Crystal Ball La Biblia
July 25, 2020 | Author: Anonymous | Category: N/A
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2. INTRODUCCION !
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4. ABSTRACT -:
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5. OBJETIVOS 5.1.
OBJETIVO GENERAL
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8. CONCEPTOS BÁSICOS 8.1. ! )
¿Qué es riesgo? 7
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Ilustración 1. Ejemplo de Cristal Ball en MS Excel
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1
¿Qué sucede durante una simulación? 7 8 = =
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¿Qué es la simulación Monte Carlo? 7
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8.5. De donde obtiene la Simulación Monte Carlo su nombre? 7
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¿Cómo analizar los resultados de una simulación?
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Ilustración 2. Cuadro de diálogo - Define Forecast
8.7. ! =
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8.7.1. Grafica de sensibilidad: %
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8.7.2. La gráfica de sobreposición: (
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8.8. ¿Cuales son los beneficios de realizar un análisis de riesgos con Crystal Ball 2000? !
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¿Qué es Optimización?
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9. VERSIONES (
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10. REQUERIMIENTOS DE HARDWARE •
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11. REQUERIMIENTOS DE SOFTWARE %
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12. COMO USAR CRYSTAL BALL 12.1.
¿QUÉ HACE CRYSTAL BALL?
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12.2.
¿Cómo abrir el programa?
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Ilustración 3 Barra de tareas ejecutando Crystal Ball
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Ilustración 4 Ubicación de Crystal Ball en el menú Inicio
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1
¿Cómo Crystal Ball mejora Excel?
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12.3.
7
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Ilustración 5 Barra de Herramientas de CB
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Ilustración 6 Barra de Menús de Excel con CB
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Ilustración 7 Menú Cell
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1
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Ilustración 8 Menú Run
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Ilustración 9 Menú CBTools
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12.4. (
¿Qué es un supuesto?
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Ilustración 10 Hoja de Calculo del ejemplo CellPhone
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Ilustración 11 Cuadro de dialogo Distribution Gallery
Ilustración 12 Distribución Triangular para la celda B11
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Ilustración 13 Celda supuesto % Long Distance (B11)
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Ilustración 14 Distribución Normal para la celda B10
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Ilustración 15 Celda supuesto Actual Minutes (B10)
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12.5.
¿Cómo definir un pronóstico?
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Ilustración 16 Cuadro de dialogo Define Forecast
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Ilustración 17 Celda pronostico B14
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¿Cómo correr una simulación?
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Ilustración 18 Nuevos valores para el modelo al correr la simulación
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Ilustración 19 Cuadro de dialogo Run Preferences
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L, M
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7
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Ilustración 20 Gráfica de pronósticos para la celda Cost Savings (B14)
12.7. ¿Como analizar los resultados arrojados por el cuadro de pronósticos? 7 8
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Ilustración 22 Cuadro de Percentiles para la celda B14
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¿Cómo usar el cuadro de sensibilidad?
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Ilustración 23 Cuadro de Sensibilidad medida por el rango de correlación
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Ilustración 24 Cuadro de Sensibilidad medida por la Contribución a ala varianza
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¿Cómo generar un reporte?
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12.9.
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Ilustración 25 – Cuadro de diálogo Create Report
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12.10.
Otros recursos
12.10.1. =
Distribution Fitting : 7 = 7 J
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Ilustración 26 Galería de Distribuciones
Ilustración 27 Cuadro de dialogo Fit Distribution
= > ; 7 J &K
12.10.2. :
Correlated Assumptions ;
=
7 71 ( : ;8 (6 1
: 8 ( ; 7
>
7
)
>
= :
%
: )
=
=
)
1
Ilustración 28 Cuadro de dialogo Correlación
12.10.3. ( !
Precision Control %
7
!
=
) =
= %
T 1 (
= !
1
) 8 = 7
; 7
Ilustración 29 Cuadro PrecisionControl
12.10.4.
Overlay Chart
)
=
=
= T =
1(
:
=
7 7 =
;
;
Ilustración 30 Cuadro de dialogo Overlay Chart
> J : ; 7 T (
K
12.10.5. *
Trend Chart
+
T =
=
;
; 1 (
T
=
:
=
7 ; ;
> = ;
(
Ilustración 31 Cuadro de Tendencias
12.10.6. =
CB Tools =
=
) =
!
12.10.7.
=
7 :
1
Example Models
!
: ;8 =
8
= #
8 = : T
) = 1
= 7
12.11.
EJEMPLOS DE APLICACIÓN DE MODELOS
12.11.1. ( & B3
PRIMER EJEMPLO. “Futura Apartments”
8 = > = 1
= 7
) ;8
;
= 8 7
= =
71
Ilustración 32 Hoja de Calculo “Futura Apartments”
!
;8 •
WD33 =
•
( T B3 43
) = = =
= 8 = = 71 ( : ; L ! = = (6 1 > : ; J 5: U J >
=
=
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:
7 = 8 >
) ; = M >
W@D1333 >= ;
= )
;
: )
: 8
= )
: )
=
=
; ;8
: 8 # =
1 /
)
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71 !
!
;
1
Correr la simulación %
7 •
;
=
,
•
8 =
7
6= *
8
6
7 ! 8 = " ! + =
; (
=
7
* = # LD33 = 1
= #
>
=
7 + : ; M >
T
6
1
Ilustración 33 Pronósticos de Ganancia/Perdida para FA
• •
+ (6 =
= % :
8
7
= >
1
= 7 7> [- ;1
7
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1 ( = = ;; > 1 !
W4AD3 = = )
7 ;
& =
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=# 1 ! ;
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= W2333 WA3331
8
Determinar el beneficio :
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!
= ;
=
;; 1 % = •
%
= ;
;
> ;;
(
•
%
=
> 7
$ ; 3L
;; ; 8
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=
= M
)
7
=
Ilustración 34 Probabilidad de Ganancia para FA
( = :
=
;
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>
)
:
L! =
M
;
=
8 W3 =
: 8 =
1 ! 7 =
7 ; +
& 7 B> W3M
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=
=
6 W2333>
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;
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1 -
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:
EBX1
1
Como usa Crystal Ball la simulación de Montecarlo (
= : :
; = 8 )
;
=
1 T
) T
7
# ;
=
1
)
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( )
T
7
:
6
(
7
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8 ;
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=
1, =
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7
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12.11.2. ( 6
SEGUNDO EJEMPLO. “Vision Research”
8 =
7
: !
: :
(
=
8 =
=
)
1
)
*
, ; 1
:4
7 =
* > = = > = =
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;
= 1 ( = $ =
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, ;
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1
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=
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1 %
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; 8 = !
: 8 * P =
4
;8 ! : 8 ! [ (6
7 '
>
= =
T
!
[%
[
= =
>
http://www.crystalball.com/models/pharma.html
* , 7 2B1
:=
=
!
Ilustración 35 Hoja de calculo ejemplo Vision Research
(
: 8
=
;
)
*
,
:
1 Definir supuestos (
!
>
;
= ; )
; :
7 @A
0 ; ;
; :
= 7
G!7 =
=
; ) =
=
= = U
8
=
; ;
=
=
U ;
) 1 %
>
= > 0 !
L ;
)
= ;;
7
= 1 ( : 8 = ;; ) ! * P1
; 7 ; >
)
7 DM 1 H1 (
;
6
= ;
7 ; 8 = > * , : ;
( =
8 = > =
6=
= =
;
7
1
Definir Testing Costs. La Distribución Uniforme : > * ! * P = D13331333 = = 1% ) = ;; ! =
, ;
: : > ; ; > J WB13331333
> * J -
;
, !
6 > 8
=
=
:
7
K 7 6 ; 7 = $ =
; )
W@313331333 WB13331333 W = ; K >* , : ) W D13331333 1
7
=
) !
; ;1 = 8
7 ;
7
;; %
; ;
1 = U
=
;
1 %
7> =
= =
! • •
!D (
T
> : =
!
1(
; : J
= 7 ; $ ) ,
: -+ =
-
Ilustración 36 Cuadro de dialogo “Distribution Gallery”
•
;
•
;
7
( '
,
(
J
;
7
K =
Ilustración 37 Cuadro de dialogo “Distribución Uniforme”
) >
!D ; = = 1 1 ; 71
; ; ; 7 6 1 * WB13331333 ) = ; 7 •
(
; = ; = ) !
>
= ,
: 6 = !
; B
=
= WD133313331 = = > L
) 7
= ( ,
= : •
%
WB13331333> =
= +
; = >
J ;
: 8 + K =
> =
; = T
: 8
M 1 )
; L8
! =
M 1
*
•
(
( =
; D
=
= ; L-
WD13331333> ! M 1
;
; =
6
=
• 7
8
; ) 7 2F
>
:
:
:
Ilustración 38 Distribución Uniforme para la celda C5
;
7 > = 7> !
; =
1
> 7 2F1 =
B •
= = = #>
!
=
D
7
!D ) 1
: 8
Definir Costos de marketing: La Distribución Triangular *
, !
: =
;
* P>
=
;
=
= = $ = ; = = ; > * W@C13331333>
=
& =
1 (
< =
=T;
1 , : = W@F13331333
W@213331333 =
;; 1
* !
,
: <
>
7 )
U
=
; 7 ; 7 6 >
=
=
; 7 = ;;
;
1 % !
=
!
<
L
<
M •
!F
•
; =
= 7
:
; $
J •
;
J
;
: ) ,
7 -
! -+ =
1 (
1
K
• (
J
;
7 -
K =
Ilustración 39 Cuadro de dialogo “Distribución Triangular”
:
= = ; ;
7 7
) ;
=
=
; 7
1
2A>
7 = =
1 ! = =
• ( ,
(
; @2
=
= : •
W@213331333> ! <
=
%
=
= ". ; 1
@F> ( !
= < •
; @C =
!
/
W@F13331333>
%
(
) # =
*
$$ 0 (
;;
=
=
W@C13331333>
6
=
<
• ;
7
;
=
8
1
Ilustración 40 Distribución Triangular para la celda C6
7> !
)
@F •
@2 =
@C1
: 8
Definir pacientes curados: La Distribución Binomial
) ;
=
$1 * ! * P ) = # >
&
=
; ! * P> * , : ; ; @33 = , : = ) & = ; = = = 23 = > ) T 1 ( = ; > 23X = ; 7 ! * P= $1 * , : = # = ; = ) = 8 #6 2DX1
%
; > J = = ; = G* , : = = > * , : 7 ; ) = ; ; T = L@33M 1 %
U
=
K > * >)
: #6 &
;
=
•
,
7 ; 7Q ) 6 L2DM
J %
2DX1 ! ; ; 7
H1 =
K >
T =
!@3
•
;
= 7
; •
(
'*
= ;
7
• •
•
( ) D3XM 1
* =
=
$ =
)
+ = = ;;
1 L&8 31D
Ilustración 41 Cuadro de dialogo “Distribución Binomial”
•
; L =
7
$1M 8 >
6
= ;; 6 1
=
•
=
1
; L
0 ( 2DX 312D
; ) = =
6= @>
3 =
8>
= * ; =
;
% ;; : )
, = = )
= ;; 313B>
T
BX1
• •
; ) =
;
&
@33 =
>
= ) * , : @33 = 6= ; 7 ; 1%
= • •
(
•
(
; 312D
= =
2DX =
=
;;
•
=
•
(
•
( =
•
@33> =
; @33
= =
&
$
@33 =
)
1
:
•
;
7
; =
8
•
Ilustración 42 Distribución Binomial para la celda C10
7> !
T
@33>
= &
)
3 =
1
•
=
: 8
Tasa de crecimiento: La Distribución Personalizada *
, : : 4313331333 = 3X DX $ ) ! * P ;
6
> =
)
=
8
=
7 =
;
1 <
2DX
6
=
= ;
=
(
)
: =
) =
= * P
! (
1 ( DX
; J ; ;
,
= = K
7 ; 1 % =
= ( =
7
#
=
;; ) 7 =
=
=
:
;
= @DX1 = 1 !
;
)
=
; > :
= ; =
0
; =
7
;
7
; =
7
>
1
=
= =
>
B1 =
7 =
=
= >
1
=
;
) > *
T
=
=
%
=
•
!
* P1 % =
U
!@D
•
;
•
= 7 ;
7
L%
M
•
( ) ; 8
; 7 B@ ) )
> 1
7 % =
=
1 +7 8
Ilustración 43 Cuadro de dialogo “Distribución Personalizada”
%
= •
(
; 3X
(
=
= •
%
•
(
3X
; DX
(
=
= •
%
•
(
( ,
=
DX
; ADX
=
=
=
=
: *
,
;;
) =
7
= =
* =
7
:1
• ;
7
3X
DX =
1
Ilustración 44 Distribución Personalizada para C15
% •
(
; U@DX
(
= •
%
•
(
= @DX
; UDX
( %
•
(
( ,
=
7
=
=
= •
7
DX
; 2DX = :
= 2DX
DX
=
;
) =
7
= =
* =
7
UDX
= *
@DX
• ; ;
7
= =
$
+ 1
@DX :
1
Ilustración 453 Distribución personalizada para C15 (2 Supuesto)
%
; 7 : 8
=
=
T ;
=
1 ( 7 !
)
=
=
•
1 =
: 8
Definir penetración en el mercado: La distribución normal ( =
= =
<
)
7
=
= *
,
; *
7 , FCX
FX
:
=
2X1 J + ; = ; 7 7 =
;
7 :
CX K
) =
= =
= ;8
7 >
@3X1
(
> CX> =
; =
1
) > =
<
DX> =
=
=
* J =
, <
:
7 K 1 %
% 7
•
#
1 ;
7
=
; U
=
; =
!@E
•
= 7
•
;
7 +
• (
J +
;
K =
Ilustración 34 Cuadro de dialogo “Distribución Normal”
:
=
)
=
=
;
7
7 •
= = =
•
%
•
(
(
C133X> =
=
7
; 2X
1
=
!
; CX CX =
(
=
2X =
7
1
• •
; 7 ) =
•
%
•
(
•
(
= ;
8 7
; > ; 1
=
; DX =
= = 1
=
= DX )
> ;
)
= 7
=
• •
;
7
; =
8
1
Ilustración 35 Distribución Normal para la celda C19
7> ! ;
)
7
) = =
Definir pronósticos
;8
DX ) : 8
CX 1
1
= # U
U= 1
=
=
*
U = 1(
7
,
; ;
: =
= 7 L!2BM=
>
=
;; = ;; 1 (
>
> = ; ! * P1
= =
=
)
L!2@M
;
Calcular el beneficio total !
=
=
71 (
>
;
= 1 % 1
U= = •
;
= =
: 8
;
= =
; = #
= L!@EM
7
;
= # > = 1% (
= U=
T
( ; 7 ; ;
= !@F\!@E\!231 ! = $ L!@FM = = L!23M 1
1 =
•
;
!2@
(
=
7
=
: J % = = ;
=
; 7
+ = 1
= ;
= 7 1( ; 1 %
1
; ;
=
" = > : 8 ;
) >
Ilustración 36 “Definir Pronostico” para C21
•
%
•
(
; J )
K
=
#
>
=
8
•
=
: 8
Calcular el beneficio neto ;
=
• •
=
;
>
!2B (
=
;
: 8
& ; L!2@M 1 & M >
=
;
L!@@
*
M > L!AM
;
>
& (
U= T
; + L!DM)
= :
;
=
;
% (
= = ] L!@@Q !2@U!AQ U!4U!DM
1(
L!4M !
•
U= ;
L!@@ :
: 1
; ;
= 7
"
Ilustración 46 “Definir Pronostico “ para C23
+
;
=
=
) = 7
)
= = #
1 •
%
•
(
; J
K
•
= #
= >
: 8
: ,
*
= :>
;
;
=
=
7
: 8 Q
= )
71 Correr la simulación ! ;
7 7 1
!
> ) T
= 7
=
=
T1 1 =
7> = )
> = %
7
=
)
T 7
•
(
8
1
= 7
%$(
1 8 = >
T
• •
-% = 7>
J , = ; D33
.' '
&[
% '/
(
'
K = &L
T
1 6
M >
•
(L
•
= 7 '/
•
(
M J
'
0 T
1L = )
!
(
$' M
; EEE
• Ver los cuadros de pronósticos ;
) = 1
;
= ;
7
=
> 6 = 7 = 7
:
1 (
; 1 = (
(
;
L0
%
( **
M =
$
>
7 BC
Ilustración 47 Cuadro de Pronostico para “Net Profit”
= 7 2"
* "
> =
7
T
=
%
1 7
=
+ ,&
> =
=
8
; 7 ;
U=
1
;
7
=
; ;
7
T =
;
1 ( = 7 = ) @F
7
+
@F = 1 ( =
,
)
1 )
= ;
= )
7 ;
7 BC>
=
> !
7
=
U= 6
8
7 1 T
= )
1
Interpretar los resultados
Entender el cuadro de pronóstico ! * T
=
=
,
:1
;
> > ) 6
6= 8
)
( W@41A : + ( =
6
= =
! T ; 1 ( @33X> ) = ; 1 , )
= ;
;
=
; 1
=
T
; > =
8 =
:
; ; ;
) : 8 1 =
= =
) 8
=
>! =
: > )
7
;# >
=
) )
1 =
:
(
7
7 BC> WB414> 1 = 7 1 %
7
=
=
=
6 6
7>
T 1 (
7
=
= 6
1 =
>
T
6
=
) 71
Determinar el nivel de certidumbre :
=
= =
1
* ;
,
: )
; = ;
%
) ;
= •
(
•
(
•
%
! )
= ; 3
7
+
-;
( > = ; 1
; W313
=
; AE1C3X = = =
J +
=
%
AE1C3X1 ( = ; = ) 6 L@33X AE1C3XM 1 *
,
; =
>=
=
=
:
%
=
K >
=
;
=
=
;
; ) * ; ;
: ) W2133313331 ! 1
) ,
:
1 2312X ;
!
Ilustración 48 Pronostico para “Net Profit” con valores positivos
•
(
•
%
; 2
=
! ;
7 BE> ! W213
; 1
Ilustración 49 Pronostico para “Net Profit”
*
,
: =
;
;
W2133313331 * , = 7 1
= !
AB1F3X =
; ) ; = >! •
(
•
%
= ; 4
: :
;
= ; ) W4133313331
= ;
* , W413331333 = =
: = ; = =
> 7
=
; ;
1 1
=
!
=
W413
Ilustración 50Pronostico para “Net Profit” (2)
(
=
7 ;
; =
! =
J & FFX1 ! W4133313331 * * P = 1
7 4@ ; ,
:
= ;
13. CRYSTAL BALL TOOLS :
!
= 1 %
=
*
) 7
: =
= 7> )
7 = ;
> 7
) =
(
:
1 D
)
!
=
7
:& ! ( -
7
!: =
-; = ( 7 7
; >
=
5
http://www.crystalball.com/crystal_ball/cbtools.html
8> ;
>
8
13.1.
Herramientas de Montaje del modelo 7
13.1.1. : &
Batch Fit :
F
= ! !
>
;
1
(6 T! : &1
13.1.2.
(
= ;
:
=
=
>
Matriz de correlación
: = )
) = Q
= ;8
6 : =
= >
>
; 7> = = 1
)
=
13.1.3. (
6
>=
8 = 1
Tornado Chart
=
= ; ;
:
6
>
;8 =
>
http://www.crystalball.com/spotlight/spotlight10.html
)
; 8 =
6 1
13.2.
Herramientas de análisis
13.2.1. ( =
Bootstrap
#
=
=
7
? =
= =
1
>
>
=
= 1
7>
(
7 )
:
;
13.2.2.
;
=
7
=
>
>
Escenario de decisión =
=
6
;
7 7Q 71
13.2.3.
Análisis de escenarios >
= ( =
) =
) )
>
;
=
13.2.4.
=
;
= =
=
=
1
= 7>
=
7>
>
7 1
Simulación Bidimensional: =
=
=
;
)
=
7
> =
7 1 (
;
)
=
;
=
7> 7
;
= $
= ;
6=
L
M > )
7 =
)
; >
L: 67
>
M >
;
T
= ;
L
7
T = M
14. ANÁLISIS DE LAS HERRAMIENTAS :
=
= 8
=
1 :
L
7
=
= 1
Herramientas de Montaje (Setup Tools)
: ;
!
=
M > = ;
7
%
8 =
8
=
14.1.
; )
8 * ;
7
14.1.1.
= *
1
Batch Fit o Herramientas de serie
Ilustración 51 Asistente de Batch Fit
(
#
=
;
> L
=
;;
= >
> #
; >
= =
T =
1M = = :
= )
7 )
;; T ; 1 =
7
> ; 1
8 (
7
=
: > = 7 = 8 1
( =
7 = ; =
;;
= > ; = ;
;
= 8
> !: U
Q ;
=
:
= = ;
>
=
=
=
> )
7
7 ) 7
:
)
;
> = )
>
8
=
8
(6 >
)
Q
=
>
1
Ejemplo (
=
; ! 8
; Q= =
>
%
7
;
8
( = ;
8
=
; @EEC ;
=
= =
= ; >
:
; 233B> =
1
:& = !
; >
: !-
T
=
(6 > :
: &>
=
@
1 B
; (
=
2 =
1
B
=
> =
=
1 >
= >
! :
=
;
;
7 : 8
1
= 1 = 7 ;
7 7
=
;;
=
> 1
%
B
B ) =
( =
1
= >)
;
7
=
3 = 7
>=
; =
=
:
>
! (6 > =
7
=
;
7 1
=
=
=
@1
: 8 =
=
T
=
;
7
:
1
(
T,
= 7
=
; 7
7 ^
!
=
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Ilustración 67 - Asistente para la simulación bidimensional
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Hoffman, F. O. and J. S. Hammonds. “Propagación de la incertidumbre en situaciones de riesgo: La necesidad de distinguir entre incertidumbre debida a la falta de conocimiento y la incertidumbre ocasionada por la variabilidad” Análisis de Riesgo, vol. 14, no. 5. pp 707-712, 1994.
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