scholarly journals A DEEPER INSIGHT IN SOME EFFECTS IN PROJECT RISK MANAGEMENT

2014 ◽  
pp. 214-220
Author(s):  
Wolfgang Tysiak
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Program Evaluation ◽  
Project Risk Management ◽  
Time Schedule ◽  
Project Risk ◽  
Popular Approach ◽  
Project Plan

This document shows a possible way how to deal with insecurities in the time schedule of a project plan. It shows that Program Evaluation and Review Technique (PERT), the most popular approach to handle this, bears some severe disadvantages. Furthermore it offers an alternative to overcome them by using Monte Carlo simulation. Finally it can be claimed that a complete change of paradigm is necessary: If you have any insecurities as inputs, everything becomes insecure. This might on the first sight convey the impression that the whole situation converts more complex, but we should rather accept this as the opportunity to apply all the well-known instruments from statistics.

scholarly journals PROJECT RISK MANAGEMENT USING MONTE CARLO SIMULATION AND EXCEL

2010 ◽  
pp. 362-367
Author(s):  
Wolfgang Tysiak ◽  
Alexander Sereseanu
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Project Risk Management ◽  
Main Task ◽  
It Projects ◽  
Project Risk ◽  
Quantitative Risk Analysis ◽  
Standard Product

In every project, especially in software and IT projects, there is the need to perform an elaborated risk management. One main task that often causes problems is the quantitative risk analysis. In this article we will show how to deal with this by using a well-known standard product: EXCEL.

On Using Monte Carlo Simulations for Project Risk Management

2016 ◽  
pp. 150-173
Author(s):  
Cristiana Tudor ◽  
Maria Tudor
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Decision Makers ◽  
Project Schedule ◽  
Project Risk ◽  
Monte Carlo Simulation Technique ◽  
Basic Probability ◽  
The Cost

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.

The Research of Construction Project Risk Management Based on Monte Carlo Method and Fuzzy Mathematics

2012 ◽  
Vol 442 ◽  
pp. 341-345
Author(s):  
Si Dong Xu ◽  
Xiao Li Cai ◽  
Wei Liu
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Qualitative Analysis ◽  
Quantitative Research ◽  
Construction Project ◽  
Negative Influence ◽  
Fuzzy Mathematics ◽  
Project Risk

A lot of risks of management occur under the process of the construction project, and it will always bring more tremendous negative influence to the project goal. Therefore, there should have the effective risk method for the project superintendent to forecast the risk occurrence and reduce the loss which the risk brings. There are lots of methods about risks management under the process of the construction project at present and most of the method emphasize particularly on qualitative analysis. This article presents a method which base on the knowledge of Monte Carlo Simulation. It will combine the qualitative analysis and quantitative research and puts forward a new solution.

High-level Overview of Project Risk Management (PRM)

2020 ◽  
pp. 11-34
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Deep Understanding ◽  
Risk Assessments ◽  
Biased Sampling ◽  
Project Risk Management ◽  
Parametric Methods ◽  
Project Risk ◽  
High Level ◽  
Selection Of

Selection of adequate project risk management (PRM) methodologies should be based on deep understanding of PRM context of a project to avoid PRM context—PRM method mismatch. Various realizations of bias can impede the selection. All PRM methodologies can be grouped as either conventional (they follow traditional PRM process steps to identify, evaluate, address, monitor, etc.) and unconventional (they are focused mostly at risk assessments). All PRM methodologies—deterministic (scoring), probabilistic (Monte Carlo), parametric, etc—have limitations. Deterministic (scoring) methods can help with development of risk addressing although they are useless for development of project contingencies. Monte Carlo methods can be used for development of project contingencies only if they take into account all relevant components of project risk exposure consistently. Parametric methods suffer from using biased sampling—convenience and judgement sampling—that undermine their accuracy. Two emerging methodologies—system dynamics and artificial neural networks (ANN)—can be considered unconventional.

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