'Club of Economics in Miskolc' TMP Vol. 11., Nr. 1., pp. 25-35. 2015
http://dx.doi.org/10.18096/TMP.2015.01.03
Risk Evaluation of Strategic Indicators
ISTVÁN FEKETE
ÉVA LIGETVÁRI
RESEARCH FELLOW
PHD STUDENT
e-mail: istvan.fekete@uni-corvinus.hu
e-mail: kkkleva@uni-miskolc.hu
VIVIEN AHMED
PHD STUDENT
e-mail: rekcont2@uni-miskolc.hu
SUMMARY
Most risk assessment methods can only be used if historical data are available, as they rely on statistical analysis to assess
risks. However, such datais usually missing or imperfect. Of course, the probability of occurrence and impact of these risks
should always be assessed (estimated) in a reliable manner. The method presented in the paper has been used in more than 50
different applications up to now. The aim of this paper is to demonstrate how the risks affecting the target values of different
strategic indicators can be assessed using the developed method.
Keywords: risk analysis, risk evaluation, integrated risk management, strategic risk management
Journal of Economic Literature (JEL) code: D81
DOI:10.18096/TMP.2015.01.03
INTRODUCTION
Corporate management increasingly demands
strategic decision support and the use of scientific tools
and methods of modelling uncertainties, thus creating a
connection between decisions and their expected
outcomes. To put it differently, corporations want to bear
the risk of their decisions consciously in order to maximise
their profits. For this reason, risk analysis and risk
management are highly topical issues in corporate
practice.
The literature of risk management introduces many
different tools and methods to carry out risk analysis.
However, as we studied the available sources we found
that they were difficult to apply, as they were described in
a language too difficult for practicing professionals to
understand, and illustrative examples were rarely used. In
other words, the methods recommended in specialised
literature are generally not user-friendly. Rather than
providing a scientific classification of the methods offered
in professional literature or proposing their enhancement,
the primary aim of this paper is to put forward a
theoretically well-based risk analysis approach that is easy
to use in corporate practice. This method will be discussed
in the next section.
Before explaining the detailed methodology,
however, we feel it essential to define shortly the concept
of risk management in order to facilitate a better
understanding of the topic.
One of the essential features during a decisionmaking process is the existence of uncertainties.
Uncertainty means that the probability of occurrence of a
given future event and its consequences are not known
exactly. Risk usually means the particular negative or
positive consequences while the occurrence itself is
uncertain, but its probability can be calculated or estimated
(Görög 2008). In order to assess the risk, different risk
sources and events should be first identified.
According to Hillson’s approach, risk usually
refersto uncertain events that may have negative or
positive outcomes (Hillson 2002). The inherent level of a
particular risk is determined by the likelihood and
magnitude of associated events (Hopkin 2012).
RISK ASSESSMENT METHOD FOR
SUPPORTING MANAGEMENT
DECISIONS
25
István Fekete - Éva Ligetvári - Vivien Ahmed
In this section the different approaches how to
identify, analyse, evaluate and treat the risks will be
highlighted.
Interpretation of risk management
It is interesting to investigate how risk analysis and
response work in practice if there are insufficient historical
data available. In the risk management literature a number
of methods can be found that are suitable for risk
assessment. Most of them can only be used if there are
historical data available, as they rely on statistical analysis
to assess risks (see e.g. Jorion 1997). If someonewould like
to calculate exchange rate or interest rate risk exposure, for
example, these statistical methods can be used if daily
databases are available. But what is the situation if
somebody would like to assess risks having an impact on
the strategic goals of the company where he or she is
working? An example could be to select the best strategic
alternative by evaluating the yield/risk ratio for each
alternative. In this case, there is rarely a daily database to
use for assessing most risks. Of course, the probability of
occurrence and impact of these risks should always be
assessed (estimated) in a reliable manner.
There are also different approaches available to
assess risks. These can be divided into two main
categories: qualitative and quantitative methods.
Qualitative methods are easy to use in practice, but
reliability may not possible to ensure.Quantitative
methods may ensure the reliability of analysis, but usage
of them requires a large amount of historical data.
It seems an obvious suggestion to produce input data
for quantitative methods (e.g. Monte-Carlo Simulation) by
using the many years’ experience of participants attending
aworkshop to ensure reliable risk assessment.Of course, a
special methodology is necessary for this, but it is worth to
apply. The method presented below has been used in more
than 50 different applications to date.The aim of this paper
is to summarise the main steps of this method and to show
how to use it in practice.
Risk management covers a systematic process of
identifying, analysing, evaluating, responding to and
controlling risk (Cooper & Chapman 1987; Chapman and
Ward 2003), (PMI 2008).The risk management process for
these steps is shown in Figure 1. The specialities of the
process will be briefly summarised below even for a
situation wherehistorical data are missing or inappropriate.
Figure 1. The suggested risk management process
Source: created by István Fekete
helpful. The composition of participants is important,
since the results are influenced to a great extent by the
Identification of risk sources and events
presence or absence of experts having relevant knowledge.
In case of inappropriate historical data a pre-made
The first task is to identify risk sources/ events in a
database
can be helpful to enhance the identification of
structured form. Several techniques have been proposed
risk
factors
(de Bakker et al. 2010; Bannerman 2008). This
for professionals to identify risk sources/events
database
can
be customised according to the needs of
(Loosemore et al. 2006; Ohtaka & Fukuzawa 2010).
particular organisations. There are different lists for this
available in the risk management literature (see for
For the method in question, brainstorming is needed
example Summer 2000; Hartman & Ashari 2002;Chow &
for executing the task. Workshops lasting a few hours or
Cao 2008; Lind & Culler 2011).
even days, depending on the nature of the task, can also be
26
Risk Evaluation of Strategic Indicators
Quantitative risk assessment
Identification of risk sources and events is followed
by the step of quantifying the probability of their
occurrence and impact. This paper focuses on how to use
the developed methodfor defining input parameters of the
Monte-Carlo simulation (Hertz 1964).
The first task is to delineate the scope of the analysis
and to define the elements of the analysis target values.
The next step is to identify and assign potential risk
sources and events to each elementof analysis. The
identification is done by experts at a workshop.
After the identification is completed, a maximum of
four different scenarios (Watchorn 2007) will be assigned
to each identified risk source and event. The next task is to
estimate the subjective probability of occurrence and
impact of each scenario. This is done by experts at
theworkshop using their many years of experience.It is
important to note that the sum of the subjective probability
of occurrence of the maximum four scenarios cannot
exceed 100%.
Following that, the existence of interrelation (if any)
among the different risk sources and events must be
assigned to one cash-flow element (Hunyadi et al. 1993).
If found, its direction and intensity must also be
investigated.(The direction is positive if an increase in one
variable’s value can cause another variable’s value to
increase and negative if a decrease in one variable’s value
can cause another variable’s value to increase. The
intensity can be measured by a correlation factor between
-1 and 1 (Hunyadi at al. 1993)).To answer this question,
experts’ estimation should be used. Empirical experience
shows that it can be assumed that the value of the
correlation measuring the intensity between two
probability variables can be maximum ±0.6 in the case of
strongest intensity. So the experts attending the workshop
only have to decide whether the intensity between two
variables is strong, medium or weak using their
experience. In this way they can estimate the value of
correlations ranging from –0.6 to 0.6. Of course, it is not
possible to calculate exact correlation values in this way.
But it should be remembered that in this case there are
insufficient historical data available to usestatistical
methods for this task.
The next task is calculation of the expected value and
standard deviation of eachelement using the results of the
scenario analysis. These will be the input data for the
Monte-Carlo Simulation. The expected value and standard
deviation can be used for selecting critical risk sources and
events as well. Inour understanding not every risk should
be treated, anyway. This is because the cost of treatment
can be higher than the cost incurred from the occurrence
of the risk. To ensure the best efficiency of treatment
activity it is vital to select the critical risks which should
be treated in any way. To do this, a special rule can be
used. According to this rule, a risk is critical if the value of
relative deviation (ratio of standard deviation/expected
value)is higher than a predefined threshold value. There
has beenno exact equation to calculate the limit of any
threshold value so far. It can only be defined by using the
experience of a risk analyst. In thispaper we will show how
to define the threshold values with regard to acase study.
If historical data are missing or inappropriate, the
way suggested above can help to increase the chance of
selecting the best suited probability distribution curve,
mean value, and standard deviation belonging to it. This is
the reason forperforming a scenario analysis first and
running Monte-Carlo Simulation only after finishing the
scenario analysis.
Selection of dependent probability variables is the
next task. The change invalue of an independent
probability variable can cause the change of value of a
dependent variable. When all input data are at our disposal,
Monte-Carlo Simulation is ready to run. Once the
predefined number of iterations has been reached, the
probability distribution of net present value with all
characteristic statistical values (mean value, standard
deviation, range, etc.) can be produced. The probability
distribution can also contain the target value, so it is
possible to compare the results of calculation before and
after risk analysis.This is done with the support of any
computer program for risk analysis found on the market
(e.g. Oracle Crystal Ball, Palisade @Risk or Szigma
Integrisk).
Steps of risk evaluation
Risk evaluation requirescreating a high-level
network diagram, including:
• the exact definition of activities,
• definition of the duration of activities,
• logical relationships between activities and
• detailed resource and budget allocation (Grey
1995).
These data are the target values (values before risk
analysis). Each project activity will work as independent
probability variables during the Monte Carlo Simulation.
The next step is to identify and assign potential risk
sources and events that can have an impact on the duration
and/or cost of every single activity (dependent probability
variables) originally calculated.When identification is
completed, the probability of occurrence and impact of
each risk source/event will be estimated by scenario
analysis as above (Cleden 2009).The interrelation among
risk events and independent probability variables (duration
and/or cost) should be analysed (Nakatsu & Iacovou
2009).
Thisis followed by selecting the probability
distribution of the duration/cost of each activity with the
use of the results of scenario analysis. In practice, the most
frequently occurring distributions are the beta, gamma,
triangle, lognormal, and normal distributions (Evans et al.
1993). After this, the parameters (expected value, standard
deviation) characteristic of the given distribution should be
27
István Fekete - Éva Ligetvári - Vivien Ahmed
calculated.The value of the probability of occurrence of
activities after junctions in the network diagram should be
estimated. It is important to keep in mind that the sum
cannot exceed 100% (Grey 1995).
When all input data are available, the simulation
process can be started. The length of the critical path
and/or total cost of the project are calculated from a large
amount of random data obtained from each probability
distribution of the duration/cost of every single activity.
This can be accomplished by any risk analysisprograms
listed above.After reaching the predefined number of
iterations, the probability distribution of the critical path
and/or total project cost can be produced (Grey 1995).
Response to the risks
The risk management process has to formulate and
execute risk response actions for critical risk sources and
events selected previously. Risk response could have the
aim of avoiding, sharing, transferring or accepting a risk
by means of defining a risk response programme
(Harris2009).It is important to consider the following
aspects whenformulating a risk response programme:
• The elements should have a quick-win
characteristic, i.e. should be applicable quickly and
at a reasonable cost.Reasonable costs mean lower
cost than in case of occurrence of the risk event.
• Risk response actions should be measurable during
actualisation. In case of an investment project it may
be possible to increase the chance to finish the
project on time and within the budget or to ensure
the targeted project return. In other words, the
execution of suggested risk response actions
shouldmove the measured value closer to the target
value (value before risk analysis).
It is important to assign a risk owner tothe proposed
actions. A risk owner is a person or an organisation that is
responsible for responding to a risk.
Now we will present different risk response actions
(Balaton et al. 2005):
Risk avoidance – basically thiscovers those actions
that are aimed atavoiding the occurrence. It is used
when risk sources/events often occur and the likely
impact is high (Pataki& Tatai 2008).An example
of this could be the integration of check points,
including internal regulation.
Risk mitigation – this could be aimed at minimising
the probability of risk occurrence by preventing
the risk from occurring. A good example can be
lobbying in order to influence lawmakers. Another
approach is for the companyto prepare different
actions in order to influence the impact, in many
cases to increase the impact of positive risk events.
A good exampleis business continuity planning.
Transferring or sharing risks – thismeans finding a
partner who consciously or unknowingly assumes
at a certain pricelosses generated from potential
dysfunctions. A typical case of risk transfer is
28
insurance, but hiring an external contributor to
implement a project could also be an example
(Görög 2008).
Risk acceptance – In this case, the risk cannot be
avoided or transferred, or the likely impact is out
of proportion with the costs of responding to it.
This implies that management bears the magnitude
of the risk consciously.
Risk controlling
The final step of the risk management process is
performing risk control that covers updating the dataset,
follow-up actions, and plan-fact analysis.
Risk management should be considered as a
snapshot at a given moment. But it could happen that the
kind of information that basically influences the results of
analysis is found the next day. In this case, it is worth
redoing the whole exercise. Of course, now the analysis
can be done quickly, since it only consists of the transfer
of the results from recording and assessing the new risk
arising from new information. It could change the list of
critical events that could modify the risk response actions.
The second element of control activity is following
the risk execution program, which is based on risk
response proposals. This could be considered as classical
control activity and in the course of this the following tasks
should be solved: overview of the situation, impact
analysis, modifications based on impact analysis, ordering
and publishing the modifications and the execution of
modifications.
The third component of control is performing a planfact analysis after finishing the execution of the risk
response actions. The aim of the analysis is to compare the
post-program status with the pre-program status. The planfact analysis means an input for cost-benefit analysis
(Rédey 2012), which can measure the effectiveness and
efficiency of the risk management activity.
RISK EVALUATION IN THE CASE
OF STRATEGIC INDICATORS
The University of Miskolc has prepared and
approved an Institutional Development Plan that includes
the strategic goals and the related performance indicators
(in harmony with the Balanced Scorecard – BSC
indicators) annually for a five-year period. Achieving the
target values of the five-year period may be influenced by
various strategic risks, positively or negatively. It is
essential for the university to identify and understand the
risks that may have any effect on these indicators. Based
on the identified risks, strategic actions can be developed
and performed in order to control the operation in
accordance with the set objectives. It should be noted that
the challenge is not a single intervention; continuous
(regular) control is necessary. The process is summarised
in Figure 2.
Risk Evaluation of Strategic Indicators
Figure2. Strategic control process
Source: created by the authors
The details of risk evaluation are presented in Figure 3.
Figure 3. Process of risk analysis of strategic indicators
Source: created by the authors
the groups must be performed by the internal experts
regularly, at least annually.
The next step is to designate the risk factors of the
strategic indicators within each group. There are various
sources that can be used for supporting the assignments. In
addition to expert estimation, historical data and literature
sources shouldbe taken into consideration. Establishing a
comprehensive risk database will significantly increase the
effectiveness of this step. Proper designation of risks factor
is essential because the probability and the impact can only
be assessedproperlyin this way. If a risk factor is assigned
to more than one strategic indicator, it must be evaluated
separately by each indicator because the impacts may be
different. Table 1 shows an example of assignment.
The content of the risk analysis process using the
methodology in the previous sectionis as follows. A
presumption is that the strategic indicators are available.
The initial step is to organise the indicators into
homogeneous groups. The aim of grouping is to find the
strategic issues that may be influenced by similar risk
factors. Homogenous groups must be the results
ofteamwork. The experts of the university perform a
workshop that allows the proper teamwork. In the
beginning external experts were involved in order to learn
the methodology and keep focus on the content. Of course,
the list of indicators in a group is not set in stone, the
relevant strategic indicators may be changed. Review of
Table 1
29
István Fekete - Éva Ligetvári - Vivien Ahmed
Assignment of risk factors to indicators
Indicator
Risk factor
Rate of students admitted to the
University of Miskolc compared to
all students gaining admission in the
recruitment process of the given
academic year
Description of the risk factor
Legal policy
changes / Changes
in government
funding quota
University’s
reputation
Changes in the government funding quota will influence the
number of students admitted to the University of Miskolc
compared to all students admitted in the country. Natural
sciences and engineering studies have a higher quota, while the
quota of law and economic studies is reduced. Minimum limits
of admission scores may be changed.
Improving the university’s reputation may attract potential
students, so this can influence the number of applications (Rate
of students admitted to the University of Miskolc compared to
all students gaining admission in the recruitment process of the
given academic year)
The task of risk factor evaluation is supported by a
scenario analysis performed in a workshop. The experts of
the University of Miskolc reviewed the factors one by one.
Possible impacts are summarised in the description of the
risk factor based on the methodology described above. It
must be noted that there is a simplification in the process:
interaction between the risk factors is out of scope. It is
hypothesised that the risk factors are independent from
each other. We know that this is not always true, but the
lack of historical data does not allow an estimation of
interrelations with an acceptable level of reliability. The
high failure ratio of the estimation does not help the proper
evaluation but needs huge efforts.Table 2 shows an
example of scenario analysis.
Table 2
Example of scenario analysis
Scenario
Demand for
bachelor
1.
courses is as
planned
Increasing
demand for
2.
bachelor
courses
Reduced
demand for
3.
bachelor
courses
Probability
80
15
5
Impact (difference
from target value,
%)
Justification for the estimation
0
In the given period the probability of achieving the related target
values is high. Statistics of previous years: 2012: 6,275 applicants,
2,650 admitted (1,899 with government funding). Number of firstplace applicants was 3,389. 2011: 8,003 applicants, 3,435 admitted
(2,149 with government funding). Number of first- place applicants
was 4424.
5
There is a competition for places in technical faculties, especially the
Faculty of Mechanical Engineering and Informatics,alsoin the
Faculty of Economics. Demand for courses of the Faculty of Law is
influenced by the distracting effect of the University of Debrecen.
Health care courses havea competition for places as well. Based on
the data of felvi.hu approx. 50−60% of these applications are firstplace applications, so this tendency may further increase.
-5
Based on the forecasts there is a low probability of decreasing demand
for the bachelor courses. There is a decline to be seen in the number
of applicants in comparison between the years 2011 (8,003) and 2014
(4,937), especially in the number of applicants with government
funding (from 2,149 to 1,899), so a general decline may be indicated
if the number of fee-paying students will not compensate.
Calculating expected values and standard deviation
based on the results of scenario analysis allows to find the
risks being treated. Table 3 summarises a sample result.
Table 3
30
Risk Evaluation of Strategic Indicators
Sample results of scenario analysis
Indicator
Rate of students
admitted to the
University of Miskolc
compared to all
students gaining
admission in the
recruitment process of
the given academic
year
Risk factor
Legal policy
changes /
Changes in
government
funding quota
Description of the risk factor
Expected value
of difference
(%)
Relative
deviation
(%)
Changes in the government funding
quota will influence the number of
students admitted to the University of
Miskolc compared to all students
admitted in the country. Natural
sciences and engineering studies have a
higher quota, while the quota of law and
economic studies is reduced. Minimum
limits of admission scores may be
changed.
23.5
25.70
After making the scenario analysis the experts chose
the risk factors which are critical to manage in order to
achieve the university’s strategic goals through meeting
the target values of strategic indicators. The methodology
requires defining tolerances for the expected values and
dispersions calculated during the scenario analysis.
Critical risk factors mustbe managed. A risk factor is
considered to be critical if it exceeds any of these
tolerances. The university experts use a tolerance limit
10% for the expected value and 200% for the relative
deviation (standard deviation divided by the expected
value of difference). Table 4 shows examples of critical
risk factors.
Table 4
Examples of critical risk factors
Indicators
Rate of students admitted to
the University of Miskolc
compared to all students
gaining admission in the
recruitment process of the
given academic year
Critical risk factor
Legal policy changes /
Changes in government
funding quota
University’s reputation
The next step of risk evaluation is elaboration of
(strategic) risk management actions. Besides the
description of the actions, this shouldinclude both the
implementation deadline and the designation of the
Description of the risk factor
Changes in the government funding quota will influence
the number of students admitted to the University of
Miskolc compared to all students admitted in the country.
Natural sciences and engineering studies have a higher
quota, while the quota of law and economic studies is
reduced. Minimum limits of admission scores may be
changed.
Improving the university’s reputation may attractpotential
students, so this can influence the number of applications.
(Rate of students admitted to the University of Miskolc
compared to all students gaining admission in the
recruitment process of the given academic year.)
individual responsibilities. Planning of actions is also
performed as a part of the risk management workshop. A
proposed risk management action is shown in Table 5.
Table 5
A proposed (strategic) risk management action
31
István Fekete - Éva Ligetvári - Vivien Ahmed
Indicator
Risk factor
Rate of students admitted
to the University of
Miskolc compared to all
students
gainingadmission in the
recruitment process of
the given academic year
Legal policy changes
/ changes in
government funding
quota
Risk management
action
Lobbying to keep the
regional knowledge
centre, especially
focusing on the
conformance to the
officialrequirements
related to the admission
quotas
In addition tothe numerical analysis and the content
of the tables above, an evaluation summary is needed that
explains the main results and the relationship between the
particularparts and figures. An important goal of this task
is the consolidation of the critical risks. In practice,
consolidation means the determination of core risk factors,
i.e. risk factors that are different from each other in
content. A prerequisite for being a core risk factor is that it
is assigned to at leastone strategic indicator by the
university experts. Consolidation shouldalso:
Deadline
Person in charge
Continuous
Vice-rectors
•
summarise the risk factors byflagging the indicators
theyare assigned to,
• flag the critical risk factors by strategic indicators.
Eventually, the flagging designates the risks that
must be managed. Table 6 shows an example of a
consolidated list.
Table 6
Consolidated list of critical risk factors
Critical risk factor
Related strategic indicators
Rate of students studying inagiven course at the University of Miskolc
compared to studentsinthe course nationwide
Changes in the number of partners involved in practical education
Utilisation of R&D&I infrastructure
Level of R&D&I orders
Number of PhD students
Legal policy changes / Changes in
Number of Hungarianand international publications and the ratio of them
government funding quota
compared to the number of employees in education/research jobs
Number of scientific publications and four-year target values of increment by
institutional (faculty) level
Number of Hungarianand international monographs and professional books and
the ratio of them compared to the number of employees in education/research
jobs
It is necessary to consolidate the risk management
action based on the consolidation of risk factors. The
results shall consider the suggestions (strategic risk
management action plans) of the university experts. The
output of consolidation is a report for decision makers that
includes in a comprehensive way the followings (an
example is shown in Table 7):
•
•
•
consolidated risk management actions,
personal and/or department level responsibilities,
expected deadlines for performing the actions.
Results of consolidation should be uploaded to the
databases of the university’sinformation management
system.
Table 7
32
Risk Evaluation of Strategic Indicators
Consolidated risk management action
Risk management action Indicator / risk factor
Person in charge
Deadline
Rate of students studyingat the University of
Miskolc compared to studentsnationwide / Legal
policy changes / Changes in government funding
quota
Lobbying to keep the
regional knowledge
centre, especially focusing
on the conformance to the Changes in the number of partners involved in Vice rectors
officialrequirements
practical education / Legal changes
related to the admission
Utilisation of R&D&I infrastructure/ Legal
quotas
changes
Continuous
Level of R&D&I orders / Legal changes
As a result of scenario analysis, annual information
is available about the expected values and standard
deviation of difference from target values of strategic
indicators. This is followed by a comprehensive evaluation
of each risk factor, including the calculation of a total
deviation from the target values. These will allow us to
calculate adjusted target values of the strategic indicators.
Target values before the risk analysis process shouldbe
adjusted by the calculated risk characteristics (expected
values and standard deviation). Ultimately, the
adjusted target values show the deviance from the
institutional development plan. Higher differences in the
values show the higher importance of risk management
actions in order to enhance the possibility of achieving the
original target value. Adjusted target values should also be
uploaded to the databases of the university’s management
information system. Table 8 shows examples of adjusted
target values.
Table 8
Strategic target values adjusted by the results of risk analysis
Indicator
Rate of students studying
ina given course at the
University of Miskolc
compared to studentsinthe
course nationwide
Ratio of first-place
applicants compared to total
applications
Target value in
the Institutional
Development
Plan (2014)
Sum of expected
values of total
difference from
target values in
Institutional
Development
Plan(%)
Expected value of
indicator
Effect of standard
deviation on the
indicator
(deviation caused
by the risk) (%)
3.53%
35.75
4.79%
32.85
57.76%
7.45
62.06%
10.79
•
CONCLUSIONS
Systematic risk management supports institutional
decision making. The systematic approach requires both a
clear methodology of calculations and a proper workflow
adapted to the organisational characteristics. The paper
summarises the solution of the University of Miskolc. The
main experiences and conclusions based on the pilot run
of the system are the following:
•
•
Establishing risk identification and analysis as a
supporting tool of strategic planning helps to
understand the influencing factors of strategic
objectives and to work out proper actions in order to
increase the chance of fulfilling these objectives.
Realisation of the expected benefits is only
achievable by performing the risk management
actions, so attention must be given to assigning
granting proper authorityand responsibilities.
It is important to upload the results to the databases
of the management information system that require
33
István Fekete - Éva Ligetvári - Vivien Ahmed
the necessary integration development actions
(including changes in regulations and technicalprogramming development).
• Deep and intensive risk analysis makes the updating
processes within the planning periodeasier. Due to
the continuous changes in internal and external
environment of the university it is necessary the
modelling of the influencing factors that is easier in
case of the proper initial analysis.
• Detailed justification and (if achievable) data
support forthe results of risk analysis enhances
itscreditability and acceptance.
The pilot evaluation is being carried out as a part of
the TÁMOP-4.1.1.C-12/1/KONV-2012-0001 project.
Long-term utilisation requires the organisational
integration of the process and the methodological
elements, including harmonisation with the management
information system and an up to date risk management
regulation. Furthermore, decision makers must recognise
the benefits and accept the results.
A further challenge insystem development is
improving the accuracy of the expert estimation. We plan
to carry out action research about further strategic
influencing factors of the strategic position of the
University of Miskolc. Including more factors in the risk
analysis will allow us to draw up a more sophisticated map
of risks and to evaluate the expected effects of the factors
in a more detailed way. Our goal is to build up a structure
of factors that is ready for running a Monte-Carlo
simulation, which could give more accurate results.
Acknowledgement
Present article was published in the frame of the
project
TÁMOP-4.1.1.C-12/1/KONV-2012-0001
(“HANDS” – Cooperation of higher educational
institutions of North Hungary). The project is realized with
the support of the European Union, with the co-funding of
the European Social Fund.
REFERENCES
BANNERMAN, P.L. (2008):Risk and risk management in software projects: A reassessment.Journal of Systems and Software,
81(12), 2118–2133. http://dx.doi.org/10.1016/j.jss.2008.03.059
CHAPMAN, C., WARD, S. (2003): Project Risk Management Processes, Techniques and Insight.(2nd Ed.) New York: John
Wiley and Sons.
CHOW,T, CAO, D.B. (2008). A survey study of critical success factors in Agile software projects. Journal of Systems and
Software. 81(6), 961–971. http://dx.doi.org/10.1016/j.jss.2007.08.020
CLEDEN, D. (2009): Managing Project Uncertainty. Farnham, UK:Gower Publishing Limited.
COOPER, D. F., CHAPMAN, C. B. (1987): Risk Analysis for Large Projects: Models, Methods and Cases. New York: John
Wiley and Sons. http://dx.doi.org/10.2307/2582754
DE BAKKER, K., BOONSTRA, A.& WORTMANN, H. (2010): Does risk management contribute to project success? A metaanalysis of empirical evidence.International Journal of Project Management, 28(5), 493–503.
http://dx.doi.org/10.1016/j.ijproman.2009.07.002
EVANS, M., HASTINGS, N. & PEACOCK, B.(1993): Statistical Distributions (2nd Ed.). New York: John Wiley & Sons.
http://dx.doi.org/10.1002/asm.3150100411
FEKETE, I. & HUSTI, I. (EDS.)(2005): Beruházási kézikönyv vállalkozóknak, vállalatoknak (Investment Guide for
Entrepreneurs and Enterprises). Budapest: Műszaki Könyvkiadó.
GÖRÖG, M. (2008): Projektvezetés (Project Management). Budapest: Aula Kiadó.
GREY, S. (1995): Practical Risk Assessment for Project Management. Chichester: John Wiley & Sons.
HARRIS, E. (2009): Strategic Project Risk Appraisal and Management. Burlington: GowerPublishing.
HARTMAN, J.& ASHARI, R. A. (2002): Project management in the information systems and information technologies
industries. Project Management Journal, 3383, 5–15.
HERTZ, D. B. (1964): Riskanalysis in capital investment. Harvard Business Review. 42 January–February, 95–106.
HILLSON, D. (2002): Extending the risk process to manage opportunities. International Journal of Project Management.
208(3), 235–240. http://dx.doi.org/10.1016/s0263-7863(01)00074-6
HOPKIN, P. (2012): Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Risk
Management(2nd ed.). London: Kogan Page.
HUNYADI, L., MUNDRUCZO, GY. & VITA, L. (1993): Statisztika II. (Statistics II). Budapest: Aula Kiadó.
JORION, P. (1997): Value at Risk: The New Benchmark for Controlling Derivatives Risk. New York:McGraw-Hill.
LIND, M. R.&CULLER, E. (2011): Information project performance: the impact of critical success factors. International
Journal of Information Technology Project Management, 2(4), 14–25.http://dx.doi.org/10.4018/jitpm.2011100102
LOOSEMORE, M., RAFTERY, J., REILLY, C.& HIGGON, D. (2005): Risk Management in Projects. (2nd Ed.)London:
Taylor & Francis. http://dx.doi.org/10.4324/9780203963708
NAKATSU, R. T.& IACOVOU, C. L. (2009): A comparative study of important risk factors involved in offshore and domestic
outstanding of software development projects,a two-panel Delphi study. Information & Management. 46(1), 57–68.
http://dx.doi.org/10.1016/j.im.2008.11.005
OHTAKA, H.& FUKAZAWA, Y. (2010): Managing risk symptom: A method to identify major risks of serious problem
projectsin SI environment using cyclic causal model. Project Management Journal, 41(1), 51–60.
PATAKI L.&TATAI,T.(2008): Kockázatelemzés, kockázatmérséklés cselekvési tervek (Risk Analysis and Risk Mitigation
Action Plans). Budapest: Raabe Kiadó.
34
Risk Evaluation of Strategic Indicators
PROJECT MANAGEMENT INSTITUTE (PMI) (2008): A Guide to the Project Management Body of Knowledge (4th ed.).
Pennsylvania: Project Management Institute.
RÉDEY, Á. (ED.) (2012): Környezetmenedzsment és környezetjog (Enviromental management and law). Veszprém: Pannon
Egyetem.
SUMMER, M. (2000). risk factors in enterprise-wide/ERP projects. Journal of Information Technology, 15(1), 317–327.
http://dx.doi.org/10.1080/02683960010009079
WATCHORN, E. (2007). Applying a Structured Approach to Operational Risk Scenario Analysis in Australia. Sydney:
Australian Prudential Regulation Authority (APRA).
35
Purchase answer to see full
attachment