qualitative | Semi-Quantitative | Quantitative | |||||
| − | Assessment based on experience, description and scales | − | Qualitative scales are given values | − | Analysis based on mathematical formulas | ||
| None mathematical subjective determination | − | Deterministic (non-random) | Probabilistic | ||||
| − | Brainstorming | − | Sensitivity analysis | Random: | |||
| − | Interview | − | dependency | − | Monte Carlo | ||
| − | Intuition | − | Spider diagrams/plots | − | Latin hyper cube | ||
| − | Questionnaire | − | Confidence envelope (probability contours) | − | Artificial neural networks | ||
| − | Assumptions analysis | − | Decision tree analysis | Stochastic (dynamic) | |||
| − | Hierarchical Holographic | − | Non-dependency | − | Markovian logic | ||
| modelling | − | Tornado diagrams | − | Network scheduling | |||
| − | Nominal group Technique | ||||||
| − | Network scheduling | Conditional probability | |||||
| − | Soft system Methodology | − | Programme Evaluation and Review Technique (PERT) Controlled Conversion Matrix (CCM) | − | Baye’s theorem | ||
| − | Risk matrix chart | − | Critical Path Method (CPM) | − | Bayesian networks (risk maps) | ||
| − | Probability- impact Tables | ||||||
| − | Risk mapping | ||||||
| − | Risk registers | ||||||
| − | Prompt lists | ||||||
| − | Checklists | ||||||
| − | Failure modes and Effects Criticality | ||||||
| − | Analysis (FMECA) | ||||||
| − | Hazard and operability studies (HAZOP) | ||||||
| − | Interviews | ||||||
Connaughton and Green (1996) define value management as:
A structured approach to define what value means to a client in meeting a perceived need by establishing a clear consensus about the project objectives and how they can be achieved.
Although the definitions are similar and contain the key elements of structure and achieving value, there does seem to be some ambiguity surrounding the understanding of the cited terms. Ganas (1997) identified this and introduced the following definitions to clear any ambiguities:
Value is the level of importance that is placed on a function, item or solution. The four traits of value are speed, quality, flexibility and cost.
a. speed – how quickly a firm can deliver a product to the customer or design and produce a product
b. quality – how well a product meets a customer’s expectations
c. flexibility – how easily the firm can change a product to closely meet the customer’s expectations/wants
d. costs – elements to be included in a life cycle costing are – capital, finance, operating, maintenance, replacement, alteration, expansion and innovation costs, and residual values
Value management (VM) is the title given to the full range of available techniques. It is a high-order title and linked to a particular project stage at which value techniques may be applied. It is a systematic, multi-disciplinary, effort directed towards analysing the functions of projects for the purpose of achieving the best value at the lowest overall life cycle project cost (Norton and McElligott 1995).
Value planning (VP) is the title given to value techniques applied during the concept or ‘planning’ phases of a project. VP is used during the development of the ‘brief’ to ensure that value is planned into the whole project from its inception. This is done by addressing the function and ranking of the stakeholders’ requirements in order of importance for guidance. This term can be further subdivided to include strategic VP, which is a technique that can be applied during and prior to the feasibility stage when alternatives to a built solution will be considered.
Value engineering (VE) is the title given to value techniques applied during the design phases of a project and, as required, in the implementation processes also. VE investigates, analyses, compares and selects amongst the various options to produce the required function and the shareholders’ project requirements. VE produces a range of ‘how’ design options for the whole project or for defined parts of it. These are tested against the stakeholders’ value objectives and criteria to remove unnecessary cost without sacrificing function, reliability, quality or required aesthetics.
Value analysis (VA) is the title given to value techniques applied retrospectively to completed projects to ‘analyse’ or to audit a project’s performance, and to compare a completed project against predetermined expectations.
Risk management and VM are all part of a single management structure. It is important, however, to differentiate between them so that the right techniques are introduced at the right time. Risk management is mainly concerned with events that might affect the ‘achievement’ of investment objectives. It requires objectives to be well defined – you cannot assess whether investment objectives will be adversely affected unless there is a prior statement of what they are. Risk management (and, in particular, risk identification and analysis) therefore has a vital role to play in identifying and choosing between competing technical solutions, which is the subject of VE.
Risk management is also an important part of VM, even though it may seem unhelpful to try to identify and manage risks until there is agreement about what the objectives are. In fact, a strategic diagnosis of the risks may well influence how the objectives are set. A consideration of investment risks is likely to feature in outline design proposals during investment feasibility (Connaugton and Green 1996).
4.7.1 Value Management Techniques
4.7.1.1 Concurrent Studies
These are structured reviews of detailed proposals, undertaken by the project team in parallel with the design work, and led by the value manager.
4.7.1.2 Contractor’s Change Proposals
These concern tender and post-tender design and/or construction changes suggested by the contractor and are intended primarily to reduce costs or improve buildability. These changes are usually linked to an incentive scheme which rewards the contractor for savings achieved.
4.7.1.3 Criteria Weighting
This is the assignment of arithmetic weights to different project criteria to reflect their relative importance.
4.7.1.4 Functional Analysis
This is a technique designed to help in the appraisal of value by careful analysis of function ; for instance, the fundamental reason why the project element or component exists or is being designed.
4.7.1.5 Functional Analysis System Technique (FAST)
FAST is a form of functional analysis expressed in diagrammatic form to show the relationship between functions and the means of achieving them.
4.7.1.6 Job Plan
This is a logical and sequential approach to problem solving, which involves the identification and appraisal of a range of options, broken down into their constituent steps and used as the basis of the VM approach.
4.7.1.7 Matrix Analysis (Optioneering)
This is a technique for the evaluation of options where scores are awarded for each option against key criteria. These scores are then multiplied by the appropriate criteria weights and the total weighted scores for each option are examined to identify which offers the best value for money.
The optioneering technique is most valuable when assessing risks. Each option will have its own risks and these risks should be taken into account before an option is agreed. For example, option A may be seen to have very little engineering risk compared with option B. If, however, option A has a shorter operating life than option B then the risk associated with option A is reduced revenue generation. If the prime objective of the investment is NPV then option A is presumed to be too risky to meet such an objective. Figure 4.10 illustrates the VM stages.
4.7.1.8 Objectives Hierarchy
This is a breakdown of the primary objective into successively lower levels of sub-objectives until all the project objectives have been accounted for. Subobjectives may be ranked and weighted as for criteria weighting.
Figure 4.10 The value management stages. (More emphasis at corporate level is made at the pre-investment stage with detailed SBU and project level involvement during the investment phase)
4.8 OTHER RISK MANAGEMENT TECHNIQUES
4.8.1 Soft Systems Methodology (SSM)
SSM is a qualitative technique and was developed in the late 1970s and early 1980s. Its purpose was to overcome the inability of traditional decision theory to solve adequately all but the most structured of problems. A particular strength of SSM is that it can begin with the simple desire to ‘make things better’.
Smith (1999) states that SSM is typically employed in a cycle of seven stages, as indicated in Figure 4.11.
The first two stages involve finding out about the situation considered as problematic, such as investigating the environment and culture in which the problem exists, the specific problems considered, the reasons why the situation is considered problematic, and the improvements that are sought in the third stage of SSM. A view of the problem is selected which provides an insight into how improvements can be achieved. This is undertaken through the use of root definitions: that is, neutral definitions of the activities or tasks to be undertaken which provide insight into the problem.
Figure 4.11 Soft systems methodology (Adapted from Smith 1999)
The fourth stage involves the building of conceptual models that are logical expansions of the root definitions generated in the previous stage. The models developed are those of systems which can adapt to and survive changes through their processes of communication and control.
The fifth stage of SSM requires that the models developed are compared with reality. This provides a means of instigating debate into how benefits in the systems can be attained. This process directs attention onto assumptions made, highlights alternatives, and provides an opportunity for rethinking many aspects of real-world activity.
The purpose of the sixth stage of SSM is to define changes that will bring about mediation benefits. Such changes have to meet criteria of systematic durability and cultural feasibility. Systematic desirability will include factors such as mechanisms to determine effectiveness and ensuring that logical dependencies are reflected in real-world sequential actions. Cultural feasibility will make allowances for illogical human actions, and the political environment in which decisions are taken.
The final stage of SSM is the implementation of the changes proposed. Undertaking these changes alters the perceptions of the initial problem situation. If required, further cycles of SSM can be employed to seek additional improvements. This process will have been made considerably more straightforward through the structuring of the problem undertaken in the first application of SSM (Smith 1999).
4.8.2 Utility Theory
Modern utility theory, developed from the work of Von Neumann and Morgenstern, is concerned with anticipating consumer behaviour under conditions of uncertainty and suggests that an individual will seek to maximise expected utility. To accommodate the notion that consumers are risk averse, for instance, successively smaller increments of utility are derived from each additional unit of wealth accumulated; it is generally assumed that they possess quadratic utility functions.
Indifference curves, such as those labelled Dl, D2, D3 in Figure 4.12, are used to explain what combination of goods a consumer will choose.
Figure 4.12 Typical indifference map (Adapted from Coyle 2001)
The optimum point is where the consumer’s budget line is tangent to an indifference curve on the indifference map. Thus a consumer will show no preference between combinations of goods X and Y that lie on the same indifference curve, but in seeking maximum expected utility, the consumer will prefer a higher indifference curve to a lower one, that is D3 rather than D2. The point of tangency between the budget line and an indifference curve indicates the consumer will be in equilibrium, maximising utility where relative prices are equal to the marginal rates of substitution.
The concept of utility theory could be applied to the central problem of decision making under uncertainty – the attitude of decision-makers to risk; however, in most industries utility theory tends to be regarded as a theoretical technique, not easily applied. Hertz and Thomas (1983) describe efforts to turn theoretical utility theory into a practical tool. They conclude that, for the present, it is important to alert managers to the possibility of bias in decision making.
4.8.3 Risk Attitude and Utility Theory
With a rudimentary knowledge of probability, it is possible to calculate the expected monetary value (EMV) for decision outcomes (Rafferty 1994). Using this one can pursue the maximisation of EMV as a decision criterion when dealing with decisions under risk. However, it is frequently seen in practice that rational consumers will prefer an alternative to the option that offers the highest expected value.
Utility theory offers a model for understanding this behaviour. Personal attitudes to risk are measured by understanding and studying individual trade-offs between gambles and certain pay-offs. From this we can place individuals into three, self-explanatory categories:
• risk neutral
• risk seeking
• risk averse.
The comparisons are usually made from the use of the ‘Basic Reference Lottery Ticket’ (BRLT). For example, suppose an individual owns a lottery ticket which has an even chance of winning £10 000 or nothing at all. The EMV for the ticket is given in the following expression:
EMV = (£10 000 × 0.5) + (£0.00 × 0.5) = £5000
Now if you were to ask the three different groups of individuals what price they would be willing to pay for the ticket, their responses will vary as follows:
• Risk neutral. This group would, in theory, be willing to sell the ticket for a minimum price of £5000, which is the EMV. The seller would be indifferent between the two outcomes; for instance, for this group, the certainty equivalent of the gamble is £5000.
• Risk seeking. This group would want to retain the ticket for the thrill of the gamble and may not be willing to part with the ticket until the prospective purchaser was willing to pay well over its EMV. This seems mathematically irrational.
• Risk averse. Here the group may decide that it is worth selling the ticket, which has a 50% chance of winning nothing, for a sum less than the mathematical EMV.
Figure 4.13 shows how, but not why, rational people sometimes prefer outcomes which do not have the highest monetary value. Utility theory suggests that instead of maximising EMV, people maximise their own utility. Utilities vary from person to person. The utility function of an individual is unlikely to be identical to the utility function of that individual’s employing organisation.
Figure 4.13 Risk options (Adapted from Coyle 2001)
4.8.4 Nominal Group Technique
Nominal group technique (NGT) is a variant of brainstorming. It is a method of generating ideas which has been developed in an attempt to overcome some of the perceived failures of brainstorming. In NGT, each group member records a number of risks and these risks are presented to the group for discussion. During the presentation, members of the group individually score each risk and the scores are ranked. The scores are then mathematically aggregated to yield a group decision (Frosdick 1997).
4.8.5 Stress Testing and Deterministic Analysis
A stress test is basically a deterministic model typically run in Microsoft Excel. The inputs are derived from factors such as cash flow magnitude, cash flow start and end points, production cost and an estimate of potential project cost escalation over and above the project contingency. Each project stakeholder is responsible for developing a range of possible outcomes, usually as a percentage and typically for their respective factors. For example, marketing is responsible for sales volume and pricing assumptions, manufacturing is responsible for the cost data and project engineering is responsible for project cost escalation assumptions. These factors are typically single point sensitivities. The financial model calculates IRR, NPV and payback period. After the model has been run for the base case, it is then run for a variety of sensitivity cases with each variable set independently for best and worst predicted outcome. The result is either a spider diagram or a tornado diagram showing the individual impact of each factor on project economic parameters such as NPV. Additionally these same impacts are then put into a project risk table that identifies the risk and its NPV impact on the project. The model is then run for the worst case scenario by setting all input variables to their worst anticipated outcomes thereby giving the worst project outcome. Conversely, each input is then set to the most optimistic case giving the best case scenario. Once these scenarios have been compiled, the assumptions are challenged by the various stakeholders in a brainstorming-type format. It is the stakeholders’ responsibility to thoroughly challenge or ‘stress test’ each assumption. Only after the respective stakeholders agree with the project assumptions is the appropriation request sent forward for corporate approval.
Table 4.4 Stress test strengths and weaknesses
| Strength | Weakness |
|---|---|
| Uses more than one analysis tools to evaluate risk | Uses relatively weak financial model in that only single point assumptions are used |
| Seeks to challenge assumptions by brainstorming methods | Relies on individual groups to come up with point assumptions |
| Reasonably simple to use with minimal inputs required to generate an output | Being simple to use, brings with it a lack of robustness that more advanced techniques possess |
| Full breadth of risks analysed even though outliers may not be overly realistic | Does not, typically, take into account interdependence of input variables |
| As with Monte Carlo relies on historical subjective data for variances from base. | |
| Risks tend to be overestimated to ensure a high degree of comfort | |
| Does not output a formal document identifying risk owner or mitigating actions |
The strengths and weaknesses of this methodology contains some strengths not found in Monte Carlo analysis due primarily to the fact that it contains not one but a variety of different risk management tools all rolled into one. Despite this fact, the methodology has inherent weaknesses that the authors feel are better addressed by Monte Carlo techniques. Table 4.4 contrasts these perceived strengths and weaknesses.
The stress test methodology, while outputting a variety of sensitivities and having many similarities to established practices, cannot be pigeonholed into any one category. The methodology outputs do identify the risks and magnitude, but do a relatively weak job of tying down respective probabilities. The tendency is to overestimate the risks and put enough cushion in the appropriation to ensure a viable project.
In contrast, the concept of Monte Carlo simulation, in principle, is fairly simple. Project risk inputs are given probability distributions and run through a mathematical model to generate a resultant risk probability curve. However, depending on the application these models can be highly complex and give misleading results to the inexperienced user. If the user disregards the tails on a distribution, this can eliminate up to 30% of the cumulative probabilities. As with any analysis tool the user needs to fully understand the mechanism, its advantages and weaknesses when applying it. Monte Carlo analysis has proven itself a valuable risk analysis tool if used correctly. Conversely, if used incorrectly it can raise as many questions as answers.
4.8.6 Tornado Diagram
The Tornado diagram is derived from the sensitivity analysis technique. Activities within a project can be subjected to percentage increases or decreases based on the uncertainty at the time of analysis.
Initially those activities, for example those shown in Figure 4.14, are considered to have various outcomes. The effect of risk is expressed quantitatively on each of the items which are then illustrated on a Tornado diagram. The best case scenario is the one that shows a positive saving and the worst case scenario shows the potential losses on each of the activities. The best and worst case scenarios are the outer lines in Figure 4.14. The inner line represents the savings and losses after risk mitigation. For example, before risk mitigation, metal prices have a range of minus $400 and plus $600. This is identified as the most sensitive activity. Insurance, on the other hand, is seen as less sensitive, having a range of plus $250 and minus $150. The risk associated with these activities can then be mitigated by buying forward in the former case and changing insurers in the latter case. Similarly the other activities are mitigated and the inner line can now be drawn to show the worst and best cases for each activity. The smaller the area between the worst case and best case line the less the uncertainty in the scheduled activities.
4.9 COUNTRY RISK ANALYSIS
Country risk assessment was considered to be a new discipline at a premature stage with unclear boundaries and terminology (Leavy 1984). In order to support this argument, a comparison with ‘sovereign risk’ and ‘political risk’ assessment was put forward. ‘Sovereign risk assessment’ is the term normally used in the banking world to refer to the risks related to the provision of loans to foreign governments, while ‘political risk assessment’ is the technique used to predict the political stability and the non-business risk in conducting operations in the different sociopolitical environment. Notable research has been carried out in the area of political risk, resulting in commercially produced inventory checklists, specialised publications and quantitative approaches, which are based mainly on decision-tree analysis, systematic Delphi techniques and other multivariate statistical analyses used to assess political risk factors, particularly in less developed countries (Desta 1985).
Figure 4.14 Typical Tornado diagram for project schedule elements
Leavy (1984) stated that ‘country risk assessment’ aims at the evaluation encapsulating the total risk, non-business (alpha risk) and business (beta risk) borne by a country, which may influence foreign investment. Techniques and frameworks to serve this purpose have been actively developed, with researchers seeking the most suitable system to extract and evaluate information. Blank (1980) reported that the primary analytical methods used by companies in a formalised country risk assessment process are standardised checklists, scenario development, structured quantitative formats, statistical analysis, computerised investment models and Delphi techniques. Many of these methods are also used by corporations investing in their countries of origin, and are thus not specific to overseas investment.
4.9.1 Country Risk Sources – the Checklist
The country risk appraisal aims to identify all the external factors affecting an organisation, resulting in a thorough assessment of the project’s viability. The prevailing country risk assessment methods generally classify the risk components into three categories – political, financial and economic risks (Sealy 2001). Leavy (1984) mentioned the necessity to consider the intricacies arising from socio-cultural differences when operating in a foreign country.
Nagy (1979) stated that in order to carry out the country risk assessment, it is imperative to have a good knowledge of the country’s political, economic and social structure, including the individual and collective character of the ruling government. The legislative, institutional and regulatory framework is equally crucial. This may be ameliorated by familiarity with the facts and figures about past and current political trends that can be used in a logical and systematic manner to assess the possibility of events occurring in the future.
4.9.2 Political Risk
Categorised under political risk are political events that may affect the prospects for the profitability of a given investment (Haendel 1979). In the view of Gutmann (1980) this area is of major interest to companies in their investment decisions. This is confirmed by the fall of the Shah of Iran, which signified the dramatic impact of political events on all financial transactions. Many internationally founded projects were expropriated by the new regime, invoices went unsettled and the local currency was devalued.
The elements of political risk drawn from IBC USA’s international country risk guide in the order of their criticality as quoted by Sealy (2001), combined with various other sources of the literature, are:
• government stability
• socio-economic conditions
• investment climate
• internal conflict and military intervention in politics
• external conflict
• corruption
• religious and/or ethnic tensions
• policy system and management of economy
• law and order
• democratic accountability and quality of the bureaucracy.
4.9.2.1 Government Stability
Government stability reflects both the government’s ability to carry out its declared programme and its ability to stay in office (Sealy 2001). It is comprised of the government’s unity, intergovernment relations, its legislative strength and the level of support from the people. This includes the possibility of change in the regime under which the country operates, rebellion for political power and coups (Thunell 1977).
The probability of a take-over by an extremist government is considered to be high when the present government is incompetent or weak, when either the democratically elected government is based on a small majority or an authoritarian government has a shaky power base, or when there exists a well-organised extremist group (Nagy 1979).
4.9.2.2 Socio-economic Conditions
Sealy (2001) cites that the presence of socio-economic pressures in society, including high levels of unemployment and poverty, could restrain government action or fuel social dissatisfaction. A government of a country with a low per capita income may be forced to delay debt repayments when it requires a reduction in the standard of living because of a restrained budget in other expenditures. Gutmann (1980) mentioned that unfavourable social conditions, such as extremes of wealth due to unequal income distribution between social classes or regions, may lead to discontent in the society and riots.
Leavy (1984) has carried out a more in-depth study of the socio-cultural factors of a country, including the type of economy, ideology (capitalist, social democratic, democratic or communist), demographic pattern, level of education, social norms/values/beliefs, social mobility and structure and culture.
A government’s incapability to resolve structural problems such as excessively rapid population growth, disparities in income distribution, substandard labour relations and illiteracy contributes to heightening socio-economic problems (Nagy 1979). A project is prone to the risk of a strike, particularly in a country that has a history of widespread labour unrest, where strikes are legal, the government is weak in imposing strike bans, wages are low, labour unions are strong and the labour market is tight.
4.9.2.3 Investment Climate
The risk associated with the investment profile may be a standalone factor or a result of other components of political, economic and financial risks. Thunell (1977) and Haendel (1979) quote the variables of the investment climate: namely, the constitutional support for foreign ownership, discrimination and control over foreign business activity, capital repatriation, stability of the local currency and domestic prices, political stability, willingness to grant tariff protection and availability of local capital. Sealy (2001) identified the risks surrounding an investment in a project: namely, contract viability or expropriation probability, repatriation of profits and payment delays.
4.9.2.4 Internal Conflict and Military Intervention in Politics
In assessing the risk of internal conflict, Sealy (2001) pointed out the need to evaluate the extent of political turbulence in the country and its impact on the government. Countries whose government has no armed opposition and does not indulge in arbitrary violence against the civilian population are favoured by investors. On the other hand, the risk of internal conflict is considered to be high in a country that experiences frequent demonstrations and guerrilla activities or is embroiled in an ongoing civil war, terrorism/political violence and civil disorder.
Strong involvement of military forces in politics diminishes the democratic accountability of a country, indicating that the government is incapable of functioning effectively, which poses an obstruction for foreign businesses to carry out their operations efficiently. Moreover, it raises the possibility for the formation of an armed opposition, which brings about the danger of a military take-over in an extreme case.
4.9.2.5 External Conflict
Pressure from foreign action can affect the ruling government, in the form of non-violent influences such as diplomatic pressure, withholding aid, trade restrictions, territorial disputes and sanctions and violent influences ranging from cross-border conflicts to all-out war. The way such external conflict may adversely affect foreign business is cited by Sealy (2001): namely, the possibility of restricting operations, trade and investment sanctions, distortion in the allocation of economic sources and forced change in the societal structure.
4.9.2.6 Corruption
Corruption within the political system is regarded as a threat to foreign investment because it may disrupt the economic and financial environment, reduce the efficiency of government and business by the appointment of incapable personnel under unfair patronage and cause instability in the political system (Sealy 2001). Evidence of corruption can be found in actual or potential situations of excessive patronage, nepotism, job reservation, ‘favours for favours’, misallocation of public funds and secret party funding. The damaging effect of corruption can be strong enough to cause the fall or overthrow of the government, the restructuring of the country’s political institutions or a breakdown in law and order.
In practice, corruption is commonly found in the financial process in the forms of bribery for import and export licences, exchange controls, tax assessments, grant of permission, tender and bid procedures, police protection or loans. Corruptive practices impede a country’s development in various ways: they reduce growth, drive away foreign investors and deprive the country of development funds.
4.9.2.7 Religious and/or Ethnic Tensions
The degree of risk is pronounced by the extent of tension within a country attributable to religious, racial, nationality or language differences that undermine the country’s stability (Gutmann 1980).
The supremacy of a single religious group in the society or government suppresses the religious freedom of the minority and may even lead to the introduction of religious law to replace the civil law and the division of a country in the worst cases, particularly when the group is vocal, strongly backed, well organised, well armed and under the influence of a fanatical, impulsive and irresponsible leader (Nagy 1979). A country with intolerant and openly conflicting, opposing religious and ethnic groups is clearly considered to be risky under this classification.
There is a high probability of riots, disorder and civil war arising when there is deep-seated or bitter antagonism between segments of the population due to ethnic, tribal, religious or ideological differences, coupled with the government’s inability to control the situation through structured reforms. In the case of riots, civil disorder or revolution, the debtservicing ability of the country will decline, since these incidents will possibly result in a drain on the country’s resources, production paralysis, decrease of productive capacity, capital flight, loss of entrepreneurial, managerial and technical expertise, and, of course, impairment of the country’s ability to borrow abroad.
4.9.2.8 Policy System and Management of Economy
The policy factors cited by Goodman (1978) are concerned with the quality of a country’s economic and financial management in relation to the country’s political leadership. Poor quality or mismanagement of the economy may result in adverse economic developments.
4.9.2.9 Law and Order
Sealy (2001) mentioned the importance of evaluating the strength and impartiality of the legal system in place, including the level of adherence to it in practice.
4.9.2.10 Democratic Accountability and Quality of the Bureaucracy
Democratic accountability is measured by assessing whether or not the incumbent government is employing a proactive approach towards the people (Sealy 2001). It ranges from a high degree of democracy to autocracy in extreme cases. A favourable, highly democratic country is signified by freedom and fairness in the election of the government, the existence of active political parties, the transparent control and monitoring of the government’s executive, legislative and judicial actions, the evidence of justice and constitutional or legal guarantees of individual liberty. Democratic accountability is often indicated by the non-dominating, alternating attainment of authority. On the other hand, autocracy refers to the unrelenting leadership of the state by a single group or person either by means of military force or by inherited right.
4.9.2.11 Economic Risk
Appraisal of the economic risk is an exercise that aims to produce a review of a country’s economic strengths and weaknesses. It reveals the condition of the current balance of payments and serves as a means of projecting the long-term growth prospects of the country under scrutiny- provided that correct interpretation is used (Nagy 1979).
In an economic appraisal, the indicators used by IBC USA’s international country risk guide as quoted by Sealy (2001) are:
• gross national or domestic product (GNP or GDP) per head
• real GNP or GDP growth
• annual inflation rate
• budget balance as a percentage of GNP or GDP
• current account as a percentage of GNP or GDP.
An overview of a country’s current level of development can be obtained from the total GNP, the balance of payments and the current account. It is generally acceptable that a country with a larger economy, that is one with a high value of these three indicators, offers greater opportunity, diversity and stability for investment (Goodman 1978).
In a review of a country’s economic situation, Ariani (2001) raised several supplementary considerations, namely level of unemployment as an element of economic development stage, assessment of the economic development plan and its feasibility, including main bottlenecks, and the resource base, the condition of natural and human resources and their availability.
Gutmann (1980) pointed out the importance of the country’s supply of energy associated with the distribution of world energy resources. The disparity between producing and consuming countries is underlined by the sharp rises in the price of oil imposed by OPEC since 1973, which still continues today. The extent to which a country is dependent upon imported energy, particularly oil, and the level of utilisation of indigenous energy resources, should be taken into account when assessing the country’s long-term economic prospects. A country that relies on imported oil for a large proportion of its energy supplies is considered vulnerable under this criterion.
Cyclical recession occurs and spreads as part of the economic process, and its effects are particularly damaging to a country that is economically vulnerable to external shocks (Nagy 1979). Severe deterioration of the general economic condition, including overheating of the economy, a tight labour market, a decline in the current account or balance of payments, high and ever-increasing interest rates, steep price rises and a decline in the country’s business, may result in an economic recession.
4.9.3 Financial Risk
According to Sealy (2001) the essence of financial risk is concerned with the country’s ability to ‘pay its way’, which includes the official, commercial and trade debt obligations. In practice, this covers a wide area, incorporating all of the existing financial support systems and frameworks available to a particular country. The financial risk components are:
• foreign debt as a percentage of GDP
• foreign debt service as a percentage of exports of goods and services
• current account as a percentage of exports of goods and services
• net international liquidity as months of import cover
• exchange rate stability.
According to Goodman (1978), the financial risks are directly or indirectly associated with the net international liquidity of a country. A favourable condition is achieved when the foreign assets and liability decrease while the maturity increases. The measure of assets is obtained from the value of international reserves to imports and the measure of liability is drawn from the debt-service burden of the country under question.
While Gutmann (1980) argued that among these financial indicators, the ones related to a country’s external debt, particularly the debt-service ratio that depicts the current debt burden, serve as the most relevant guide, an assessor should bear in mind the fact that the available information often excludes unguaranteed private debt, recently signed debt and the due liability of debt repayments of the current contract.
As a refinement of the financial analysis of a country, Gutmann (1980) stated that the quality of its financial institutions is an essential matter. A country having a fundamentally strong financial establishment – comprising an efficient central bank and a sound institutional framework – is considered to be proficient in its debt management and international financial relations. Institutional support is valuable in providing stability for the financial performance, in the event of political or social disturbances.
The political, economic and financial risks of a country discussed above are the major areas that are closely related to and considered to have substantial effects on foreign investment. A systematic procedure to provide an early warning of risks should be developed to facilitate a thorough appraisal, especially in view of the volatile international business environment.
4.9.4 Organisational Usage of Risk Management Techniques
The following points summarise the results from a recent survey in terms of the risk management techniques used at each level of an organisation (Merna 2003).
The risk management techniques used at the risk identification stage are as follows:
• Brainstorming is a very popular technique which is used at corporate and strategic business levels.
• Checklists are very popular at the project level, with over 70% of targeted organisations using them.
• Prompt lists or risk measures are a popular technique at the project level.
• Risk registers are used throughout organisations. Over 70% of targeted organisations use this technique at strategic business and project levels.
• Very little value management is exercised at corporate and strategic business levels. Value management is primarily seen as a project-level tool; however, the business case stage of the value management process is normally undertaken at the corporate level.
The risk management techniques used at the risk analysis stage are as follows:
• Interviews are very popular techniques used at the corporate level.
• Value management is a more project-oriented tool and not used at the corporate level.
• Probability impact tables are more commonly used at strategic business and project levels.
• Decision trees are seen to be a project-level technique, with over 60% of targeted organisations using them.
• Monte Carlo simulation and sensitivity analysis are seen more as project-level-oriented techniques.
• The majority of risk analysis occurs at the project level, followed by the strategic business level and then the corporate level.
• The mathematics-oriented techniques are primarily carried out at the project level.
4.10 SUMMARY
The choice of risk management technique and application is extremely important in the assessment of project and business investments. Contingency sums should not be added to a project or business without a thorough assessment.
Risk management techniques are generic to all risk assessment. The tools and techniques chosen by an organisation will be based on the type of investment or project to be undertaken. It is important to note there is no ‘specific’ technique to analyse a particular risk. The use of a particular risk management technique is at the discretion of the practitioner.
This chapter has described the choices of tools and techniques, both qualitative and quantitative, used in the risk management process that can be applied at corporate, strategic business and project levels. The key features of the value management process and its application have also been described.
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