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Risk analysis in project management
Risk analysis in project management Armin Franke
The tense risk situation in industrial plant contracting requires target-oriented risk management. Being a dynamic process, risk management presupposes regular updating in order to analyse the development of the project risks continuously. Only knowledge of the risk structure and of the dates when risks occur during the project sequence make it possible to initiate definite measures for minimizing risks. The method described for analysing and quantifying risks has been applied successfully to projects in chemical-plant and powerplant contracting. Keywords:
management,
risk analysis,
risk assessment
At present, plant contracting worldwide is facing a phase of recession and change, and is characterized by severe competition on relatively few large-scale projects. As a result of the high corresponding contract volumes, the risks of the plant contractors in design, handling and financing will increase disproportionately. The contributing factors to this tense risk situation are as follows. Increasing country-related risks. Outlets for export trade are to be found more and more in countries characterized by payment trouble, lack of qualified personnel and an underdeveloped infrastructure. Estimating and handling risks. These result from the big individual contract volumes of large-scale projects and could very well jeopardize the very existence of companies. Increasing financing problems. When supplying goods to countries having balance-of-payment problems, the customers not only expect the bidder
Lurgi GmbH, furt am Main
Gervinusstr.
17/19, Postfach
11 12 31, DA000
Frank-
I I, FRG
Vol 5 No 1 February
1987
0263-78(73/87/010029-06
to submit the technical quotation, but also a financing concept which often involves risks such as cofinancing, barter transactions, technical assistance for the initial years of operation, etc. Therefore, with large-volume contracts and the increasing risk potential, dynamic cost engineering is required with the aim of instigating project cost control. To ensure risk transparency and risk minimization, the managerial sector of the plant contracting industry must receive specific and sufficiently accurate cost information, so as to give it a means of making decisions. An important aspect in the run-up to making a decision is the cost quantification of project risks, based on the concept of dynamic risk analysis and risk assessment. CONCEPT OF RISK ASSESSMENT
ANALYSIS
AND RISK
Experiences of the past few years have given rise to a concept of risk analysis and risk assessment which has meanwhile been applied successfully to projects in chemical and metallurgical plants and in power-plant engineering, where investment sums between 1OOM DM and several billion DM are involved. The procedure outlined below is, however, generally applicable and is feasible with projects in the research and development field, EDP projects, etc. The concept of risk analysis is based on two theses (Figure 1). Analogically to the project structure, there are uncertainties within every project which can be described by means of a risk structure and oriented according to the project execution. On the basis of the risk structure, risks can be classified as a combination of quantifiable, the costs of which can be forecasted, and qualifiable, the cost effects of which cannot be assessed directly.
$03.00 @ 1987 Rutterworth
& Co (Publishers)
Ltd
29
Figure 3, a uniform basis of assessment which will inevitably only involve costs, i.e. monetary units. Therefore, all risks resulting from divisions scheduling, work progress, engineering, quality and the commercial sector, will inevitably have an effect on the costs of defined project targets. The essential steps for analysing and assessing qualitative risks are
Figure
forming a structured risk checklist, classifying the risks using the ABC analysis, cost-related risk assessment using the Delphi method, quantifying expert opinion and preparing a risk profile, management decision as to the scale of the necessary contingency initiating risk and minimization measures, repetition of the risk analysis.
I. Risk components
The following detail.
I--
Projectsequence
start Figure
End
2. Risk development
Qualifiable
and quantifiable
risks
The distinction between qualifiable and quantifiable risks forms the basis for risk analysis and risk assessment during the project handling stage. During project execution, the risks decrease at the same rate as knowledge of the project increases, since specific risks occur as the project proceeds, the effects on cost of which can therefore be assessed. while other risks can not occur beyond a particular point on account of their scheduled dates in the project sequence. To put it simply, there are many qualitative risks at the beginning of a project which diminish as more information becomes available (Figure 2). The quantifiable risks can be easily assessed within the scope of routine cost control by means of ‘change management’ and the analysis of those costs still to complete, so that the uncertainty with regard to the project’s cost target is essentially influenced by the cost effects of the qualitative risks. Only if sufficiently accurate information is available within good time, can the management initiate definite measures for minimizing risks. Therefore, qualitative risks have to be recognized early on in the start phase of a project, and to what extent and with what probability they are likely to occur has to be assessed.
sections
describe
the individual
steps in
Risk checklist - structuring of risks Structured risk checklists are used to identify specific project risks and to emphasize those factors that will influence the project targets (milestones, costs, quality, etc.). A standardized risk checklist for medium-scale projects in industrial plant engineering could be structured as follows: risks resulting from quantities and efficiencies (processes, engineering, procurement, erection, tests and inspections), risks resulting from dependencies (customer, suppliers, etc.),
Risks
Scheduling
Progress
Risk quantification It is the aim of risk quantification to recognize qualitative risks, define them and assess them. In so doing, an essential principle should be kept in mind. That is, the risks of a project must have, as shown in
30
Uniform assessment in monetary units
Figure
3. Uniform
basis of assessment
Project
Management
external influences (authorities, situation, etc.), 0 uncertainties as to payment, l uncertainties as to liability (delay, l warranty risks, l scheduling risks.
l
politics,
penalty,
Moderator
market
etc.), \
For prototype or complex plants, the use of standardized checklists is not sufficient for describing the risk situation. In this respect, the preparation of individual risk catalogues has proved to be an effective measure. Figure 4 shows an example of how to structure a risk catalogue. ABC analysis To enable a specific target-oriented investigation into the project risk situation, the risks defined in the risk catalogue or the risk checklist are classified according to their effective contribution to the overall risk potential. Using the ABC analysis method, the few ‘Arisks’ will be those that have the greatest effect on the projects’ risk situations. To determine and calculate the A-risks, approximate, simplified time schedules (milestone network diagrams, etc.) and cost estimates have proved effective. Special attention should be paid to the conditions and variations of the assumptions and boundary conditions, these being the defined basis for project handling. Delphi method for risk assessment The assessment of those risks, defined in the risk catalogue or in the checklist, is based on the Delphi method. The essential difference to the classical Delphi method is that the necessary questioning of the experts is not performed by way of a survey, but the experts form their own opinions in meetings presided over by a moderator (Figure 5). A detailed outline of the risk assessment procedure is as follows.
0
0
0
0 0
Experts Figure
Risk
r
1 Risk I I I I
1 Total,risk
I
]___--
_______
A
Risk
I
AA
_1
l
B
I Risk AB
I
Risk EA
Risk BEI
I
I I I
Risk ABA
Project
Figure
sequence
4. Risk structure
Vol 5 No I February
1987
/I!;
discussion
Exact definition of the risk. At the beginning of the meeting, the risk to be assessed is defined and differentiated from other risks. 0 Risk discussion. Following the definition of the risk, its causes and interdependencies will be pointed out, influential parameters will be rated and forecasts will be prepared during the questioning, so that all members of the team will be equally informed of the risk. It is recommended that, first, the technical aspects of the risk are discussed before a cost assessment margin is defined. Cost assessment. Following this risk discussion, the experts voice their opinions as to the extent of the risk and the corresponding probability of its occurrence. 0
I
I
I
I
-_
I
I I I I
____
5. Experts’
of the risk, depending on the corresponding probability of its occurrence. The competent implementation of these meetings is crucial for the expressiveness and quality of a risk assessment. Based on experiences of the past few years, the following regulations should be complied with:
Formation of representative expert teams. For the purpose of analysing and assessing qualitative risks, interdisciplinary expert teams are formed. Teams of between four and eight experts will ensure the highest efficiency. Expert meeting and risk quantification. The experts’ meetings are held in accordance with the abovementioned version of the Delphi method. At the end of the meeting, the experts indicate a cost assessment b__----
0 /
0
Figure 6 shows the result of the questioning experts on one particular risk. The following points are to be considered assessment: l
of four in the
No criticism among the group members of their individual assessment; differences of opinion should be overcome in the preceding discussion,
31
Expert
Risk values, -5 0
1 2 3 4
10 50 10 0
15 30 20 100
Probability
Figure
l
6. Expert
million 6 30 20 10 0
DM 12
30
70
z %
5 0 20 0
20 0 10 0
20 0 30 0
100 100 100 100
of occurrence,
assessment
%
EDP-AIDED PROFILE
PREPARATION
The experts’ enquiry as to the qualitative risks supplies a variety of individual data. This data is evaluated according to the statistical simulation method. Mathematical
for one risk
experts’ opinions will be anonymously mentioned in order to achieve a result supported by every member of the expert team.
OF A RISK
model
The overall risk of a project follows from the synthesis of the individual risk components. Therefore, the assessment is based on a mathematical model, which derives the necessary overall contingency (C,,) from the total of individual risk values (I?“).
Cc,= 2 R,; R, =
f (R,,
PC,)
Preparing a risk protile In the statistical evaluation of experts’ opinions, the number of combinations will very quickly increase so that a complete enumeration is not possible. Therefore, mathematical simulation is used to evaluate the subjective data obtained within the scope of the experts’ questioning and to represent them in a risk profile. Figure 7 shows such a risk profile indicating the contingency for all risks as a function of the probability of occurrence. The set-up of the risk profile depends on the risk structure (see Figure 4). The range between 10% and 90% probability has proven to be informative, although the variation becomes too high in the marginal areas. The mathematical model for the determination of the risk profile and the configuration of the associated EDP-system are dealt with later.
The risk values depend on the scope of the estimated individual risks (R,) and the corresponding probability of their occurrence (PO). In other words, the total of individual risks corresponds to the scope of overall contingency. For this the different opinions of the experts are purpose, compiled and evaluated to form combinations of values. In the statistical evaluation of experts’ opinions, the following becomes apparent. A number of possible value combinations will very quickly increase for the determination of the required overall contingency (C,), which no longer permits a complete enumeration. Therefore, the process of mathematical simulation offers itself for the assessment of the experts’ opinions.
Management decisions and initiation of risk minimizing measures The experts’ questioning, the statistical evaluation of the opinions given, and the graphics, form the basis for the managerial decisions to be taken by the management. Parameter variation makes it possible to establish the influence of specific risks on the overall risk, and thus to analyse the sensitivity of the risk structure of a project. An essential measure taken by the management is the initiation of a target-oriented updating of the risk analysis. Only periodical repetition of the risk analysis, and necessary revision of the risk catalogue, create assessment criteria which permit successful execution of the measures, designed to minimize the risk.
The risk profile is calculated by means of an EDP system of modular structure. Figure 8 outlines the configuration of this system. The configuration and application of EDP instruments are not explained in in the author’s opinion, the greater detail, since, success of risk analysis and risk assessment is ensured by their competent realization by the controller in charge. Using and commanding mathematical methods and EDP svstems. for the assessment and representation of results, *is essential.
Configuration
APPLICATIONS Having described the concept of risk analysis, this section shows how risk analysis has been introduced and used at Lurgi GmbH. The construction of large-
1
Module
Function
1
Data collection: collecting, revising, adding, deleting Simulation Generating random data for the selection of value combinations to process them in a mathematical model Mathematical model: calculation of the total contingency (C,) Statistics output Various reports Graphical output
2 2.1
2.2
I
0
40
80
120
160
200
240
280
of the EDP system
2.3 3 3.1 3.2
Contingency, TDM
Figure
32
7. Risk profile
Figure
8. Modular
structure
of the EDP
Project
system
Management
DO1 Process
engineering
005 Dependencies of client 006 Dependencies of suppliers 007 Engineering delay 008 Erection delay 009 Political situation
A Quantity and efficiency risks A Quantity and efficiency risks A Quantity and efficiency risks A Quantity and efficiency risks B Risks resulting F dependencies B Risks resulting F dependencies D Scheduling risks D Scheduling risks C External influences
010 Escalation
C External
DO2 Engineering
service
003 Mechanical equipment 004 Electrical equipment
Summary
Figure
Risk value 1 0 Expert
Probability
01 02 03 04 05 06
20 20 20 20 0 0
Expected
value
3 of occurrence,
70 40 50 50 0 0
10 30 20 30 50 10
5
Expected value expert, DM
% 0 10 10 0 50 90
risk
1 3 2 2 6 6
900 000 700 500 000 800
3810
Figure IO. Data gathering for one risk: efficiency risks for engineering sm~icm
quantity
and
scale nuclear power plants with contract values up to billions of DM has been a particularly successful area for the employment of risk analysis.
Risk checklist Figure 9 shows one page of a standardized risk checklist and the appertaining questioning of the experts for one risk (see also Figure 6).
Data input Figure 10 shows not only the input record of the collected data for one particular risk but also the appertaining expected values. Figure 11 shows the presentation of a summary of assessed risks with information of the appertaining group of risk and the expected risk values.
Simulation
and results
The main module of the EDP system supporting the risk analysis is simulation module 2. The logical flow of data processing in the simulation module is shown in Figures I? and 13. Figure 12 shows the results of the
Vol 5 No I February
1987
150
3 810 2 610 2 730 2 800 1310 3 530 5 350 4 230 1 330
influences
45 850
of risks and expected
risk values
2: Simulation A:BMFT85.
Data set name Using the following risk groups Number of ranges Number of simulations
9. Risk checklist
18
of risks
I I. Summary
Module
Figure
Expected value
Risk group/title
Risk-title
DTA ABV 10 300
List of simulations 22 39 38 42 40 42 32 47 49 38 32 39 31 36 32 39 48 26 37 30 39 50 40 32 33 32 31 33 34 43 36 42 34 38 28 36 31 27 39 31 38 38 33
37 34 33 44 37 28 30 32 40 43 46 23 28 27 45 41 45 36 35 36 27 34 37 40 47 39 41 40 41 34 31 38 46 42 41 37 30 41 37 46 42 30 43 Minimum
Figure
38 50 38 29 38 40 42 38 33 38 38 43 42 34 31 32 32 32 41 36 36 37 32 39 39 46 39 37 38 27 44 28 35 31 30 38 38 42 35 32 33 35 43 and
maximum
43 28 45 43 37 41 36 40 27 42 47 40 30 38 34 34 42 39 33 25 35 36 39 43 48 44 31 30 36 42 53 37 36 35 30 35 31 35 29 35 39 36 42
29 29 39
32 30 43 38 41 37 36 34 36 40 28 35 43 32 51 38 32 28 31 39 39 47 45 35 47 40 46 43 39 39 28 23 46 42 33 27 37 37 47 47 36 33 36
41 41 35 30 43 39 36 36 34 43 25 36 35 35 36 39 35 35 36 37 32 34 42 30 28 33 30 40 39 34 34 36 40 39 33 41 40 44 30 48
22
53
31 43 37 29 32 44 39 33 28 37 28 25 23 35 40 33 35 34 34 34 29 31 27 36 40 40 34 25 34 39 41 39 33 36 29 23 40 39 36 39 28 31
12. Simulations
33
Statistics Average Variance Standard
deviation
Distribution
1 2 3 4 5 6 740 8 946 10 11 12
Figure
36.4 33.5 5.8
mean
curve
Range
Quantity
Subjective probability, %
Cumulative probability, %
Cumulative probability, %
22 25 28 31 34 37
1 8 20 33 48 59 61 40 15 11 3 1
0.33 2.67 6.67 11.00 16.00 19.67 20.33 13.33 5.00 3.67 1.00 0.33
0.33 3.00 9.67 20.67 36.67 56.33 76.67 90.00 95.00 98.67 99.67 100.00
100.00 99.67 97.00 90.33 79.33 63.33 43.67 23.33 10.00 5.00 1.33 0.33
43 49 52 55
13. Distribution
curve
simulations against the input parameters. Some important statistical quantities, as well as the distribution curve, are represented in Figure 13. Finally, the results are represented in graphical form as a risk profile, which has been already shown in Figure 7. REFERENCES 1 Backhaus, K and Molter, W Risikomanagement im Harvard GroRanlagenbau’ in internationalen manager II (1984) pp 3643
Berth, R ‘Risikoquantifizierung’ in Harvard manager I(1983) pp 2631 Brandes, W, Budde, H J and Bloeck, J ‘Die anwendungsorientierte Risikoabschatzung fur stratcgischc Invcstitionen’ Dcr Betrirb Vol 5 1152 ( 19X.3) pp 2697-2700 Endell, L ‘Die Kontrolle finanzieller Risiken beim Anlagenexport Zfhf (April 1984) pp 30&316 Hertz, D ‘Risk analysis in capital investment’ Harvard Business Rev. Vol42 No 1 (1964) pp 95-106 Lichtenherg, S, Mortensen, J H, Taylor, R and Tengvad, S Risk management-terminology, methods and examples Copenhagen, 1981 Miiller-Merbach, H ‘Risikoanalyse’ in Die neuen Methoden
der Entscheidungsfindung
’
Armin Frunke studied ut the Technical University of Darmstadt. finishing with the degwe of a quulified commercial engineer. Since then, he bus spent more than five yerws in the project control department of Lurgi GmhH, Frunkfttrt. As the heud of the cost-controlling group, he has been successful, not only in project execution, hut ~1.70 in the development of concepts and instruments. He is working us a responsible coordinating manager in the I.urgi consulting group for the fast breeder power plant of Kalkur, appointed by the German Ministry of Research und Technology.
Project
Management
The tense risk situation in industrial plant contracting requires target-oriented risk management. Being a dynamic process, risk management presupposes regular updating in order to analyse the development of the project risks continuously. Only knowledge of the risk structure and of the dates when risks occur during the project sequence make it possible to initiate definite measures for minimizing risks. The method described for analysing and quantifying risks has been applied successfully to projects in chemical-plant and powerplant contracting. Keywords:
management,
risk analysis,
risk assessment
At present, plant contracting worldwide is facing a phase of recession and change, and is characterized by severe competition on relatively few large-scale projects. As a result of the high corresponding contract volumes, the risks of the plant contractors in design, handling and financing will increase disproportionately. The contributing factors to this tense risk situation are as follows. Increasing country-related risks. Outlets for export trade are to be found more and more in countries characterized by payment trouble, lack of qualified personnel and an underdeveloped infrastructure. Estimating and handling risks. These result from the big individual contract volumes of large-scale projects and could very well jeopardize the very existence of companies. Increasing financing problems. When supplying goods to countries having balance-of-payment problems, the customers not only expect the bidder
Lurgi GmbH, furt am Main
Gervinusstr.
17/19, Postfach
11 12 31, DA000
Frank-
I I, FRG
Vol 5 No 1 February
1987
0263-78(73/87/010029-06
to submit the technical quotation, but also a financing concept which often involves risks such as cofinancing, barter transactions, technical assistance for the initial years of operation, etc. Therefore, with large-volume contracts and the increasing risk potential, dynamic cost engineering is required with the aim of instigating project cost control. To ensure risk transparency and risk minimization, the managerial sector of the plant contracting industry must receive specific and sufficiently accurate cost information, so as to give it a means of making decisions. An important aspect in the run-up to making a decision is the cost quantification of project risks, based on the concept of dynamic risk analysis and risk assessment. CONCEPT OF RISK ASSESSMENT
ANALYSIS
AND RISK
Experiences of the past few years have given rise to a concept of risk analysis and risk assessment which has meanwhile been applied successfully to projects in chemical and metallurgical plants and in power-plant engineering, where investment sums between 1OOM DM and several billion DM are involved. The procedure outlined below is, however, generally applicable and is feasible with projects in the research and development field, EDP projects, etc. The concept of risk analysis is based on two theses (Figure 1). Analogically to the project structure, there are uncertainties within every project which can be described by means of a risk structure and oriented according to the project execution. On the basis of the risk structure, risks can be classified as a combination of quantifiable, the costs of which can be forecasted, and qualifiable, the cost effects of which cannot be assessed directly.
$03.00 @ 1987 Rutterworth
& Co (Publishers)
Ltd
29
Figure 3, a uniform basis of assessment which will inevitably only involve costs, i.e. monetary units. Therefore, all risks resulting from divisions scheduling, work progress, engineering, quality and the commercial sector, will inevitably have an effect on the costs of defined project targets. The essential steps for analysing and assessing qualitative risks are
Figure
forming a structured risk checklist, classifying the risks using the ABC analysis, cost-related risk assessment using the Delphi method, quantifying expert opinion and preparing a risk profile, management decision as to the scale of the necessary contingency initiating risk and minimization measures, repetition of the risk analysis.
I. Risk components
The following detail.
I--
Projectsequence
start Figure
End
2. Risk development
Qualifiable
and quantifiable
risks
The distinction between qualifiable and quantifiable risks forms the basis for risk analysis and risk assessment during the project handling stage. During project execution, the risks decrease at the same rate as knowledge of the project increases, since specific risks occur as the project proceeds, the effects on cost of which can therefore be assessed. while other risks can not occur beyond a particular point on account of their scheduled dates in the project sequence. To put it simply, there are many qualitative risks at the beginning of a project which diminish as more information becomes available (Figure 2). The quantifiable risks can be easily assessed within the scope of routine cost control by means of ‘change management’ and the analysis of those costs still to complete, so that the uncertainty with regard to the project’s cost target is essentially influenced by the cost effects of the qualitative risks. Only if sufficiently accurate information is available within good time, can the management initiate definite measures for minimizing risks. Therefore, qualitative risks have to be recognized early on in the start phase of a project, and to what extent and with what probability they are likely to occur has to be assessed.
sections
describe
the individual
steps in
Risk checklist - structuring of risks Structured risk checklists are used to identify specific project risks and to emphasize those factors that will influence the project targets (milestones, costs, quality, etc.). A standardized risk checklist for medium-scale projects in industrial plant engineering could be structured as follows: risks resulting from quantities and efficiencies (processes, engineering, procurement, erection, tests and inspections), risks resulting from dependencies (customer, suppliers, etc.),
Risks
Scheduling
Progress
Risk quantification It is the aim of risk quantification to recognize qualitative risks, define them and assess them. In so doing, an essential principle should be kept in mind. That is, the risks of a project must have, as shown in
30
Uniform assessment in monetary units
Figure
3. Uniform
basis of assessment
Project
Management
external influences (authorities, situation, etc.), 0 uncertainties as to payment, l uncertainties as to liability (delay, l warranty risks, l scheduling risks.
l
politics,
penalty,
Moderator
market
etc.), \
For prototype or complex plants, the use of standardized checklists is not sufficient for describing the risk situation. In this respect, the preparation of individual risk catalogues has proved to be an effective measure. Figure 4 shows an example of how to structure a risk catalogue. ABC analysis To enable a specific target-oriented investigation into the project risk situation, the risks defined in the risk catalogue or the risk checklist are classified according to their effective contribution to the overall risk potential. Using the ABC analysis method, the few ‘Arisks’ will be those that have the greatest effect on the projects’ risk situations. To determine and calculate the A-risks, approximate, simplified time schedules (milestone network diagrams, etc.) and cost estimates have proved effective. Special attention should be paid to the conditions and variations of the assumptions and boundary conditions, these being the defined basis for project handling. Delphi method for risk assessment The assessment of those risks, defined in the risk catalogue or in the checklist, is based on the Delphi method. The essential difference to the classical Delphi method is that the necessary questioning of the experts is not performed by way of a survey, but the experts form their own opinions in meetings presided over by a moderator (Figure 5). A detailed outline of the risk assessment procedure is as follows.
0
0
0
0 0
Experts Figure
Risk
r
1 Risk I I I I
1 Total,risk
I
]___--
_______
A
Risk
I
AA
_1
l
B
I Risk AB
I
Risk EA
Risk BEI
I
I I I
Risk ABA
Project
Figure
sequence
4. Risk structure
Vol 5 No I February
1987
/I!;
discussion
Exact definition of the risk. At the beginning of the meeting, the risk to be assessed is defined and differentiated from other risks. 0 Risk discussion. Following the definition of the risk, its causes and interdependencies will be pointed out, influential parameters will be rated and forecasts will be prepared during the questioning, so that all members of the team will be equally informed of the risk. It is recommended that, first, the technical aspects of the risk are discussed before a cost assessment margin is defined. Cost assessment. Following this risk discussion, the experts voice their opinions as to the extent of the risk and the corresponding probability of its occurrence. 0
I
I
I
I
-_
I
I I I I
____
5. Experts’
of the risk, depending on the corresponding probability of its occurrence. The competent implementation of these meetings is crucial for the expressiveness and quality of a risk assessment. Based on experiences of the past few years, the following regulations should be complied with:
Formation of representative expert teams. For the purpose of analysing and assessing qualitative risks, interdisciplinary expert teams are formed. Teams of between four and eight experts will ensure the highest efficiency. Expert meeting and risk quantification. The experts’ meetings are held in accordance with the abovementioned version of the Delphi method. At the end of the meeting, the experts indicate a cost assessment b__----
0 /
0
Figure 6 shows the result of the questioning experts on one particular risk. The following points are to be considered assessment: l
of four in the
No criticism among the group members of their individual assessment; differences of opinion should be overcome in the preceding discussion,
31
Expert
Risk values, -5 0
1 2 3 4
10 50 10 0
15 30 20 100
Probability
Figure
l
6. Expert
million 6 30 20 10 0
DM 12
30
70
z %
5 0 20 0
20 0 10 0
20 0 30 0
100 100 100 100
of occurrence,
assessment
%
EDP-AIDED PROFILE
PREPARATION
The experts’ enquiry as to the qualitative risks supplies a variety of individual data. This data is evaluated according to the statistical simulation method. Mathematical
for one risk
experts’ opinions will be anonymously mentioned in order to achieve a result supported by every member of the expert team.
OF A RISK
model
The overall risk of a project follows from the synthesis of the individual risk components. Therefore, the assessment is based on a mathematical model, which derives the necessary overall contingency (C,,) from the total of individual risk values (I?“).
Cc,= 2 R,; R, =
f (R,,
PC,)
Preparing a risk protile In the statistical evaluation of experts’ opinions, the number of combinations will very quickly increase so that a complete enumeration is not possible. Therefore, mathematical simulation is used to evaluate the subjective data obtained within the scope of the experts’ questioning and to represent them in a risk profile. Figure 7 shows such a risk profile indicating the contingency for all risks as a function of the probability of occurrence. The set-up of the risk profile depends on the risk structure (see Figure 4). The range between 10% and 90% probability has proven to be informative, although the variation becomes too high in the marginal areas. The mathematical model for the determination of the risk profile and the configuration of the associated EDP-system are dealt with later.
The risk values depend on the scope of the estimated individual risks (R,) and the corresponding probability of their occurrence (PO). In other words, the total of individual risks corresponds to the scope of overall contingency. For this the different opinions of the experts are purpose, compiled and evaluated to form combinations of values. In the statistical evaluation of experts’ opinions, the following becomes apparent. A number of possible value combinations will very quickly increase for the determination of the required overall contingency (C,), which no longer permits a complete enumeration. Therefore, the process of mathematical simulation offers itself for the assessment of the experts’ opinions.
Management decisions and initiation of risk minimizing measures The experts’ questioning, the statistical evaluation of the opinions given, and the graphics, form the basis for the managerial decisions to be taken by the management. Parameter variation makes it possible to establish the influence of specific risks on the overall risk, and thus to analyse the sensitivity of the risk structure of a project. An essential measure taken by the management is the initiation of a target-oriented updating of the risk analysis. Only periodical repetition of the risk analysis, and necessary revision of the risk catalogue, create assessment criteria which permit successful execution of the measures, designed to minimize the risk.
The risk profile is calculated by means of an EDP system of modular structure. Figure 8 outlines the configuration of this system. The configuration and application of EDP instruments are not explained in in the author’s opinion, the greater detail, since, success of risk analysis and risk assessment is ensured by their competent realization by the controller in charge. Using and commanding mathematical methods and EDP svstems. for the assessment and representation of results, *is essential.
Configuration
APPLICATIONS Having described the concept of risk analysis, this section shows how risk analysis has been introduced and used at Lurgi GmbH. The construction of large-
1
Module
Function
1
Data collection: collecting, revising, adding, deleting Simulation Generating random data for the selection of value combinations to process them in a mathematical model Mathematical model: calculation of the total contingency (C,) Statistics output Various reports Graphical output
2 2.1
2.2
I
0
40
80
120
160
200
240
280
of the EDP system
2.3 3 3.1 3.2
Contingency, TDM
Figure
32
7. Risk profile
Figure
8. Modular
structure
of the EDP
Project
system
Management
DO1 Process
engineering
005 Dependencies of client 006 Dependencies of suppliers 007 Engineering delay 008 Erection delay 009 Political situation
A Quantity and efficiency risks A Quantity and efficiency risks A Quantity and efficiency risks A Quantity and efficiency risks B Risks resulting F dependencies B Risks resulting F dependencies D Scheduling risks D Scheduling risks C External influences
010 Escalation
C External
DO2 Engineering
service
003 Mechanical equipment 004 Electrical equipment
Summary
Figure
Risk value 1 0 Expert
Probability
01 02 03 04 05 06
20 20 20 20 0 0
Expected
value
3 of occurrence,
70 40 50 50 0 0
10 30 20 30 50 10
5
Expected value expert, DM
% 0 10 10 0 50 90
risk
1 3 2 2 6 6
900 000 700 500 000 800
3810
Figure IO. Data gathering for one risk: efficiency risks for engineering sm~icm
quantity
and
scale nuclear power plants with contract values up to billions of DM has been a particularly successful area for the employment of risk analysis.
Risk checklist Figure 9 shows one page of a standardized risk checklist and the appertaining questioning of the experts for one risk (see also Figure 6).
Data input Figure 10 shows not only the input record of the collected data for one particular risk but also the appertaining expected values. Figure 11 shows the presentation of a summary of assessed risks with information of the appertaining group of risk and the expected risk values.
Simulation
and results
The main module of the EDP system supporting the risk analysis is simulation module 2. The logical flow of data processing in the simulation module is shown in Figures I? and 13. Figure 12 shows the results of the
Vol 5 No I February
1987
150
3 810 2 610 2 730 2 800 1310 3 530 5 350 4 230 1 330
influences
45 850
of risks and expected
risk values
2: Simulation A:BMFT85.
Data set name Using the following risk groups Number of ranges Number of simulations
9. Risk checklist
18
of risks
I I. Summary
Module
Figure
Expected value
Risk group/title
Risk-title
DTA ABV 10 300
List of simulations 22 39 38 42 40 42 32 47 49 38 32 39 31 36 32 39 48 26 37 30 39 50 40 32 33 32 31 33 34 43 36 42 34 38 28 36 31 27 39 31 38 38 33
37 34 33 44 37 28 30 32 40 43 46 23 28 27 45 41 45 36 35 36 27 34 37 40 47 39 41 40 41 34 31 38 46 42 41 37 30 41 37 46 42 30 43 Minimum
Figure
38 50 38 29 38 40 42 38 33 38 38 43 42 34 31 32 32 32 41 36 36 37 32 39 39 46 39 37 38 27 44 28 35 31 30 38 38 42 35 32 33 35 43 and
maximum
43 28 45 43 37 41 36 40 27 42 47 40 30 38 34 34 42 39 33 25 35 36 39 43 48 44 31 30 36 42 53 37 36 35 30 35 31 35 29 35 39 36 42
29 29 39
32 30 43 38 41 37 36 34 36 40 28 35 43 32 51 38 32 28 31 39 39 47 45 35 47 40 46 43 39 39 28 23 46 42 33 27 37 37 47 47 36 33 36
41 41 35 30 43 39 36 36 34 43 25 36 35 35 36 39 35 35 36 37 32 34 42 30 28 33 30 40 39 34 34 36 40 39 33 41 40 44 30 48
22
53
31 43 37 29 32 44 39 33 28 37 28 25 23 35 40 33 35 34 34 34 29 31 27 36 40 40 34 25 34 39 41 39 33 36 29 23 40 39 36 39 28 31
12. Simulations
33
Statistics Average Variance Standard
deviation
Distribution
1 2 3 4 5 6 740 8 946 10 11 12
Figure
36.4 33.5 5.8
mean
curve
Range
Quantity
Subjective probability, %
Cumulative probability, %
Cumulative probability, %
22 25 28 31 34 37
1 8 20 33 48 59 61 40 15 11 3 1
0.33 2.67 6.67 11.00 16.00 19.67 20.33 13.33 5.00 3.67 1.00 0.33
0.33 3.00 9.67 20.67 36.67 56.33 76.67 90.00 95.00 98.67 99.67 100.00
100.00 99.67 97.00 90.33 79.33 63.33 43.67 23.33 10.00 5.00 1.33 0.33
43 49 52 55
13. Distribution
curve
simulations against the input parameters. Some important statistical quantities, as well as the distribution curve, are represented in Figure 13. Finally, the results are represented in graphical form as a risk profile, which has been already shown in Figure 7. REFERENCES 1 Backhaus, K and Molter, W Risikomanagement im Harvard GroRanlagenbau’ in internationalen manager II (1984) pp 3643
Berth, R ‘Risikoquantifizierung’ in Harvard manager I(1983) pp 2631 Brandes, W, Budde, H J and Bloeck, J ‘Die anwendungsorientierte Risikoabschatzung fur stratcgischc Invcstitionen’ Dcr Betrirb Vol 5 1152 ( 19X.3) pp 2697-2700 Endell, L ‘Die Kontrolle finanzieller Risiken beim Anlagenexport Zfhf (April 1984) pp 30&316 Hertz, D ‘Risk analysis in capital investment’ Harvard Business Rev. Vol42 No 1 (1964) pp 95-106 Lichtenherg, S, Mortensen, J H, Taylor, R and Tengvad, S Risk management-terminology, methods and examples Copenhagen, 1981 Miiller-Merbach, H ‘Risikoanalyse’ in Die neuen Methoden
der Entscheidungsfindung
’
Armin Frunke studied ut the Technical University of Darmstadt. finishing with the degwe of a quulified commercial engineer. Since then, he bus spent more than five yerws in the project control department of Lurgi GmhH, Frunkfttrt. As the heud of the cost-controlling group, he has been successful, not only in project execution, hut ~1.70 in the development of concepts and instruments. He is working us a responsible coordinating manager in the I.urgi consulting group for the fast breeder power plant of Kalkur, appointed by the German Ministry of Research und Technology.
Project
Management