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Qualitative Operational Risk Software -Development of a Software Tool for Determining the Root of Operational Risk

Dionicio Peña Torres1, Carlos Rodríguez Monroy2, Pablo Solana3, José del Hierro Marqués4

1

Electrical Engineer, Magíster Scíentíarum Industrial Engineer, Business Administration Advanced Degrees. (Nationality: Venezuelan)

2

PhD in Industrial Engineering. Degree in Economics and Business. Degree in Law and Political Science.

ETSII Universidad Politécnica de Madrid. Dir.: C/José Gutiérrez Abascal., 2. 28006. Madrid

-España. crmonroy@etsii.upm.es.

3

PhD in Industrial Engineering.

ETSII Universidad Politécnica de Madrid. Dir.: C/José Gutiérrez Abascal., 2. 28006. Madrid

-España.psolana@etsii.upm.es.

4

System Design.

ETSII - Universidad Nacional de Educación a Distancia, Dir.: Calle Francos Rodríguez,

77-28039 Madrid - España. jhierro3@alumno.uned.es.

Keywords:Operational risks, Value at risk, Software tool.

Category:Technical Paper.

Abstract

This article shows software that allows determining the statistical behavior of qualitative data originating surveys previously transformed with a Likert’s scale to quantitative data. The main intention is offer to users a useful tool to know statistics' characteristics and forecasts of financial risks in a fast and simple way. Additionally, this paper presents the definition of operational risk. On the other hand, the article explains different techniques to do surveys with a Likert’s scale (Avila, 2008) to know expert’s opinion with the transformation of qualitative data to quantitative data. In addition, this paper will show how is very easy to distinguish an expert’s opinion related to risk, but when users have a lot of surveys and matrices is very difficult to obtain results because is necessary to compare common data. On the other hand, statistical value representative must be extracted from common data to get weight of each risk. In the end, this article exposes the development of “Qualitative Operational Risk Software” or QORS by its acronym, which has been designed to determine the root of risks in organizations and its value at operational risk OpVaR (Jorion, 2008; Chernobai et al, 2008) when input data comes from expert’s opinion and their associated matrices.

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“The story of this storm in the global markets is the story of how the government intervened to solve the previous crisis and laying the foundation for a new one”, the government’s mismanagement of the world financial crisis and how we are now dangerously repeating many

mistakes that were made such as: monetary policy, subprime SROLF\ DQG ¿QDQFLDO LQQRYDWLRQV

combined to create the new ZRUVW¿QDQFLDOcatastrophe, (Norberg, 2009).

As you can see, the world's governments do not address suggestions of experts like the Basel Committee and others that intend to make continuous improvements in processes, seeking the root of the problems, which are assumed to have solved the problems in the medium and long term as it would take some time to implement new management models.

Gregoriou (2009), show how applying only "time series" was produced actual crisis because event risks with low frequency and high severity is thinkable to detect only with qualitative data from expert’s opinion or both (qualitative and quantitative data).

2. Structured Abstract 2.1. Purpose

The aim of this paper is to systematically analyze the primary sources of operational risk (OpR) in the industry different to financial sector.

The analysis is based on questionnaires handed over in both management and blue collars, from a methodical system designed by authors of this paper founded on Total Quality Control (Falconi,

1992 a and b; Juran, 1990; Deming, 1989) and applying suggestions fromBank for International

Settlements(BIS, 2011).

The version of Qualitative Operational Risk's Software (QORS) presented herein is primarily based on qualitative methods taken from matrix expert’s decision (Manktelow, 2004) applied when is very difficult the agreement between experts and when its necessary the identification and operational risk assessment (Gregoriou, 2009; Jorion, 2007).

2.2. Design and Methodology

The software is designed following this sequence:

(i) Applying techniques for collecting and analyzing information to identify critical

process areas.

(ii) Importing external qualitative data of survey and sample with managerial and

technical staff.

(iii) Transform qualitative data to quantitative data with a Likert’s scale.

(iv) Selecting automatically common data with Excel to know sources of OpR.

(v) Using of software Visual Basic and Excel as programming language of QOR –

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(vi) Calculating typical statistic data of OpR by Excel for users not experts in statistics and forecasting.

(vii) Integration with others statistics software (SAS, SPSS, Statgraphics Plus, others) for

calculating statistic, goodness test and forecasting of operational risks when the user have a lot of experience in statistics.

2.3. Results

The operational risk’s sources have been likely to identify when applying the guidelines of Basel II and III. The aforementioned sources were detected when applying surveys and interviews to managers, professionals and technician's staff in electric service companies. Previously, similar techniques were applied by one of the authors in banking sector (Peña, 2006), hence is possible to implement the suggested methodologies by BIS to any sort of organization.

QORS allowed studying many of the risk’s sources proposed by BIS such as government policies, human resources and so on. QORS has been fundamental to establish OpVaR.

This software tool allows stratifying sources of operational risks from matrices called "Paired Comparison Analysis" and "Grid Analysis" (Manktelow, 2004) and other simple matrix designed by authors to explain how the frequency and the impact of the data coming from expert’s opinions are caused.

QORS interacts explicitly with other commercial statistical software to help to determine typical statistical values to establish the severity and frequency of OpR and the main ownership comes from the facility to be used by anyone with minimal knowledge of statistics, samples and surveys.

QORS does not intend to displace others sophisticated statistical software, because it only tries to help users when they come up with many arrays, which are very difficult when selecting common data to detect those sources of operational risk.

Furthermore this software selects automatically common data for users with little knowledge, who can realize studies to know the behavior of this operational risk qualitative data then users have having two possibilities to implemented analysis then users have two options for their studies: Excel and from the most sophisticated software’s market (SPSS, SAS and Statgraphics Plus).

2.4. Practical and Social implications

Currently, when people read or listen to financial risk (news) they do not understand the technical language and financial data used. However, these represent an important role for everyone due to the fact that in the global market existing in these times these records affect all aspects in our lives.

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On the other hand this software allows carrying out statistical studies and forecasting from qualitative data risk for people with basic knowledge about it and also for people with high level of knowledge that need to do fast studies about financial risk.

2.5. Originality/Value

It is common when market’s agents need to know about financial risk that studies “time series of financial risk” from data of Fitch (Fitch, 2012), Moody´s (Tudela, 2011), Standard and Poor’s (S&P, 2012) and other qualifications agency or from private data or banking data which has been the main source of data, but after financial world crash in 2008, The BIS change in other hand this studies.

Before the financial crisis, the expert´s opinions were despised, because it was said that is not possible to ensure reliability of these information, but now the financial market has understood that is impossible perceive only with time series, risks with low frequency and high severity and for this main reason change this opinion.

In the market exist lots software for study financial risk with time series, e.g. Palisade

(http://www.palisade.com/) or RiskAMP (http://www.riskamp.com/), but is difficult find good

software when forecasters required analysis from qualitative data.

This software enables to with qualitative data taken from polls of experts opinion, but the principal attribute is likely to put in practice by having a minimum knowledge of statistics.

3. Operational Risk

The formal definition of operational risk that is currently accepted was proposed by the Bank

for International Settlements(BIS, 2011) as follows:

Operational risk is the risk of loss resulting from inadequate or failed internal processes, people or systems, or from external events.

Under the advanced measurement approach of the Basel II & III Accord, banks are required to measure their total annual operational risk exposures (Gregoriu, 2009; BIS, 2011).

4. Techniques for Effective Decision – Making

Manktelow (2004) said that the techniques applied in this software help you to make the best decisions possible with the information you have available. With these tools, you will be able to map out the likely consequences of decisions, work out the importance of individual factors, and choose the best course of action to take.

Some tools known to resolve conflicts or make difficult decisions:

• Selecting the most important changes to make -Pareto Analysis

• Evaluating the relative importance of different options -Paired Comparison Analysis

• Selecting between good options -Grid Analysis

• Choosing between options by projecting likely outcomes -Decision Trees

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• Analyzing the pressures for and against change -Force Field Analysis

• Looking at a decision from all points of view -Six Thinking Hats

• Seeing whether a change is worth making -Cost/Benefit Analysis

xScenario analysis of expert opinion by Basel II & III – Qualitative Operational Risk

Measurement.

In this software we look at decision-making tools in two stages. First, we will look at a set of good techniques that help you to select between different options. The second set helps you to decide whether a course of action is worth following.

In the first development of QQRS it was only considered three methodologies: Paired

Comparison Analysis, Grid Analysis and Qualitative Operational Risk Measurement, due to the fact that this software is a complement of other works. In addition it will be likely to develop other techniques in future versions.

4.1. Paired Comparison Analysis

Manktelow (2004) explain that Paired Comparison Analysis helps you to work out the

importance of a number of options relative to each other. It is particularly useful when you do not have objective data to base this on.

This makes it easy to choose the most important problem to solve, or select the solution that will

give you the greatest advantage. Paired Comparison Analysis helps you to set priorities when

there are conflicting demands on your resources.

To use the technique, compare each option with each other option, one-by-one. For each comparison, decide which of the two options is most important, and then assign a score to show how much more important it is. Figure 1, will show an example of survey with this technique applied an electric company. You can then consolidate these comparisons so that each option is given a percentage importance.

4.1.1. Size of Paired Comparison Analysis

This array compares a quality with others qualities, hence the result of the matrix is square, then: Matrix’s Row Numbers = N

Matrix’s Columns Numbers = N.

After that, the generated matrix is N x N. 4.1.2. Level’s numbers

It is the number of hierarchical levels of the organization. For instance in the Figure 1 as you may notice the levels are 3: direction, division and departments.

4.1.3. Expert’s Number

It is the number of people who complement the polls. The sample taken was 87 experts in the example of the Figure 1.

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Manktelow (2004) clear up that Grid Analysis is a useful technique to use for making a decision. It is most effective where you have a number of good alternatives and many factors to take into account.

The first step is to list your options and then the factors that are important for making the decision. Lay these out in a table, with options as the row labels, and factors as the column headings.

Next work out the relative importance of the factors in your decision. Show these as numbers. We will use these to weight your preferences by the importance of the factor.

These values may be obvious, and if they are not, than use a technique such as Paired Comparison Analysis (see 4.1) to calculate them.

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Applying Paired Com

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Applying Grid Analysis –

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Source:

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Note that you do not have to have a different score for each option. If none of them are good for a particular factor in your decision, then all options should score 0. See Figure 2 to clarify this matrix.

Now multiply each of your scores by the values for your relative importance. This will give them the correct overall weight in your decision.

Finally add up these weighted scores for your options show that the highest percentage is the most risky.

4.2.1. Size of Grid Analysis

The matrix studies activities, processes or what the pollster decides, but the value of risk focuses in the characteristics of these procedures, hence this matrix compares a procedure with another one. For this reason generally the matrix is rectangular:

Matrix’s Row Numbers = M Matrix’s Columns Numbers = N.

After that, the generated matrix is M x N.

4.2.2. Level’s numbers

Mainly, this poll describes the procedure and then it is valued according to preestablished criteria. The number of levels depends on the information that the user wants to describe from the procedures to compare. The example of the Figure 2 shows this array has two descriptive levels.

4.2.3. Expert’s Number

Similar to Paired Comparison Analysis’s case, it is the number of people who complement the polls. In the next example 58 experts were taken to be polled.

4.3. Qualitative Operational Risk Measurement

These are techniques designed by authors from BIS suggestions (Gregoriou, 2009). Basically the authors carried out a deep investigation about internal and external activities, processes, systems and others considerable issues inside the organization including many brainstorms and meetings with managerial and blue collars.

This article describes risks associated and the experts should expose their impressions and opinions and transforming of these with a likert scale predefined about frequency and severity of operational risk detected. Figure 3, shows this technique.

This arrangement is generally dimensioned with M files and N columns, therefore it is a rectangular matrix M x N. It generally has one level where the information provided about the risks and its associated activities are described.

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ID R .O Co n s o l . No m b r e A c t i v i da d y /o Pr o c e s o No m b r e d e l Ri e s g o De s c r i p c i ó n d e l Ri e s g o At r i b u t o C a t e g o r i a Fr e c . Pr o m e d . Im p . Pr o m e d . R1 Es t im a r V e n t a s M e n s ua le s de En e r g ía Es t im a r V e n t a s Me n s u a le s d e En e r g ía E r r one a m e n t e S e pr od u c e por e v e n t o s e xt e r nos o f or t u it os , y a q u e s u oc u r r e nc ia e s t a a s oc ia d a a q u e la s g r a nd e s e m pr e s a s s u s c r ipt or a s no c u mpl e n los pl a n e s y p r o g r a m a s d e c ons u m o d e e n e r g ía , ya s e a por : r e d u c c ión d e s u pr od u c c ión, f lu c t u a c ione s d e s u m e r c a d o, f a ll a s ope r a c iona le s int e r na s , va r ia c ione s e n s u s pl a n e s d e ma nt e n im ie nt o, in c u mpl im ie nt o e n la p la n if ic a c ió n d e e n t r a d a y s a lid a d e s u s e q u ip o s y u n id a d e s d e p r o d u c c ió n , p a r a liz a c ió n d e a c t iv id a d e s e n u n a e m pr e s a ( hu e lg a s ) , r e d u c c ión d e t u r nos d e t r a b a jo, c a mb io s c li m a t ic os , nu e v a s L e ye s o D e c r e t os e n t r e ot r o s . P r áct icas d e c li e n t es , pr od u c t o s y ne g o c ios , n o a d e c ua da s Ex t e r n o 12 3 R2 Es t im a r V e n t a s M e n s ua le s de En e r g ía Es t im a r V e n t a s Me n s u a le s d e En e r g ía E r r one a m e n t e E l pe r s ona l d e P la ni f ic a c ión C o r por a t iv a /C o me r c ia li z a c ió n n o t ie n e e l s u f ic ie n t e a d ie s t r a m ie n t o p a r a r e a liz a r lo s pr onós t ic o s d e v e n t a s m á s a d e c u a d o s y pr e c is os , d e b id o a q u e n o s e t ien e e s t ip u lad o o n o s e cu m p le q u e lo s an al is t a s in ic ia le s d e los pr onós t ic o s r e c ib a n la c a pa c it a c ión a c a d é m ic a r e q u e r id a . E s d e c ir s e d e s c onoc e la p la ni f ic a c c ión por e s c e na r ios , m od e los pr os pe c t vi s t a s y p e r pe c t iv is t a s , e s t u d ios d e s e r ie s d e t ie m po, e s t a c iona li d a d , c ic lo s d e p r o d u c c ión, e s t r a t if ic a c ión d e d a t o s , e s t u d io d e t e nd e n c ia , ot r o s . F a lla s e n la ej ecu c ió n , en t r eg a y g e s t ión d e l pr oc e s o P r oc e s os 7 7 R3 Es t im a r V e n t a s M e n s ua le s de En e r g ía Es t im a r V e n t a s Me n s u a le s d e En e r g ía E r r one a m e n t e L a s b a s e s d e d a t o s d e E xc e l c ont ie ne n d a t os e s pu r e os no id en t if icad o s a d e cu ad am en t e a t r a v e s d e s o f t w ar e e s t ad is t ico s . E s t o s d a t os e r r one os s e pr od u c e n por me d ic ione s ine xa c t a s , pr od u c t o d e la pe r ior ic id a d e n t r e u na y o t r a , d e f ic ie n c ia s e n e l S C A D A y e l S A P , f a ll a s o d e fi c ie n c ia s d e H a rd w a re , in t ra n e t , d a t o s ine xa c t os ob t e ni d o s por vi a s c o mu ni c a c iona le s ( int r a ne t , in t e r n e t , t e le f onos , f a x , o t r os ) . A d ic iona lm e n t e , lo s s o f t w a r e ( ¿C u a l( es ) ? ) d e p r onos t ic o s y ma ne jo s e s t a d is t ic os no s o n los má s id one os o a c t u a li z a d os pa r a e s t ima r l a s ve nt a s . F a lla s e n la ej ecu c ió n , en t r eg a y g e s t ión d e l pr oc e s o T e c nol og ic o 44 R4 Es t im a r V e n t a s M e n s ua le s de En e r g ía Es t im a r V e n t a s Me n s u a le s d e En e r g ía E r r one a m e n t e N e g li g e n c ia , d e s a t e nc ione s , f a lt a d e r e vi s ión d e t a ll a d a pr od u c e r e vi s ione s e r r óne a s e n e l c ont r o l d e la g e s t ión d e d a t o s . P e r s ona l ins u f ic ie nt e r e s pe c t o a l vol u m e n d e t r a b a jo. O m is ió n d e d a t os im por t a nt e s , int e n s ione s poc o c la r a s e n la e la b or a c ión d e l a s e s t ima c ione s . F a lla s e n la ej ecu c ió n , en t r eg a y g e s t ión d e l pr oc e s o R ecu r s o s H u ma nos 14 2 Figure 3.

Applying Qualitative Operationa

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5. Development of Qualitative Data Operational Risk Software

Qualitative Operational Risk Software 1.0.xlsm (QORS 1.0), is a software developed under Excel 2010 and is based on VBA (Garcia, 2004).

VBA is a language object-oriented, generally used to support Office’s macros and in this case in particular Excel. These languages have shown much versatility to calculate with financial risk’s qualitative data and the risk’s event require for the users. Moving onto how the software works and its internal logic:

5.1. Starting Qualitative Operational Risks Software

Once started the execution, QORS 1.0 shows a graphic of qualitative data in three-dimensional convolution from a real case that is shown in Figure 4. The chart’s movements are the result of the consecutive and overlap presentation in the same point of a risk’s graph which has been done

under the function LoadPicture(Microsoft, 2012a).

Figure 4.First Screen on QORS

Source:Authors (2012)

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Tabla I.LoadPicture function syntax

Part Description

filename Optional. String expression specifying a filename. Can include folder and drive. If no filename is specified LoadPicture clears the Image or PictureBox control.

size Optional variant. If filenameis a cursor or icon file, specifies the desired image size.

colordepth Optional variant. If filenameis a cursor or icon file, specifies the desired color depth.

x Optional variant required if yis used. If filenameis a cursor or icon file, specifies the

width desired. In a file containing multiple separate images, the best possible match

is used if an image of that size is not available. X and y values are only used when

colordepthis set to vbLPCustom. For icon files 255 is the maximum possible value.

y Optional variant, required if xis used. If filenameis a cursor or icon file, specifies

the height desired. In a file containing multiple separate images, the best possible

match is used if an image of that size is not available. For icon files 255 is the

maximum possible value.

Source:Microsoft (2012a)

The LoadPicture was designed in BMP format from several points of view. A BMP file is a bitmap file, i.e. a graphic image file, with pixels stored in the form of point table and managing

the colors either as true colors or using an indexed palette. The BMP format has been studied in

such a way as to obtain a bitmap that is independent of the peripheral display device (DIB,

Device independent bitmap).

The structure of a bitmap file is the following: File header, Bitmap information header (also called information Header), Palette, The image body. (Lancaster, 2011).

The figure 5 shows how the graphic has completed an entire rotation; it automatically disappears to show the types of studies which develop the application. These are:

9 Techniques for Effective Decision Making

(13)

Figure 5.Screen of Option Studies

Source:Authors (2012)

The buttons in the Figure 5, which are shown as options mode, are functions called

CommandButton(Simon, 2002), that allow identifying the user’s decision.

As I said before, even though VBA (an object-oriented language) is employed, it is explained the procedure of the developed techniques, using a structural method, which was used to endow the entire procedure of a sequentially, which allows the user to understand the development of QORS.

The Figure 6 presents the QORS’s internal logic flowchart with possible sub-orders when the program is started.

QORS 1.0 only includes the possibility of realize some analysis of the following tools (Manktelow, 2004):

x Paired Comparison

x Grid Analysis

(14)

Objective:

Design by: Inspected by: Eng. MSc. Dionicio Peña T.

Approved by:

PhD. Carlos Rodríguez M./ PhD. Pablo Solana

Last Edition: Page:

Notes

April 27, 2012 Show flowchart of QORS

1 of 1 Start

Option Techniques

Effective Decision

Main Menul

Pareto Analysis Decision Tree Analysis

Plus Minus

Implication Six Thinking Hats

Cost/Benefits Analysis

José del Hierro Marqués

Paired

Comparision Grid Analysis Qualitative

Operational Risk Measure

Risk Process

Paired Process

Grid Process

End (1)

(1) FRM Technics

(2) To development in the next version

(2) (2) (2) (2) (2)

Would you realice a new study?

NO

YES

Delete old data of before studies Do you like

continue some study with old data?

NO

YES

Figure 6.Logic Flowchart of QORS

Source:Authors (2012)

5.2. Helping to Users

The software provides help to the user with in order to clarify the matrix’s basic concepts implemented in qualitative polls to identify the operational risks. This option is identify with the

question mark and is objects like type IMAGE, which once pulsed by the users it will allow them

to obtain the basic information regarding to the different polls likely to study by using this tool.

Figure 7, shows how this help is an object like UserForm with an object Label type, which is

included in execution’s time in function of selected option (Microsoft, 2012b).

A UserForm object is a window or dialog box that makes up part of an application's user interface.

The UserForms collection is a collection whose elements represent each loaded UserFormin an

application. The UserForms collection has a Count property, an Item property, and an Add

method. Count specifies the number of elements in the collection; Item (the default member)

(15)

Figure 7.Helping to Users

Source:Authors (2012)

The figure 8 presents how the frmClean (Paul, 2002) has been created in order to delete remained

data from previous studies, because it is important to have patterns in white at the beginning of any study, so the format and the patterns used differ substantially within techniques.

Figure 8.FrmClean

(16)

This is an object type User Form (Microsoft, 2012b) which is responsible to eliminate all the data from previous executions and leaves the application in its original state.

5.3. Selecting to Techniques to Effective Decision

The figure 9 shows the next image when the option Techniques to Effective Decisionis selected

with the diverse techniques of analysis available according to Figure 5, which can be executed by pulsing the appropriate button.

Figure 9.Selecting to Techniques to Effective Decision

Source:Authors (2012)

In the previous figure is observed that beside each of the options, help is presented again with a deeply explanation of the developed techniques. Software apply system 5W + 1H (What, Who, When, Where, Which and How) to resolve studies included for users.

5.3.1. Paired Comparison Analysis

If the user decides to pulse the button Paired Comparison, the QORS will assess the respective

(17)

Objective:

Design by: Inspected by:

Eng. MSc. Dionicio Peña T.

Approved by:

PhD. Carlos Rodríguez M./ PhD. Pablo Solana

Last Edition: Page:

Notes

April 27, 2012 Show flowchart of Paired Comparision Analysis

1 of 1 José del Hierro Marqués

Paired Process

Paired

Configuration Matrix Size Level’s Number Produce Sample Template

Is it set well? NO

YES

Do you require some statistics Analysis?

Do you need a basic statistics Analysis? Start

Do you like return to another QOR study?

Import External

Data

QORS End

YES

YES

YES NO

NO

NO Transform

data (4)

Statistics Analysis by SAS

Statistics Analysis by SPSS

Statistics Analysis by Statgraphics

Basic Statistics Analysis by Excel

(1) (2) (3)

Export Internal

Data (5)

(3) Search qualitative surveys of expert in a personal data or organizational data under study and then attach in this software (4) Transform data from each template in common column data

(5) Export result data of this software for other sophisticated software if necessary when required more deep statistics studies (1) In this process is possible including only square matrix with equal number of rows and

columns or n x n

(2) Including levels inside of organization in study i.e. including different levels of management inside the company

Figure 10.Logic Flowchart of Paired Comparison Analysis

Source:Authors (2012)

Considering the previously describe internal logic, Figure 11 displays how the users are

interrogated by the software to include descriptive data from paired comparison analysis and

how the arrays is internally generated likewise Figure 1 that will be imported from their original files.

Subsequently the matrix data to include QORS 1.0 generates a pattern sheet for all the polls which had been executed. After including the number of polled experts, it will produce as many patterns as the number of polled experts.

Moreover, the user will be able to import all of the polls from any excel’s book, obviously with the prerequisite that the matrixes have the same features as the generated patterns.

The user will be able to go backwards as many times as his/her considers necessary to the setting form to modify any of the matrixes previously introduced.

(18)

Figure 11.Forming a Paired Comparision Analysis

Source:Authors (2012)

As you may notice, this procedure starts again with a call to the form Userform’s type, which

allows obtaining from the user all the variables previously described.

Figure 12.Transforming Data

(19)

The figure 12 shows the following possible execution from a new CommandButton called

Transform Data, which is presented beside the button Paired Comparison. In the Figure 13 the way that QORS execute internally its sequence is shown.

Proceso:

Objective:

Design by: Inspected by:

Eng. MSc. Dionicio Peña T.

Approved by:

PhD. Carlos Rodríguez M./ PhD. Pablo Solana

Last Edition: Page:

Notes

April 29, 2012

E:Transform Data

Show flowchart of QORS where internally transform all templates in only one sheet with common data per columns

1 of 1 Start

José del Hierro Marqués

(1) Value (Pos + 1, Cell) = Value (Rows, Columns) Save positions of writing and reading data sheets

Value (Pos, Cell):= Value (Rows, Columns)

Running and saving all data

sheet (1)

Answer (Pos+1, Cell):=

Value (Rows, Columns) Sheet:= Sheet + 1

Software will required more

sheets?

NO YES

Sheet:= 1, Pos:= 1 Cell:= Cell + 1

Column := 1 Column := Column +1

Do you have value (Row, Column) > 0?

NO

YES

Sheet:= 1, Pos:= 1 Cell:= Cell + 1

Row := 1 Row := Row +1

Do you have value (Row, Column) > 0?

NO

YES Don´t exist more sheets

Column finished

Are you conform with transform data?

NO YES

Row finished

End

Figure 13.Logic Flowchart of Transform Data

Source:Authors (2012)

By pressing this new bottomCalculation Paired Comparison, QORS 1.0 internally executes the

calculation´s process which is shown below

- Elimination of the previous execution´s calculations, in the case there are. On the contrary

it added to the end of the answer sheet

If (Sheets(Sheets.Count).Name = "Analisis") Then Sheets(Sheets.Count).Select

Selection.Delete Else

Sheets.Add After:=Sheets(Sheets.Count) ActiveSheet.Name = "Analisis"

(20)

- Below, the process is automatically positioned in the first data sheet with the next internal execution.

Sheets(2).Select

- Subsequently QORS records in memory the starting location of data to the lecture

( Variables, Rows and Column) and writing in the results sheet (Position and Cells) with the next commands:

Range(ComienzoDatos).Select 'Posicion de lectura origen Fila = ActiveCell.Address Columna = ActiveCell.Address Sheets(Sheets.Count).Select Range("A1").Select

'Posicion de escritura destino Pos = ActiveCell.Address Celda = ActiveCell.Address

This sequence allows extracting data from each paired comparison and the aforementioned are exported to a new arrangement where the common data are positioned one after the other in the same column, how it is shown in the Figure 14:

Proyectos de Mejoras de Generación

Mantenimiento Eléctrico

Mantenimiento Mecánico

Ingeniería de

Mantenimiento Operaciones

Mantenimiento de Control e Instrumentación

Servicios Generales

Protecciones y Mediciones de Generación

Centros de Control de Generación

Mantenimiento Eléctrico

Mantenimiento Mecánico

Ingeniería de

Mantenimiento Operaciones

Mantenimiento de Control e Instrumentación

Servicios Generales

Protecciones y Mediciones de Generación

Centros de Control de Generación

5 4 4 4 3 4 3 4 4 3 3 3 2 3 2 3 4

3 4 3 3 3 3 3 2 2 3 3 3 2 3 2 2 2

3 2 2 3 2 2 3 3 3 2 2 3 2 2 3 3 3

3 4 4 4 4 3 4 4 4 3 3 3 3 4 4 5 5

4 4 4 4 3 4 3 4 4 4 4 4 4 4 3 4 4

3 4 4 4 2 3 4 4 4 3 3 3 3 4 4 5 5

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

5 2 2 2 2 2 3 2 2 2 2 2 2 2 3 2 2

4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

4 4 4 4 2 4 4 3 2 3 3 4 2 4 4 3 2

4 4 4 4 3 4 3 4 4 3 3 3 2 3 2 3 3

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

5 1 1 3 1 4 5 5 5 1 1 1 1 1 2 2 2

2 1 1 1 1 1 2 2 2 1 1 1 1 1 2 2 2

3 3 3 3 3 3 4 3 3 3 3 3 3 3 4 3 3

3 5 5 5 5 5 5 3 3 3 3 3 2 3 3 3 3

2 2 2 2 2 2 5 2 2 3 3 3 3 3 5 4 4

3 2 2 3 2 3 4 3 3 4 4 2 1 2 4 2 2

3 3 2 3 3 3 2 2 2 3 3 3 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

2 2 2 2 2 2 4 3 3 2 2 2 2 2 4 2 2

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4

3 3 2 3 3 3 2 2 2 3 3 3 2 2 2 2 2

3 4 4 2 4 4 2 3 3 4 4 3 4 4 3 3 3

2 2 1 2 1 2 4 1 2 1 1 2 1 1 4 1 2

4 4 4 4 4 4 2 4 4 4 4 4 4 4 2 4 4

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4

4 4 4 4 4 4 2 4 4 4 4 4 4 4 2 4 4

4 4 4 4 4 5 2 2 3 3 2 1 2 5 2 3 4

2 2 2 2 2 1 3 2 2 2 2 2 2 2 3 2 3

3 3 3 5 2 3 3 3 3 3 4 5 2 3 3 3 3

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4

3 3 2 2 2 3 3 2 3 3 3 3 2 2 2 2 2

3 4 4 2 2 2 4 2 2 2 3 3 2 3 3 2 2

2 2 2 3 2 2 3 2 2 2 2 2 2 2 3 2 2

4 4 4 2 2 2 4 4 4 3 3 3 3 3 4 2 4

3 3 3 3 3 3 3 3 3 3 3 4 4 3 3 2 2

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4

3 4 4 4 4 4 2 4 4 4 4 4 4 4 2 4 4

1 2 1 1 2 1 1 3 3 3 1 1 2 1 1 3 3

3 3 3 4 4 3 2 3 3 3 3 4 4 3 2 3 3

2 2 2 3 2 2 3 3 3 2 2 3 2 2 3 3 3

3 2 2 3 2 3 3 3 3 1 1 3 2 2 3 3 3

3 1 1 3 1 1 2 2 1 1 1 3 1 1 2 2 1

Investigaciones y Pruebas de Generación Vs Proyectos de Mejoras de Generación Vs

Figure 14.Transformed data from square matrix to common data on columns arrays

(21)

Obtained the previous arrangement how is shown in the figure 10, the user will have 2 statistical

options of study: 1 Basic Statistics Analysis by Excel or 2. Sophisticated Statistics Analysis by

others software.

5.3.1.1. Basic Statistics Analysis by Excel

At the end of the previously ordered arrangement (Figure 14), if the user pulses another

CommandButton he/she will obtain basic statistical data such as average, mean, mode and standard deviation likewise box and whisker graphs, histograms, scatter plots and so on; thus it is able to detect additionally which is the representative statistical value of his sample.

5.3.1.2. Sophisticated Statistics Analysis by others software

It was observed that in the Figure 10 the user additionally will be able to export the obtained data (Figure 13) to more sophisticated statistical software and forecast. In the QORS´s 1.0 version will be possible to export to SPSS, SAS and Statgraphics Plus.

This option is presented due to the fact is likely the user wants to obtain results even more confident than the ones obtained under Excel.

Independently on which of the via the user takes, the Paired Comparisonprocess demands to get

a statistical value which represents compared ordered pair and graphic features displayed in the Figure 16.

The statistical value was chosen by the user many times until the matrix is completed again with them. The array is reorganized (In case that the user choses the option by using Excel, QORS 1.0 updated automatically) and a new arrangement is obtained as the shown in the Figure 15.

1 2 3 4 5

Dirección

División

Dirección División Departamento

Investigaciones y Pruebas de Generación

Proyectos de Mejoras de Generación

Mantenimiento Eléctrico

Mantenimiento Mecánico

Ingeniería de Mantenimiento Operaciones

Mantenimiento de Control e Instrumentación

Servicios Generales

Protecciones y Mediciones de Generación

Centros de Control de Generación Investigaciones y

Pruebas de Generación

0,000 2,983 2,500 2,000 2,776 2,431 2,655 2,914 2,793 2,810 53

Proyectos de Mejoras de Generación

2,017 0,000 3,000 3,000 2,638 2,310 2,500 2,776 2,586 2,793 52

Mantenimiento

Eléctrico 2,500 2,000 0,000 3,000 3,155 3,000 3,017 3,534 3,293 3,207 59 Mantenimiento

Mecánico 3,000 2,000 2,000 0,000 2,000 3,034 3,069 3,655 3,448 3,466 57 Ingeniería de

Mantenimiento 2,224 2,362 1,845 3,000 0,000 2,707 2,724 3,379 3,121 3,155 54 Operaciones 2,569 2,690 2,000 1,966 2,293 0,000 3,259 4,000 3,500 3,655 58

Mantenimiento de Control e Instrumentación

2,345 2,500 1,983 1,931 2,276 1,741 0,000 3,397 3,362 3,397 51

Servicios

Generales 2,086 2,224 1,466 1,345 1,621 1,000 1,603 0,000

2,000 2,000

34

Protecciones y Mediciones de Generación

2,207 2,414 1,707 1,552 1,879 1,500 1,638 3,000

0,000 2,862

42

Centros de Control

de Generación 2,190 2,207 1,793 1,534 1,845 1,345 1,603 3,000

2,138 0,000

39 No relevante

Poco Relevante Medianamente Relevante

Relevante Muy Relevante

% Peso/Total

Producción

Ingeniería de Mejoras de Generación

Planta Guri

Protecciones,Super visión y Control de Generación

Producción

Ingeniería de Mejoras de Generación Planta Guri Protecciones,Supervisión

Figure 15.Paired Comparison Analysis obtained

(22)

Figure 16.Graphic features of random variables obtained

(23)

The new detected column is the resultant from the average of each ordered pair, obtained after proving that is a normal value. The biggest resultant weight will be the area studied with the

highest operational risk. In the Figure 15, the most risky areas can be seen: “Mantenimiento

Eléctrico” and “Operaciones”.

To conclude the Paired Comparison Analysis can be used in any field. Although some authors have considered it as a powerful tool even more when there are immense organizations such as the electrical sector´s companies, which has been exemplifying.

The sample´s company presented an organization chart with 8 directions, 22 divisions and 45 departments. The 45 departments were compared among them, therefore a 45X45 matrix was produced and as a result 960 ordered pairs.

The figure´s 15 matrix is the recurrent application following Pareto´s principle 80/20, until reaches the area which generates the mayor quantity of operational risk in the organization.

5.3.2. Grid Analysis

As observed in the figure 9, this is one of the others study´s alternatives which presents QORS 1.0, it has been founded in the presented theorist base in the section 4.2 of this article. In the Figure 17 the internal logic that executes the software for this analysis is shown:

Proceso: Objective:

Design by: Inspected by:

Eng. MSc. Dionicio Peña T.

Approved by:

PhD. Carlos Rodríguez M./ PhD. Pablo Solana

Last Edition: Page:

Notes

April 27, 2012

C:Grid Analysis

Show flowchart of Grid Analysis

1 of 1 José del Hierro Marqués

Grid Analysis

Grid Analysis Matrix Size Characteristics

and Qualities

Produce Sample Template

Is it set well? NO

YES

Do you require some statistics Analysis?

Do you need a basic statistics Analysis? Start

Do you like return to another QOR study?

Import External

Data

QORS End

YES

YES

YES NO

NO

NO Transform

data

(3) How many surveys are you include in this analysis,?, How many experts?

(4) Search qualitative surveys of expert in a personal data or organizational data under study and then attach in this software (5) Transform data from each template in common column data

(6) Export result data of this software for other sophisticated software if necessary when required more deep statistics studies (5)

Statistics Analysis by SAS

Statistics Analysis by SPSS

Statistics Analysis by Statgraphics

Basic Statistics Analysis by Excel

(1) (2) (4)

Export Internal

Data (6)

(1) In this process is possible including only matrix with different number of rows and columns, i.e. m x n

(2) Including characteristics and qualities of the processes under study then you need explain and what do you need know and contrast between processes?

Including Number of Sample Template

(3)

Figure 17.Logic Flowchart of Grid Analysis

(24)

QORS´s 1.0 internal sequence to make this analysis is similar to the implemented in Paired Comparison Analysis, but it will take the theoretical considerations of section 4.2 among which stands out that for the Grid Analysis. The matrix is M x N and includes explicative alphanumeric data, not only does the software discriminate internally the letters from the numeric data, but also it selects the numeric data. After executing all the commands an arrangement will be obtained similar to the one shown in the Figure 18.

The last column is the resultant of the average in each studied process, which is obtained after proving that it is a normal value. The resultant weight will be bigger than the area o studied

process with bigger operational risks. The Figure 18 shows the processes “Ejecutar el

mantenimiento” and “Ejecutar las operaciones” are the most risky, which allows Paired Comparion Analysis to confirm that the operational risk´s initial detection in this organization must be oriented to the maintenance and operations department.

Likewise Paired Comparison Analysis, the grid analysis can be compared with either any company or daily routine, but in the example showed, the authors have considered to deepen in the studied productive processes of an electric corporation. Initially, brainstorms were created to describe the processes and later numerically ranked by those skilled.

5.4. Qualitative Operational Risk Measurement

It was presented in the figure 9 how these matrices are structured and as you may notice QORS

1.0, will do an analysis similar to Paired Comparison Analysisand Grid Analysis, but in this case

the random variables to study are the frequency and loss. As a consequence the probability´s distribution will be studied. After that it will be obtained by applying the Montecarlo´s method (Sobol, 1974) an operational risk´s value approximation.

Monte Carlo simulation in MS Excel is based on the generation of multiple trials to determine the expected value of a random variable (Jeges, 2012). The basis of the method is provided by the following relationship:

% 8 . 99 3

1 |

¿ ¾ ½ ¯

® ­

¢

¦

N N

P

N r

V P [

There are a number of commercial packages that run Monte Carlo simulation, however a basic spreadsheet program can be used to run a simulation. In this case the generation of multiple trials is implemented by propagating a basic formula as many times as the number of iterations required by the model. In the Figure 19 the internal logic that executes the software for this analysis is shown.

A real studied case by the electric´s company authors was presented in the Figure 3, from which

it is extracted the risk number 35 (R35) called: “No desincorporar activos fijos” therefore a

process is described to be implemented from QORS.

(25)

1N o r el ev a nt e 2 Poc o R el ev a nt e 3 M ed ia na m ent e Re le va nt e 4 Rel ev a nt e Di recci ón d e P rod ucci ón 5M uy R el ev a nt e N om b re d el P roces o Di recci ón Po stu la nt e Des cr ip ci ón P roces o Á re a s i nv ol ucr a d a s D es cr ip ció n d el im p a ct o en ca so d e ocu rre nc ia rd id as fi nanc ie ra s a soci a d a s a l p roces o Recl a m os d e cl ie nt es (in ter no

s y ex

te rn os ) M ult a s o C a stig os Af ec ta ció n a la co nt inui dad de l ne go ci o Re le va nc ia d en tro d el pl an e str at ég ic o Af ec ta ció n d e la im a g en de la O rgani za ci ón Punt uac n C ol oca r el nom b re d el p roces o a l cu a lcon sid er a a p lic a r e l S G RO No m b re d e la Di recci ón q ue po stul a el p roces o Br ev e d es cr ip ci ón so b re el p roces o In di ca r las di sti nt a s ár ea s d e la O rg a ni za ci ón q ue in te rv ie ne n en la ej ecu ci ón d el p roces o, d e pr in ci pi o a fin; es M enc io nar lo s di sti nt os im p a ct os q ue p ud ie se n ge ne ra r las rd id as en ca so d e ocu rri r el p roces o tie ne o p ud ies e ten er Im p a ct o ne g a tiv o ex p res a d o en rm in os m on eta rio p ro d uc to d e fa lla s en la co nt inui dad y ca lidad de l se rv ic io com o res ul ta d o el p roces o tie ne recl a m os p or p a rte d e cl ie nt es in te rn os y ex te rn os Si el p roces o se en cu en tra ex p ues to a M ul ta s o sa nc io ne s a nt e la oc ur re nc ia de al gún ries g o op er a tiv o la oc ur re nc ia d e un ries g o op er a tiv o a fect a ría la con tin ui d a d d el p roces o La im p or ta nci a q ue tien e el p roces o d en tro d e la p la nif ic a ció n es tra g ic a de la O rgani za ci ón ca us ar ía daño s a la re put a ci ón o im age n d e la O rg a ni za ci ón El v a lo r o b te ni do de 2 Fo rm ul ar E st rat eg ia s y Line a m ie nt os D iv isió n P la nt a Gu ri D et er m in a r lo s n iv ele s d e d em a nd a d el se rv ic io y co n bas e e n l a c a pac idad in st al ada de la pl an ta , s e es ta b lece l a ca nt id a d d e D iv d e P la nif ic a ció n Co rp or at iv a, Di v.

de Com

er cia liz a ció n y Se rv ic io a l C lie nt e Pé rd id as al ca nz adas d eb id o a in te rru p cio ne s d el n eg oc io p or fa lla s e n lo s sist em a s, t ec no lo g ía o in fra est ru ct ur a . P ér d id a s 3, 83 3, 28 2, 75 3, 55 4, 20 3, 67 71 6 Eje cu ta r la O p er a ció n De par tam en to d e O p er a cio ne s de P lan ta Gu ri Ej ec ut a r la s m a ni ob ra s op er at iv as e n coor d in a ci ón c on la D iv isió n d e O p er a cio ne s so b re lo s e q ui p os, sist em a s e in st a la cio ne s d e p rod uc ci ón a soc ia d a sa la De par tam en to de O p er a cio ne s P la nt a G ur

i y

lo s De par tam en to s de Ma nt en im ie nt o d e Pl a nt a G ur i: El éct ric o Ser vi ci os Pé rd id as al ca nz adas d eb id o a in te rru p cio ne s d el n eg oc io p or fa lla s e n lo s sist em a s, t ec no lo g ía o in fra est ru ct ur a .P ér d id a s oc a sio na d a s p or fa lla s e n el p roces a m ie nt o d e la s 4, 47 4, 51 3, 66 4, 20 4, 50 4, 29 85 0 10 Ej ecu ta r el M a nt to . De par tam en to s

de Ma

nt en im ie nt o: El éc tr ic o, Se rv ic io s G en er a le s, Co nt ro l e In st ru m en ta ció

n, Ing

en ie ría d e M a nt en im ie nt o Re a liz a r la s a ct iv id a d es d e m a nt en im ie nt o a lo s e q ui p os, sist em a s e in st a la cio ne s d e la C en tr a l Hi dr oe lé ct ric a Si mó n Bo lív a r, d e a cu er d o c on lo s p a rá m etr os e sta b le ci d os po r l a e m pr es a. De par tam en to de O p er a cio ne s P la nt a G ur

i y

lo s De par tam en to s de Ma nt en im ie nt o d e Pl a nt a G ur i: El éc tr ic o, S er vic io s G en er a le s, C on tr ol e I nst ru m en ta ció n, In g en ie ría d e M a nt en im ie nt o y Pér d id a s p rod uc to d e ac to s f rau du le nt os in te nc io na le s, o d e r ob o q ue in vo lu cr a a t e rc er o s , a s í c o mo e n i n c u mp limi e n to d e le ye s y p o lít ica s i n te rn a s de l a em pr es a. Pér d id a s or ig in a d a s p or d a ño s a lo s a ct iv os f ísic os d e la em p res a , p rod uc to d e d esa st re sn a t 4, 58 4, 43 3, 90 4, 57 4, 63 4, 55 89 12 Ev a lu a r e l M a ntto . De par tam en to s

de Ma

nt en im ie nt o: El éc tr ic o, Se rv ic io s Ge ne ra le s Ev a lu a r lo s r esu lta d os d el m a nt en im ie nt o d e lo s e q ui p os, sist em a s e in st a la cio ne s d e la C en tr a l Hi dr oe lé ct ric a Si mó n Bo lív a r a fin d e d et ec ta r De par tam en to s de Ma nt en im ie nt o d e Pl a nt a G ur i: El éc tr ic o, S er vic io s G ene ra le s, C ont ro l e In str um en ta ci ón Pé rd id a s o ca sio na d a s p or fa lla s e n e l p ro ce sa m ie nt o d e la s t ra nsa cc io ne s o g est ió n d e lo s p ro ce so s, p ro ve nie nt es d e la s re la ci on es co m er cia le s 3, 48 3, 47 2, 57 3, 27 3, 75 3, 44 67 0 13 G e s ti o n a r e l RRHH Ge re nc ia de Re cu rs os Hu man os C oor d in a , a d m in ist ra y lle va a c a b o e l se g uim ie nt o d e la ej ecu ci ón d e l a s es tr a te g ia s d el d esa rro llo de l pe rs on al , co nj un ta m en te co n l os P re s id en c ia, J un ta D ir e c ti va . Ge re n c ia s d e : RRHH, C o n s u lto rí a Ju rí d ica , L ici ta ci o n e s , Ge s ti ó n A m b ie n ta l, As u n to s Pú b lic o s , A u di to ri a I n te rn a; Ce n tr o d e In ve s ti gac io n e s Pé rd id a s o ca sio na d a s p or fa lla s e n e l p ro ce sa m ie nt o d e la s t ra nsa cc io ne s o g est ió n d e lo s p ro ce so s, p ro ve nie nt es d e la s re la ci on es c om er cia le s con cl ie nt es , p rov eed or es , 3, 00 2, 76 2, 39 2, 88 3, 79 3, 30 60 14 G e s tio n a r l a s A d q u is ic io n e s y e l A lma c e n a mi e n to Di v. de A d m in ist ra ci ón A seg ur a r q ue l os p roces os de c omp ra

s de

mat er ial es y b ien es , con tr a ta ci ón d e ob ra s y se rv ic io s r eq ue rid os p or su s u nid a d es or g a ni za tiv a s. A sim ism o p a ra lo sp ro ce so sd e y C on s u ltor ía J ur íd ica , Li c it a c ion es , A u di to ri a In te rn a; D ir e c c ion es : P la n if ic a c ió n , F ina nz a s y A d mi n is tra c ió n , S e rv ic io s , T e le m á ti c a , Pr o ye c to s d e Pé rd id as al ca nz adas d eb id o a in te rru p cio ne s d el n eg oc io p or fa lla s e n lo s sist em a s, t ec no lo g ía o in fra est ru ct ur a .P ér d id a s oc a sio na d a s p or fa lla s e n el p roces a m ie nt o d e la s 4, 15 3, 49 2, 93 3, 97 3, 40 3, 34 71 D et a lle s d el p ro ce so C rit er io s p a ra je ra rq ui za r el p roces o d e a cu er d o a su im p a ct o p or : N úm ero C orre la tiv o d e Pr oces o PR O C ESO S D E G EST N OP ER A R E L S IS TE M A D E P ROD UCCIÓN D E E N ER GÍA E C TR ICA MA N TE N ER E L S IS TE M A D E P ROD UCCIÓN D E E N ER GÍA E C TR ICA PR O C ESO S D E A PO YO Figure 18.

Grid Analysis obtained

Source:

(26)

Proceso: Objective:

Design by: Inspected by:

Eng. MSc. Dionicio Peña T.

Approved by:

PhD. Carlos Rodríguez M./ PhD. Pablo Solana

Last Edition: Page:

Notes

April 27, 2012

D:Qualitative Operational Risk Measurement Show flowchart of QORM

1 of 1 José del Hierro Marqués

QORM

QORM Matrix Size Characteristics and Qualities

Produce Sample Template

Is it set well? NO

YES

Do you require some statistics Analysis?

Do you need a basic statistics Analysis? Start

Do you like return to another QOR study?

Import External

Data

QORS End

YES

YES

YES NO

NO

NO Transform

data

(3) How many surveys are you include in this analysis,?, How many experts?

(4) Search qualitative surveys of expert in a personal data or organizational data under study and then attach in this software (5) Transform data from each template in common column data

(6) Export result data of this software for other sophisticated software if necessary when required more deep statistics studies (5)

Statistics Analysis by SAS

Statistics Analysis by SPSS

Statistics Analysis by Statgraphics

Basic Statistics Analysis by Excel

(1) (2) (4)

Export Internal Data (6)

(1) In this process is possible including only matrix with different number of rows and columns, i.e. m x n

(2)Including characteristics and qualities of the sub processes of the process with most highest operational risk inside organization under study then you need attach templates with qualitative expert´s opinion about frequency and severity of risks

Including Number of Sample Template

(3)

Figure 19.Logic Flowchart of Qualitative Operational Risk Measurement

Source:Authors (2012)

Weibull, gamma, beta, Pareto, Burr, and mixture distributions were considered to loss distribution (Jorion, 2008).Several studies have been revised from the recent literature on the empirical findings with operational loss data. In the electric company in study, Weibull distributions are best describing operational loss magnitudes and Poisson are best describing frequency distribution.

R35 is adjusted to a Poisson´s distribution to the parameterized frequency Ȝ = 19.13. The loss

was designed for a lognormal distribution parameterized ȝ = 9.07 and ı = 0.999. Montecarlo´s

simulation is used to find a sample of 1.000.000 observations from the Loss Distribution Approach (LDA) that it is a convolution of the frequency and loss or impact.

(27)

Figure 19.Loss Distribution Approach and OpVaR

Source:Authors (2012)

6. References

Avila, Hector (2008), “Introducción a la Metodología de la Investigación”.Edición Electrónica.

Available via: http://www.eumed.net/libros/2006c/203/index.htm.Chihuahua – México.

BIS (2011), “Sound Practices for the Management and Supervision of Operational Risk”.

Available via: http://www.bis.org/

Chernobai, A., Rachev, S., & Fabozzi, F., (2007). Operational Risk (A Guide to Basel II Capital

Requirements, Models and Analysis),John Wiley & Sons Inc, 4thEdn, USA.

Deming, W. Edwards, (1989). Calidad, Productividad y Competitividad: La Salida de la Crisis,

Diaz de los Santos Edic, Madrid, Spain.

Falconi C., Vicente, (1992a). Control de la Calidad Total (Al Estilo Japonés). Ediciones QFCO,

II Edic, Belo Horizonte – Brazil.

Falconi C., Vicente, (1992b). Calidad – Gerenciamiento de la Rutina del Trabajo Cotidiano.

Ediciones QFCO, II Edic, Belo Horizonte – Brazil.

Fitch, (2012), “Fitch - Complete Sovereign Rating History”,

http://www.fitchratings.com/jsp/sector/Sector.faces

García S., José P. (2004), “Manual de Visual Basic para Excel”, Proyecto Europa - Universidad Politécnica de Valencia.

Referencias

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