Dst Assignment 2

8/7/2019 Dst Assignment 2

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Ghulam M Kanhar 099041830

Q : 1

Introduction

Each of our decisions we constantly confronted with uncertainty, doubt and inconsistency. Despite

of we have access to information is not unprecedented; we cannot accurately predict

the future. Monte Carlo simulation can See all possible outcomes of our decisions and assess

the incidence of risk allowing decision making process better under uncertainty. Monte Carlo is being

used from 1940’s this is best way to simulate analyse the risk and give the best solution, opportunities

probabilities to the situation. To meet any desired goal in this global competition manger plans to

meet their required task and they look forward to uncertainties that a project will go through. This risk

assessment of project helps to completion of project. The Monte Carlo simulation is all about this risk

assessment and allows making decision better under uncertainty. (Hammersley, Handscomb, 1964)

Monte Carlo simulation

Monte Carlo simulation is a computerized mathematical technology that allows people to calculate the

risk quantitative analysis and decision making. This technique is used by specialists in these various

areas throughout the wide financial, project management, energy, manufacturing, engineering,

research and development, insurance, oil and gas, transport and the environment. Monte Carlo

simulation provides the decision makers a range of possible outcomes and opportunities to discuss the

options of working. Monte Carlo simulation analysis construct a risk model and replace the set of

values probability distribution of any factor that has inherent uncertainty and present a model that

calculates all results and random values of probability function. Monte Carlo may cause the

simulation of thousands of new calculation before it completes. Monte Carlo method is of use to

mould with major changeability and uncertainty in inputs. For studying system it is very important

technique, due to high flexibility in simulation approach; one can mould a composite domain by using

Monte Carlo simulation. So, that is why this method is highly used commercially and it has rapid

results. In Monte Carlo simulation different values are selected for individual doubt without allowing

for any relation to each doubt. Practically there is always there is a link between uncertainties. So, in

Monte Carlo each uncertainty is independent and consequently a large number of weird modes are

resulted in Monte Carlo Simulation, which makes confusion in expected outputs. Monte Carlo

simulation is makes manager or project head in position to make right decision with less risk and

uncertainties. (Wu, 2008)

Advantages of Monte Carlo Simulation

It minimised the efforts

It is very easy to calculate the risk and helps to take decision.

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It May be the only tool accessible when mathematical analysis methods are not

accessible.

It is also useful if mathematical analysis methods are available but very complex.

It let comparisons of alternative designs or alternative operating policies.

It let time compression or expansion

Disadvantages of Monte Carlo simulation

For a stochastic model, simulation estimates the output

When if an analytical solution is available it produces the exact output

Often it is expensive and time consuming to develop

It is possible an invalid model might result with confidence in wrong results.

There are two different approaches by which we can calculate the risk. There are so many ways to

assess the risk of the project. It totally depends upon the manger of the project to go through any risk

assessing approach according to location and condition. But the most profitable methods out these are

decision tree and failure mode effect and criticality analysis.

The Decision Tree

Decision trees are useful tools to help you choose between several courses of action.

They provide a very effective structure through which you can explore the options, and to

investigate the possible outcomes of choosing those options. It also helps to form a balanced

picture of the risks and rewards associated with each possible course of action.

This makes them especially useful for choosing between different strategies, and projects or

investment opportunities, especially when your resources are limited.

Failure Mode Effect and Criticality Analysis (FMECA)

Failure mode effect and criticality analysis is a set of activities which identify and evaluate the failure

of the product that happens during documents processing. It involves suggestions to recognize all

potential failure happens. It assigns numerical priorities to all modes. By this we can track

documenting corrective actions.

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Q : 2

Histogram Diagram

Cumulative Probability Diagram

What is Histogram

A graph display that construct a frequency table with different values given. It plots the graph from

low to high on the x-axis. It is used to examine and identify variables range and propose variables

central tendency.

What is Cumulative frequency

Cumulative frequency is defined as the running total of frequencies is called cumulative frequency.

Q : 3

The company is making finical planning of project. Finance is the backbone of every project so

management make bright efforts in scheduling of project that its task should complete in due time and

within rewarded budget. To find out which decision and which cost is better project makers go

through with different approaches with respect to physical and economical conditions. Several

approaches are used for risk assessments which help us to find out which decision is better. These

approaches are decision tree, cumulative frequency, FMECA, Monte Carlo technique etc are the risk

assessment tools by which project manager will be 95 % confident that overspend will not occur in

the project.

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REFRENCESS

Hammersley, J.M, Handscomb, D.C. (1964), “Monte Carlo Methods”, 1st edition, Fletcher &

son ltd, Norwich.

Rezaie, K, Amalnik, M.S, Gereie, A, Ostadi, B, Shakhseniaee, M. (2007), “Using extended

Monte Carlo simulation method for the improvement of risk management: Consideration of

relationships between uncertainties”, Applied Mathematics and Computation, Volume 190,

Issue 2, pp 1492-1501

Smid, J.H, Verloo, V, Barker,G.C, Havelaar, A.H, (2009), “Strengths and weaknesses of

Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment”,

International Journal of Food Microbiology.

Susan Coles, S., Rowley, J., (1995), “ Revisiting decision trees”, Management Decision, MCB

University Press Limited, Volume 33, Numbers 8, pp. 46-50

Wu, Y.F. (2008), “Correlated sampling techniques used in Monte Carlo simulation for risk

assessment”, International Journal of Pressure Vessels and Piping, volume 85, issue 9, pp

662-669.

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Dst Assignment 2