This document discusses using Monte Carlo simulation for budgeting. It notes that budgeting involves uncertainty about variables like sales, prices, wages, etc. It recommends building an EBITDA model to capture key cost and value drivers at a level where consolidation is easy. The model allows simulating probability distributions for budget forecasts using uncertainty assessments. Rolling forecasts can update as the year progresses. Managers provide uncertainty ranges for variables based on experience. Simulation outputs show probability distributions that identify overconfidence or underbudgeting tendencies.
Alguns exercícios de
Geometria Analítica (vetores) resolvidos.
Em caso de dúvidas/sugestões e relato de erros, enviar e-mail para rodrigo.silva92@aluno.ufabc.edu.br
Este documento discute funções do tipo y = a + b/x-c. Estuda o domínio, contradomínio, zeros, sinal, extremos, monotonia, injetividade, paridade, continuidade e limites destas funções. Também explica como obter os gráficos destas funções a partir de transformações como dilatações, compressões, translações horizontais e verticais da função básica y=1/x. Por fim, fornece exercícios sobre estas funções.
Este documento discute inequações do primeiro grau. Explica que uma inequação é uma expressão algébrica com um sinal de desigualdade e apresenta exemplos. Também aborda a solução de inequações, propriedades das desigualdades e sistemas de inequações do primeiro grau.
Este documento estabelece regras sobre o tratamento de dados pessoais no setor das comunicações eletrónicas em Portugal. Determina que as entidades formadoras autorizadas têm um ano para obter certificação e são dispensadas de alguns requisitos temporariamente. Também estabelece que as competências do IMT nas regiões autónomas são exercidas pelas respetivas administrações regionais.
La guía ofrece recomendaciones para que periodistas aprovechen mejor las herramientas de Google en su trabajo, incluyendo cómo crear un canal en YouTube para ampliar el alcance de sus contenidos, usar herramientas como Google Adwords para medir el impacto de sus contenidos en la web, e insertar videos de YouTube en sitios web o blogs. También recomienda aplicaciones para periodistas en el Android Market y extensiones de Google Chrome.
Este documento resume la historia de 25 años de FLUIDEX, una asociación española de empresas exportadoras de equipos para la manipulación de fluidos. Se fundó en 1989 y actualmente agrupa a más de 85 empresas del sector. A lo largo de estos 25 años, FLUIDEX ha organizado más de 160 misiones comerciales y 47 misiones inversas en todo el mundo, así como participado en 94 ferias del sector. Testimonios de varias personas destacan el importante papel de FLUIDEX en la promoción internacional de las empresas españolas del sector.
Este documento presenta un proyecto interdisciplinario realizado por estudiantes de 1er año sobre la construcción de títeres y un teatrino. Los estudiantes construyeron diferentes títeres como una araña, abeja, caracol, grillo, vaquita de San Antonio y perico utilizando materiales como goma espuma y telgopor. Luego diseñaron un teatrino y una obra teatral para presentar a los niños de jardín de infantes.
Este documento habla sobre los blogs. Explica que un blog es un sitio web interactivo donde se publican artículos de forma periódica. Luego resume brevemente la historia de los blogs desde 1994 y su creciente popularidad. También destaca la importancia de los blogs como herramientas de formación e información, y recomienda algunos blogs populares.
Alguns exercícios de
Geometria Analítica (vetores) resolvidos.
Em caso de dúvidas/sugestões e relato de erros, enviar e-mail para rodrigo.silva92@aluno.ufabc.edu.br
Este documento discute funções do tipo y = a + b/x-c. Estuda o domínio, contradomínio, zeros, sinal, extremos, monotonia, injetividade, paridade, continuidade e limites destas funções. Também explica como obter os gráficos destas funções a partir de transformações como dilatações, compressões, translações horizontais e verticais da função básica y=1/x. Por fim, fornece exercícios sobre estas funções.
Este documento discute inequações do primeiro grau. Explica que uma inequação é uma expressão algébrica com um sinal de desigualdade e apresenta exemplos. Também aborda a solução de inequações, propriedades das desigualdades e sistemas de inequações do primeiro grau.
Este documento estabelece regras sobre o tratamento de dados pessoais no setor das comunicações eletrónicas em Portugal. Determina que as entidades formadoras autorizadas têm um ano para obter certificação e são dispensadas de alguns requisitos temporariamente. Também estabelece que as competências do IMT nas regiões autónomas são exercidas pelas respetivas administrações regionais.
La guía ofrece recomendaciones para que periodistas aprovechen mejor las herramientas de Google en su trabajo, incluyendo cómo crear un canal en YouTube para ampliar el alcance de sus contenidos, usar herramientas como Google Adwords para medir el impacto de sus contenidos en la web, e insertar videos de YouTube en sitios web o blogs. También recomienda aplicaciones para periodistas en el Android Market y extensiones de Google Chrome.
Este documento resume la historia de 25 años de FLUIDEX, una asociación española de empresas exportadoras de equipos para la manipulación de fluidos. Se fundó en 1989 y actualmente agrupa a más de 85 empresas del sector. A lo largo de estos 25 años, FLUIDEX ha organizado más de 160 misiones comerciales y 47 misiones inversas en todo el mundo, así como participado en 94 ferias del sector. Testimonios de varias personas destacan el importante papel de FLUIDEX en la promoción internacional de las empresas españolas del sector.
Este documento presenta un proyecto interdisciplinario realizado por estudiantes de 1er año sobre la construcción de títeres y un teatrino. Los estudiantes construyeron diferentes títeres como una araña, abeja, caracol, grillo, vaquita de San Antonio y perico utilizando materiales como goma espuma y telgopor. Luego diseñaron un teatrino y una obra teatral para presentar a los niños de jardín de infantes.
Este documento habla sobre los blogs. Explica que un blog es un sitio web interactivo donde se publican artículos de forma periódica. Luego resume brevemente la historia de los blogs desde 1994 y su creciente popularidad. También destaca la importancia de los blogs como herramientas de formación e información, y recomienda algunos blogs populares.
A Pareto chart is a type of bar chart used to identify problems. It arranges data in descending order of frequency or impact, separating the major issues from minor ones. This allows users to focus on addressing the top 20% of causes that create 80% of the problems. To construct a Pareto chart, data is collected, categorized, and plotted as bars with associated frequencies and cumulative percentages. This visual format makes it easy to identify priority issues to improve.
Monte Carlo simulation allows businesses to model uncertain situations by simulating random outcomes thousands of times on a computer. It can be used to estimate probabilities, determine optimal decisions under risk, and evaluate investments. The document describes how to simulate random variables in Excel using functions like RAND(), VLOOKUP(), and NORMINV(). It provides an example of a greeting card company using Monte Carlo simulation over 1,000 iterations to determine the production quantity with the highest expected profit under variable demand.
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
The document describes the Mistral Matrix and Method for increasing productivity, effectiveness, and ROI. The method involves applying the Pareto principle ex-ante to qualify prospects. It assigns weighted factors to prospects and ranks them, with the top 20% of prospects expected to generate 80% of results. By focusing on the top 20% and next 35% of prospects as identified by the Mistral Matrix, productivity and business development are increased by 400%.
The document discusses various quality tools and techniques including a project charter, check sheet, Pareto analysis, cause-and-effect diagram, and matrix. It provides details on how to create and use each tool. A project charter is used to define the goals, metrics, scope, team, and plan for a quality project. Check sheets collect attribute and variable data to identify defects, locations, and causes. Pareto analysis arranges information to establish improvement priorities by highlighting the most common issues. A cause-and-effect diagram displays potential factors that influence a problem or quality characteristic.
Performing Strategic Risk Management with simulation modelsWeibull AS
“How can you be better than us to understand our business risk?"
This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can utilize your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.
The main reason for this lies in how we can treat uncertainties 1 in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.
This document discusses how spreadsheets like Microsoft Excel and Google Sheets can be used to account for expenses and determine if a product's estimated sale price will be profitable. It provides an example of using Excel to calculate the total cost of ingredients for a milk tea product and compare it to the estimated sale price to see if it will earn a profit. The document also demonstrates how Excel formulas like SUM, subtraction, COUNTIF, and AVERAGEIF can be used to analyze survey data about a product to help evaluate its potential success in the target market.
please do the extra credit \%\% (1) Load training set images from a .mat file (2 points) clear;
close all; clc; % Detailed instructions: \% Load the training set of 2414 images contained in the
MATLAB data file \% training_set_faces.m into the MATLAB workspace: Each image % in the
training set is a 48px by 42px image of a face. The training set fa% are saved in the MAT file
training_set_faces.mat. When you load this data \% file into the MATLAB workspace, you will
see a 3D array called yalefaces \% where the entry yalefaces (:,:,1) is the first image of the
training set, % yalefaces (:,:,2) is the second image, etc. % \% Suggested MATLAB functions:
load \% !! Your code here !! Load the training images from the provided \% MAT file
training_set_faces.mat load training_set_faces.mat;
\%\% (2) Plot one training set image (5 points) % Detailed intructions: \% Plot one of the images
from the training set 3D array to demonstrate you've \% correctly imported the training set
images. % \% Suggested MATLAB functions: imshow \% PLOTTING WITH imshow: Use the
options 'DisplayRange', [], and \% 'InitialMagnification', 'fit' when using imshow so that the
plotted images % are easy to see. See imshow help documentation for syntax details and the %
in-class Eigenfaces activities more help. % !! Your code here !! In a new figure, plot one of the
images you just loaded \% into MATLAB. Include a descriptive figure title. (5 points) figure
imshow(yalefaces (:, : , 1), [10 250 , 'InitialMagnification ', ... 'fit', 'Interpolation', 'bilinear')
title('eigenface 1)
\% (3) Vectorize the images and combine into 2D array (8 points) % Detailed instructions: %
create a 2D array that contains all the vectorized images in the training % set. First, convert each
image into a vector, simply by concatenating the row % of pixels in the original image, resulting
in a single column with 4842=%2016 elements, just like the in-class activity. % Then combine
all 2414 training set image column vectors into % a single matrix T, where each column of the
matrix is an image. Each row % of this matrix T corresponds to a pixel location. The 2D array
must be % assigned to the variable T. The size of T will be 20162414. %% suggested MATLAB
functions: reshape (or using a for loop) % !! Your code here !! Vectorize training set images into
a 2D array called T % ( 8 points) [a,b, c] = size(yalefaces); T = reshape(yalefaces, [a*b, c]);
\% We need to be sure the data type of our T matrix is double for the rest % of the computations
in this code (mean, subtraction, eig, etc.) T= double (T);% do not modify the line % (4) Find the
mean face ( 5 points) % Detailed instructions: \% The mean face is found by taking the average
pixel value across all images % in the training set at each pixel location. So the mean face pixel
(2,3) is % the average of the pixel values from all the images at the training set for % that one
pixel (2,3). The mean face will be a column vector of size 20161. % Remember that the.
Value at Risk (VAR) is a risk management measure used to calculate potential losses over a given time period at a specified confidence level. There are three key elements - the level of loss, time period, and confidence level. For example, there is a 5% chance losses will exceed $20M over 5 days. VAR does not provide information on potential losses above the VAR level. There are three main methodologies used to calculate VAR - historical simulation, variance-covariance, and Monte Carlo simulation. Each has its own strengths and weaknesses in terms of implementation and ability to capture risk.
Data tables in Excel allow users to model scenarios by changing input values to see their impact. They can be used with Spark APIs by tagging the data table inputs and outputs. This document demonstrates creating single and two-variable data tables to model ticket sales. It shows setting up the framework, defining the inputs under 'What-If Analysis', and presenting the results through Spark.
This document analyzes the Capital Group U.S. Income and Growth (m) mutual fund over various time periods. It shows that the fund has exhibited a value style with some growth characteristics. Performance attribution shows that over 93% of the fund's returns have been explained by movements in the Russell 1000 Value index. Risk-adjusted returns place the fund above the median for large value funds over most periods examined.
This document proposes improvements to existing customer lifetime value models. It discusses deriving current models A and B, which discount average revenues over a subscriber's expected duration. The improvements consider estimating future cash flows and growth rates through regression analysis, accounting for other revenue streams, and incorporating the value of a subscriber's social network. The proposed model uses discounted cash flow analysis and least squares regression to forecast revenues and growth rates for each subscriber, considering revenues from mobile, TV, broadband and the revenues of subscribers within their social network. It requires subscriber revenue and call data to implement the analysis.
The document outlines various techniques for stand-alone risk analysis, including sensitivity analysis, scenario analysis, break-even analysis, simulation analysis, and decision tree analysis. It provides examples and procedures for conducting each type of analysis. Sensitivty analysis and scenario analysis are discussed in detail through examples. Simulation analysis covers defining probability distributions, dealing with correlations, and issues in application. Decision tree analysis is introduced as a tool for sequential decision making under risk.
The document discusses various transformations that can be applied to variables in SPSS to satisfy assumptions of normality, homogeneity of variance, and linearity. It describes logarithmic, square root, inverse, and square transformations and how to compute them in SPSS. Adjustments may need to be made to variable values depending on minimum/maximum values and distribution skew. The document provides examples of computing each transformation for a variable measuring time spent online.
The document discusses several quality management tools:
1) The Pareto principle, also known as the 80/20 rule, which states that roughly 80% of effects come from 20% of causes. This applies to areas like income distribution and project management time allocation.
2) Scatter plots which show the relationship between two variables and can indicate if they have a positive or negative correlation. They are useful for early data exploration.
3) Flow charts which use pictures and arrows to model processes and develop a common understanding, though symbols may need explanation between different audiences. They can be used to define, analyze, standardize, and improve processes.
Compensation modelling for performance appraisal
To understand the process/ logic/ science/ maths behind the various numbers that keep floating around during performance appraisal, eg why is the increment/ variable pay X%?
Assessing Model Performance - Beginner's GuideMegan Verbakel
A binary classifier predicts outcomes that are either 0 or 1. It is trained on historical data containing features and targets, and learns patterns to predict probabilities of each class for new data. Performance is evaluated using metrics like accuracy, precision, recall from a confusion matrix, and ROC AUC. The bias-variance tradeoff and over/under fitting are minimized by optimizing model complexity during training and testing.
The Intellectual Property Licensing Valuation Model is based on the Monte Carlo simulation and was created using Crystal Ball software. The model provides an Internal Rate of Return (IRR) and Net Present Value (NPV) valuation while taking into account the inherently high uncertainty (risk) of cost and revenue associated with embryonic technology projects. The widget and its associated cost/revenue figures are fictitious. However, the model can be applied to real world projects.
The document analyzes data on annual return on investment (ROI) for two college majors: business and engineering. Regression analyses were conducted for each major and found a negative linear relationship between cost and annual ROI. The analyses indicated that over 90% of the variation in annual ROI could be explained by cost for both majors. Confidence intervals and hypothesis tests were also reported.
The role of events in simulation modelingWeibull AS
The need for assessing the impact of events with binaryi outcomes, like loan defaults, occurrence of
recessions, passage of a special legislation, etc., or events that can be treated like binary events like
paradigm shifts in consumer habits, changes in competitor behavior or new innovations, arises often
in economics and other areas of decision making.
By using analogies from intervention analysis a number of interesting and important issues can be
analyzed:
If two events affects one response variable will the combined effect be less or greater than the sum of both?
Will one event affecting more than one response variable increase the effect dramatically?
Is there a risk of calculating the same cost twice?
If an event occurs at the end of a project, will it be prolonged? And what will the costs be?
Questions like this can never be analyzed when using a ‘second layer lump sum’ approach. Even
more important is possibility to incorporate the responses to exogenous events inside the simulation
model, thus having the responses at the correct point on the time line and by that a correct net
present value for costs, revenues and company or project value.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
More Related Content
Similar to Budgeting with Monte Carlo simulation models
A Pareto chart is a type of bar chart used to identify problems. It arranges data in descending order of frequency or impact, separating the major issues from minor ones. This allows users to focus on addressing the top 20% of causes that create 80% of the problems. To construct a Pareto chart, data is collected, categorized, and plotted as bars with associated frequencies and cumulative percentages. This visual format makes it easy to identify priority issues to improve.
Monte Carlo simulation allows businesses to model uncertain situations by simulating random outcomes thousands of times on a computer. It can be used to estimate probabilities, determine optimal decisions under risk, and evaluate investments. The document describes how to simulate random variables in Excel using functions like RAND(), VLOOKUP(), and NORMINV(). It provides an example of a greeting card company using Monte Carlo simulation over 1,000 iterations to determine the production quantity with the highest expected profit under variable demand.
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
The document describes the Mistral Matrix and Method for increasing productivity, effectiveness, and ROI. The method involves applying the Pareto principle ex-ante to qualify prospects. It assigns weighted factors to prospects and ranks them, with the top 20% of prospects expected to generate 80% of results. By focusing on the top 20% and next 35% of prospects as identified by the Mistral Matrix, productivity and business development are increased by 400%.
The document discusses various quality tools and techniques including a project charter, check sheet, Pareto analysis, cause-and-effect diagram, and matrix. It provides details on how to create and use each tool. A project charter is used to define the goals, metrics, scope, team, and plan for a quality project. Check sheets collect attribute and variable data to identify defects, locations, and causes. Pareto analysis arranges information to establish improvement priorities by highlighting the most common issues. A cause-and-effect diagram displays potential factors that influence a problem or quality characteristic.
Performing Strategic Risk Management with simulation modelsWeibull AS
“How can you be better than us to understand our business risk?"
This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can utilize your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.
The main reason for this lies in how we can treat uncertainties 1 in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.
This document discusses how spreadsheets like Microsoft Excel and Google Sheets can be used to account for expenses and determine if a product's estimated sale price will be profitable. It provides an example of using Excel to calculate the total cost of ingredients for a milk tea product and compare it to the estimated sale price to see if it will earn a profit. The document also demonstrates how Excel formulas like SUM, subtraction, COUNTIF, and AVERAGEIF can be used to analyze survey data about a product to help evaluate its potential success in the target market.
please do the extra credit \%\% (1) Load training set images from a .mat file (2 points) clear;
close all; clc; % Detailed instructions: \% Load the training set of 2414 images contained in the
MATLAB data file \% training_set_faces.m into the MATLAB workspace: Each image % in the
training set is a 48px by 42px image of a face. The training set fa% are saved in the MAT file
training_set_faces.mat. When you load this data \% file into the MATLAB workspace, you will
see a 3D array called yalefaces \% where the entry yalefaces (:,:,1) is the first image of the
training set, % yalefaces (:,:,2) is the second image, etc. % \% Suggested MATLAB functions:
load \% !! Your code here !! Load the training images from the provided \% MAT file
training_set_faces.mat load training_set_faces.mat;
\%\% (2) Plot one training set image (5 points) % Detailed intructions: \% Plot one of the images
from the training set 3D array to demonstrate you've \% correctly imported the training set
images. % \% Suggested MATLAB functions: imshow \% PLOTTING WITH imshow: Use the
options 'DisplayRange', [], and \% 'InitialMagnification', 'fit' when using imshow so that the
plotted images % are easy to see. See imshow help documentation for syntax details and the %
in-class Eigenfaces activities more help. % !! Your code here !! In a new figure, plot one of the
images you just loaded \% into MATLAB. Include a descriptive figure title. (5 points) figure
imshow(yalefaces (:, : , 1), [10 250 , 'InitialMagnification ', ... 'fit', 'Interpolation', 'bilinear')
title('eigenface 1)
\% (3) Vectorize the images and combine into 2D array (8 points) % Detailed instructions: %
create a 2D array that contains all the vectorized images in the training % set. First, convert each
image into a vector, simply by concatenating the row % of pixels in the original image, resulting
in a single column with 4842=%2016 elements, just like the in-class activity. % Then combine
all 2414 training set image column vectors into % a single matrix T, where each column of the
matrix is an image. Each row % of this matrix T corresponds to a pixel location. The 2D array
must be % assigned to the variable T. The size of T will be 20162414. %% suggested MATLAB
functions: reshape (or using a for loop) % !! Your code here !! Vectorize training set images into
a 2D array called T % ( 8 points) [a,b, c] = size(yalefaces); T = reshape(yalefaces, [a*b, c]);
\% We need to be sure the data type of our T matrix is double for the rest % of the computations
in this code (mean, subtraction, eig, etc.) T= double (T);% do not modify the line % (4) Find the
mean face ( 5 points) % Detailed instructions: \% The mean face is found by taking the average
pixel value across all images % in the training set at each pixel location. So the mean face pixel
(2,3) is % the average of the pixel values from all the images at the training set for % that one
pixel (2,3). The mean face will be a column vector of size 20161. % Remember that the.
Value at Risk (VAR) is a risk management measure used to calculate potential losses over a given time period at a specified confidence level. There are three key elements - the level of loss, time period, and confidence level. For example, there is a 5% chance losses will exceed $20M over 5 days. VAR does not provide information on potential losses above the VAR level. There are three main methodologies used to calculate VAR - historical simulation, variance-covariance, and Monte Carlo simulation. Each has its own strengths and weaknesses in terms of implementation and ability to capture risk.
Data tables in Excel allow users to model scenarios by changing input values to see their impact. They can be used with Spark APIs by tagging the data table inputs and outputs. This document demonstrates creating single and two-variable data tables to model ticket sales. It shows setting up the framework, defining the inputs under 'What-If Analysis', and presenting the results through Spark.
This document analyzes the Capital Group U.S. Income and Growth (m) mutual fund over various time periods. It shows that the fund has exhibited a value style with some growth characteristics. Performance attribution shows that over 93% of the fund's returns have been explained by movements in the Russell 1000 Value index. Risk-adjusted returns place the fund above the median for large value funds over most periods examined.
This document proposes improvements to existing customer lifetime value models. It discusses deriving current models A and B, which discount average revenues over a subscriber's expected duration. The improvements consider estimating future cash flows and growth rates through regression analysis, accounting for other revenue streams, and incorporating the value of a subscriber's social network. The proposed model uses discounted cash flow analysis and least squares regression to forecast revenues and growth rates for each subscriber, considering revenues from mobile, TV, broadband and the revenues of subscribers within their social network. It requires subscriber revenue and call data to implement the analysis.
The document outlines various techniques for stand-alone risk analysis, including sensitivity analysis, scenario analysis, break-even analysis, simulation analysis, and decision tree analysis. It provides examples and procedures for conducting each type of analysis. Sensitivty analysis and scenario analysis are discussed in detail through examples. Simulation analysis covers defining probability distributions, dealing with correlations, and issues in application. Decision tree analysis is introduced as a tool for sequential decision making under risk.
The document discusses various transformations that can be applied to variables in SPSS to satisfy assumptions of normality, homogeneity of variance, and linearity. It describes logarithmic, square root, inverse, and square transformations and how to compute them in SPSS. Adjustments may need to be made to variable values depending on minimum/maximum values and distribution skew. The document provides examples of computing each transformation for a variable measuring time spent online.
The document discusses several quality management tools:
1) The Pareto principle, also known as the 80/20 rule, which states that roughly 80% of effects come from 20% of causes. This applies to areas like income distribution and project management time allocation.
2) Scatter plots which show the relationship between two variables and can indicate if they have a positive or negative correlation. They are useful for early data exploration.
3) Flow charts which use pictures and arrows to model processes and develop a common understanding, though symbols may need explanation between different audiences. They can be used to define, analyze, standardize, and improve processes.
Compensation modelling for performance appraisal
To understand the process/ logic/ science/ maths behind the various numbers that keep floating around during performance appraisal, eg why is the increment/ variable pay X%?
Assessing Model Performance - Beginner's GuideMegan Verbakel
A binary classifier predicts outcomes that are either 0 or 1. It is trained on historical data containing features and targets, and learns patterns to predict probabilities of each class for new data. Performance is evaluated using metrics like accuracy, precision, recall from a confusion matrix, and ROC AUC. The bias-variance tradeoff and over/under fitting are minimized by optimizing model complexity during training and testing.
The Intellectual Property Licensing Valuation Model is based on the Monte Carlo simulation and was created using Crystal Ball software. The model provides an Internal Rate of Return (IRR) and Net Present Value (NPV) valuation while taking into account the inherently high uncertainty (risk) of cost and revenue associated with embryonic technology projects. The widget and its associated cost/revenue figures are fictitious. However, the model can be applied to real world projects.
The document analyzes data on annual return on investment (ROI) for two college majors: business and engineering. Regression analyses were conducted for each major and found a negative linear relationship between cost and annual ROI. The analyses indicated that over 90% of the variation in annual ROI could be explained by cost for both majors. Confidence intervals and hypothesis tests were also reported.
Similar to Budgeting with Monte Carlo simulation models (20)
The role of events in simulation modelingWeibull AS
The need for assessing the impact of events with binaryi outcomes, like loan defaults, occurrence of
recessions, passage of a special legislation, etc., or events that can be treated like binary events like
paradigm shifts in consumer habits, changes in competitor behavior or new innovations, arises often
in economics and other areas of decision making.
By using analogies from intervention analysis a number of interesting and important issues can be
analyzed:
If two events affects one response variable will the combined effect be less or greater than the sum of both?
Will one event affecting more than one response variable increase the effect dramatically?
Is there a risk of calculating the same cost twice?
If an event occurs at the end of a project, will it be prolonged? And what will the costs be?
Questions like this can never be analyzed when using a ‘second layer lump sum’ approach. Even
more important is possibility to incorporate the responses to exogenous events inside the simulation
model, thus having the responses at the correct point on the time line and by that a correct net
present value for costs, revenues and company or project value.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
The evils of a single point estimate.
Traditionally, when estimating costs, project value, equity value or budgeting, one number is
generated – a single point estimate. There are many problems with this approach. In budget
work this point is too often given as the best the management can expect, but in some cases
budgets are set artificially low generating bonuses for later performance beyond budget
Public works projects.
In public works and large scale construction or engineering projects – where uncertainty mostly (only) concerns cost, a simplified scenario analysis is often used.
M&A analytics: When two plus two is five or three or .Weibull AS
Mergerrs & Acquisitions (M&A) is a way f or companiies to expannd rapidly a nd much faaster than or ganic growtth – that is coming fromm existing bbusinesses –– would havve allowed. M&A’s have foor decades bbeen a trillioon‐dollar buusiness, butt empirical sstudies repoorts that a significaant proporttion must bee considereed as failurees.
1) The document discusses working capital management strategies and how they can impact return on capital employed (ROCE). It analyzes data from annual working capital surveys.
2) While a shorter cash conversion cycle should theoretically increase ROCE by reducing capital tied up, the survey data does not show a clear relationship between changes in the cash conversion cycle and changes in ROCE.
3) The document then examines a company that has seasonal demand and discusses strategies for maintaining flexible working capital levels to maximize equity value across seasons while balancing cash availability and opportunity costs.
i. Valuation under uncertainty uses simulation modeling to calculate the value of an entity, debt, and equity over time while accounting for uncertainty.
ii. Key inputs to the model include invested capital, excess marketable securities, capital charges, the net present value of translation gains/losses, forecasted earnings per share, and continuing value.
iii. The model provides probability distributions of the value of the entity, debt, and equity over time which help assess valuation risk under different scenarios.
Strategy@Risk provides strategic risk management software and services. Their models link enterprise strategy to risk management through probabilistic simulation of financial statements and valuation under uncertainty. This allows clients to evaluate risk across strategies, make informed decisions, and increase shareholder value through improved risk management.
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
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This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
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This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
Digital Transformation Frameworks: Driving Digital Excellence
Budgeting with Monte Carlo simulation models
1. Budgeting
Budgeting is one area that is well suited for Monte Carlo Simulation. Budgeting involves personal
judgments about future values of large number of variables like; sales, prices, wages, down‐ time, error
rates, exchange rates etc. – variables that describes the nature of the business.
Everyone that has been involved in a budgeting process knows that it is an exercise in uncertainty;
however it is seldom described in this way and even more seldom is uncertainty actually calculated as an
integrated part of the budget.
In practice budgeting can be performed on different levels:
1. Cash Flow
2. EBITDA
3. EBIT
4. Profit or
5. Company value.
The most efficient is on EBITDA level, since taxes, depreciation and amortization on the short term is
mostly given. This is also the level where consolidation of daughter companies easiest is achieved. An
EBITDA model describing the firm’s operations can again be used as a subroutine for more detailed and
encompassing analysis thru P&L and Balance simulation.
The aim will then be estimation of the firm’s equity value and is probability distribution. This can again
be used for strategy selection etc.
Forecasting
In today’s fast moving and highly uncertain markets, forecasting have become the single most important
element of the budget process.
Page 1 of 9
2.
Forecasting or predictive analytics can best be described as statistic modeling enabling the prediction of
future events or results, using present and past information and data.
1. Forecasts must integrate both external and internal cost and value drivers of the business
2. Absolute forecast accuracy (i.e. small confidence intervals) is less important than the insight
about how current decisions and likely future events will interact to form the result
3. Detail does not equal accuracy with respect to forecasts
4. The forecast is often less important than the assumptions and variables that underpin it – those
are the things that should be traced to provide advance warning.
5. Never relay on single point or scenario forecasting.
All uncertainty about the market sizes, market shares, cost and prices, interest rates, exchange rates and
taxes etc. – and their correlation will finally end up contributing to the uncertainty in the firm’s budget
forecasts.
The EBITDA model
The EBITDA model have to be detailed enough to capture all important cost and value drivers, but
simple enough to be easy to update with new data and assumptions.
The number of variables and goodness of fit to problem
100
"Inadequate"
80
60
Stress
"Good enough"
40
"Sufficient"
20
0
0 20 40 60 80
Number of variables
Input to the model can come from different sources; any internal reporting system or spread sheet. The
easiest way to communicate with the model is by using Excel1 spread sheet ‐ templates.
Such templates will be pre‐defined in the sense that the information the model needs is on a pre‐
determined place in the workbook. This makes it easy if the budgets for daughter companies is reported
(and consolidated) in a common system (e.g. SAP) and can ‘dump’ onto an excel spread sheet. If the
budgets are communicated directly to head office or the mother company then they can be read
1
The model can also read data written in its own native language: FCS/EPS.
Page 2 of 9
4.
and overconfidence4 will stand out as excessive large deviations from the model calculated expected
value (probability weighted average over the interval).
Output
The output from the Monte Carlo simulation will be in the form of graphs that puts all run’s in the
simulation together to form the cumulative distribution for the operating expenses (red line):
100 100
80 80
Probability (%)
60 60
Frequency
40 40
20 20
0 0
870 880 890 900 910 920 930
Operating Expences
In the figure we have computed the frequencies of observed (simulated) values for operating expenses
(blue frequency plot) ‐ the x‐axis gives the operating expenses and the left y‐axis the frequency. By
summing up from left to right we can compute the cumulative probability curve. The s‐shaped curve
(red) gives for every point the probability (on the right y‐axis) for having an operating expenses less than
the corresponding point on the x‐axis. The shape of this curve and its range on the x‐axis gives us the
uncertainty in the forecasts.
A steep curve indicates little uncertainty and a flat curve indicates greater uncertainty. The curve is
calculated from the uncertainties reported in the reporting package or templates.
Large uncertainties in the reported variables will contribute to the overall uncertainty in the EBITDA
forecast and thus to a flatter curve and contrariwise. If the reported uncertainty in sales and prices has a
marked downside and the costs a marked upside the resulting EBITDA distribution might very well have
a portion on the negative side on the x‐axis ‐ that is, with some probability the EBITDA might end up
negative.
In the figure below the lines give the expected EBITDA and the budget value. The expected EBIT can be
found by drawing a horizontal line from the 0.5 (50%) point on the y‐axis to the curve and a vertical line
4
When the reported most likely value are way above expected value (Overconfidence bias, can be cultural or just
lip service).
Page 4 of 9
5.
from this point on the curve to the x‐axis. This point gives us the expected EBITDA value – the point
where it is 50% probability of having a value of EBITDA below and 100%‐50%=50% of having it above.
1
0.8
0.6
Probability
80% 60%
Calculated figure
0.4
0.2
Budget figure
0
40 45 50 55 60 65 70
EBITDA (mill.)
The second set of lines give the budget figure and the probability that it will end up lower than budget.
In this case it is almost a 100% probability that it will be much lower than the management have
expected.
This distributions location on the EBITDA axis (x‐axis) and its shape gives a large amount of information
of what we can expect of possible results and their probability.
The following figure that gives the EBIT distributions for a number of subsidiaries exemplifies this. One
wills most probable never earn money (grey), three is cash cows (blue, green and brown) and the last
(red) can earn a lot of money:
1
0.8
Probability
0.6
0.4
0.2
0
-150 -100 -50 0 50 100 150 200
Budget EBITDA across subsidiaries (mill.)
Page 5 of 9
7. Probability Range Budget-Actual
The figures give the probability of having the Actual result below Budget.
The other end of the bar indicates the probability of having a result below Actual.
100
80
Accumulated Probability
72
70
64
60 63
40
20
0
ry #1 #2 #3 #4
unt un try unt
ry
un try
Co Co Co Co
In the following we have measured the deviation of the actual result both from the budget values and
from the expected values. In the figures the left axis give the deviation from expected value and the
bottom axis the deviation from budget value.
1. If the deviation for a country falls in the upper right quadrant the deviation are positive for both
budget and expected value – and the country is overachieving.
2. If the deviation falls in the lower left quadrant the deviation are negative for both budget and
expected value – and the country is underachieving.
3. If the deviation falls in the upper left quadrant the deviation are negative for budget and
positive for expected value – and the country is overachieving but has had a to high budget.
With a left skewed EBITDA distribution there should not be any observations in the lower right quadrant
that will only happen when the distribution is skewed to the right – and then there will not be any
observations in the upper left quadrant:
100
Deviation from Expected value by subsidary
80
60
40
20
0
-20
-20 0 20 40
Deviation from Budget by subsidary
Page 7 of 9
8.
As the manager’s gets more experienced in assessing the uncertainty they face, we see that the budget
figures are more in line with the expected values and that the interval’s given is shorter and better
oriented.
1
0.8
2007
2008
Probability 0.6 2009
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1 1.2 1.4
Normalized Budget Uncertainty
If the budget is in line with expected value given the described uncertainty, the upside potential ratio
should be approx. one. A high value should indicate a potential for higher EBITDA and vice versa. Using
this measure we can numerically describe the managements budgeting behavior:
Country Country 1 Country 2 Country 3 Country 4 Country 5 Country 6 Country 7
Upside
2,38 1,58 0,77 0,68 0,58 0,56 0,23
Potential Ratio
Rolling budgets
If the model is set up to give rolling forecasts of the budget EBITDA as new and in this case monthly
data, we will get successive forecast as in the figure below:
Probability distribution for EBITDA
Forecast pr; 1/01, 1/02, 1/03, 1/04, 1/05
1
08
0.6
Probability
0.4
02
0
1000 1500 2000 2500 3000
EBITDA
Page 8 of 9