| | 1. Research one of the Monte Carlo analysis Products listed in the Topic Notes I reviewed the following products that developed Monte Carlo analysis package: Monte Carlo Simulation within Microsoft Excel Data Analysis and Business Palisade's @RiskModeling

I really found two of the four solutions excellent.

1. Monte Carlo Simulation within Mocrosoft Excel
I really was amazed by by Monte Carlo Simulation that is available within the Excel Software. The cost of the book Microsoft Excel Data Analysis and Business Modeling by Wayne L Winston costs only $39.99 and includes a practice CD. This solution is outstanding and major companies utilize it such as General Motors, Proctor & Gamble and Eli Lilly. This Excel based solution is the most cost effective. However, the user has to learn and practice the various steps to get to the final results. The users have to learn the various formulas, such as =Rand(), Vlookup, COUNTIF, NORMINV(rand(),mu,sigma) for normal random variable, NORMINV(p, mu, sigma) for normal random varianve with a mean and a standard deviation. Finally, they have to learn the usage of the confidence interval for mean profit by using the formula listed below:

Mean Profit +- 1.96 * profitstd.dev / number iterations
To learn all the steps and apply the formulas require the existing and new staff to be enrolled in training sessions to be able to utilize the product appropriately.

2. I actually recommend the purchase and utilization of the Palisade's @Risk Product under paragraph#2. This product is developed in Excel, as well, however, the model is programmed. This product is user friendly and relatively easy to train the existing and new employees on the utilization. The cost of the professional product is $1,495 while the industrial version...

...Silver Spring , MD, USA
Correspondence: Young Hoon Kwak, Associate Professor of Project Management, Department of
Decision Sciences, School of Business, The George Washington University, Washington, DC 20052,
USA. E-mail: kwak@gwu.edu
A b stra ct
MonteCarlo simulation is a useful technique for modeling and analyzing real-world
systems and situations. This paper is a conceptual paper that explores the applications
of MonteCarlo simulation for managing project risks and uncertainties. The benefits
of MonteCarlo simulation are using quantified data, allowing project managers to
better justify and communicate their arguments when senior management is pushing
for unrealistic project expectations. Proper risk management education, training, and
advancements in computing technology combined with MonteCarlo simulation software allow project managers to implement the method easily. In the field of project
management, MonteCarlo simulation can quantify the effects of risk and uncertainty
in project schedules and budgets, giving the project manager a statistical indicator of
project performance such as target project completion date and budget.
Key wo rds
MonteCarlo simulation, project management, risk analysis and management,
exploratory study
Risk Management (2007) 9, 44–57....

...Application of MonteCarlo Simulation in Capital Budgeting
| |
|by Prit, Aug 2, 2008 |
|The usefulness of Montecarlo Simulation in Capital Budgeting and the processes involved in MonteCarlo Simulation. It also |
|highlights the advantages in some situation compared to other deterministic models where uncertainty is the norm. |
|[pic] |
|Capital budgeting is an important area in Financial Management. Capital Budgeting means the investment in capital projects and|
|identify the projects, which has the highest value adding to the company at the cost of capital. It uses net present value of |
|future cash flows discounted at the appropriate cost of capital and compares it with initial investment and to see whether it |
|is a positive net present value. If the present value is less than the initial investment then the project is rejected. That |
|is the net present value is dependent on future cash flows. |
|In a deterministic model the cash flows are forecasted as a single figure and scenarios are considered one by one and |
|uncertainty of cash flow is not considered....

...Calculation of Pi Using the MonteCarlo Method
by Eve Andersson
Home : Pi : One Calculation
________________________________________
The "MonteCarlo Method" is a method of solving problems using statistics. Given the probability, P, that an event will occur in certain conditions, a computer can be used to generate those conditions repeatedly. The number of times the event occurs divided by the number of times the conditions are generated should be approximately equal to P.
How this program works:
If a circle of radius R is inscribed inside a square with side length 2R, then the area of the circle will be pi*R^2 and the area of the square will be (2R)^2. So the ratio of the area of the circle to the area of the square will be pi/4.
This means that, if you pick N points at random inside the square, approximately N*pi/4 of those points should fall inside the circle.
This program picks points at random inside the square. It then checks to see if the point is inside the circle (it knows it's inside the circle if x^2 + y^2 < R^2, where x and y are the coordinates of the point and R is the radius of the circle). The program keeps track of how many points it's picked so far (N) and how many of those points fell inside the circle (M).
Pi is then approximated as follows:
4*M
pi = ---
N
Although the MonteCarlo Method is often useful for solving problems...

...April 2010
‘The problems of MonteCarlo Simulation’ by David Nawrocki
This article describes the problems associated with using the MonteCarlo Simulation Model
as a tool for determining future investment outcomes for investors. The tool is widely used
by Financial Advisors as a means of showing investors future returns on investments. The
article discusses why the use of MonteCarlo Simulation in financial planning is difficult and
can lead to incorrect decisions which can have a detrimental impact on investors’
expectations of expected returns. The article tells us that MonteCarlo Simulation uses
assumptions based on normal distributions and correlation coefficients of zero, neither of
which are real in the financial markets.
The article discusses why MonteCarlo Simulation should only be used when there is no data available or it is too expensive to implement and why other methods may provide the same or better answers without being assumptive.
The author uses evidence from previous authors highlighting the problems with MonteCarlo Simulation and the use of alternatives as a more accurate way of forecasting future returns for an investor. There are four alternatives shown and discussed, however, the article explores the use of exploratory simulation which states can provide more...

...Preface
This is a book about MonteCarlo methods from the perspective of ﬁnancial engineering. MonteCarlo simulation has become an essential tool in the pricing of derivative securities and in risk management; these applications have, in turn, stimulated research into new MonteCarlo techniques and renewed interest in some old techniques. This is also a book about ﬁnancial engineering from the perspective of MonteCarlo methods. One of the best ways to develop an understanding of a model of, say, the term structure of interest rates is to implement a simulation of the model; and ﬁnding ways to improve the eﬃciency of a simulation motivates a deeper investigation into properties of a model. My intended audience is a mix of graduate students in ﬁnancial engineering, researchers interested in the application of MonteCarlo methods in ﬁnance, and practitioners implementing models in industry. This book has grown out of lecture notes I have used over several years at Columbia, for a semester at Princeton, and for a short course at Aarhus University. These classes have been attended by masters and doctoral students in engineering, the mathematical and physical sciences, and ﬁnance. The selection of topics has also been inﬂuenced by my experiences in developing and delivering professional training courses with Mark Broadie, often in...

...MonteCarlo Simulation
Risk analysis is part of every decision we make. We are constantly faced with uncertainty, ambiguity, and variability. And even though we have unprecedented access to information, we can’t accurately predict the future. MonteCarlo simulation (also known as the MonteCarlo Method) lets you see all the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty
What is MonteCarlo simulation?
MonteCarlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment.
MonteCarlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action.. It shows the extreme possibilities—the outcomes of going for broke and for the most conservative decision—along with all possible consequences for middle-of-the-road decisions.
The technique was first used by scientists working on the atom bomb; it was named for Monte...

...Analysis of Manpower Supply
Wastage Analysis
Manpower wastage is an element of labor
turnover. It is not labor turnover by itself. It
includes- voluntary retirement, normal retirement,
resignations, deaths and dismissals.
Wastage Analysis Curve identifies three different
phases
During induction phase, marginal employees
leave.
During differential transit period, an employee
learns about the organization and identifies his role
in it.
During the period of settled connection, an
employee settles down and decides to stay long.
Analysis of Manpower Supply
Wastage Analysis
Wastage decreases with the increase of length of
service.
Wastage also decreases with the increased skill
exercises and age of employees.
Characteristically wastage of manpower is more
in female than male employees.
It varies with the level of employment and also
exhibits seasonal variations.
Working conditions and size of the firm are also
important variables of manpower wastage.
Different Methods of Wastage Analysis
Labor Turnover Index
Indicates the number of leavers as percentage
of average number of employees.
Average number of employees employed in a
given time period is decided by adding the
employees at the beginning and end then
dividing the same with two.
Stability Index
Indicates stable workforce percentage for a
given period.
Different Methods of Wastage Analysis...

...Reliability analysis in ship’s critical machinery
Objectives
1. Implement Markov process to identify availability of the engines of a vessel.
2. Using MonteCarlo simulation technique to model the Markov process using non-continuous transition rates.
3. Using the simulation model, calculate different reliability cost and worth, using numerous what-if scenarios.
Objectives #1: Implement Markov process to identify availability of the engines of a vessel.
Markov Process
A Markov model of a system operation process is proposed and its selected parameters are determined. A series-parallel multi-state system is considered and its reliability and availability are found. Lastly, the asymptotic approach to reliability and availability of the multi-state series-parallel system in its operation process is applied.
Most maritime systems are very complex and it is difficult to analyze their reliability and availability in their time-varying operation processes. The huge number of components and their operating complexity created difficulty in the evaluation of the reliability and availability of these systems. A very important technique proposed to simplify the reliability and availability evaluation of large systems is the asymptotic approach.
Accordingly, reliability analysis and evaluation is applied to the Markov model of these changing systems operation states. In this model, the variability of system...