scholarly journals Application of Duration Measure in Quantifying the Sensitivity of Project Returns to Changes in Discount Rates

2019 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Vahidreza Yousefi ◽  
Siamak Haji Yakhchali ◽  
Jolanta Tamošaitienė
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Project Management ◽  
Discount Rate ◽  
Construction Projects ◽  
Project Manager ◽  
Management Systems ◽  
Duration Measure ◽  
The Relationship

In this research, the concept of Duration with a new application in project management has been defined. The Duration of each project provides the project manager with a combined measure containing concepts of return, cost and time of the project. Further in this article, the changes in project return, based on different assumptions such as discount rate, have been examined. To examine the effect of the changes in these factors, the Monte Carlo simulation has been used. The relationship between these factors is nonlinear which reflects the great importance of investment on appropriate risk management systems. The data from a set of construction projects have been used in order to verify the results of this study. Similar relationships can be expected to exist in other industries as well.

An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

2017 ◽  
Vol 12 (3) ◽  
pp. 252-266 ◽  
Author(s):  
Don Cyr ◽  
Lester Kwong ◽  
Ling Sun
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Tail Dependence ◽  
Copula Function ◽  
Bivariate Distribution ◽  
Nonlinear Relationship ◽  
Futures Prices ◽  
The Relationship ◽  
Jel Classifications

AbstractThis paper explores the nonlinearities of the bivariate distribution of Bordeaux en primeur, or wine futures, prices and Parker “barrel ratings” for the period of 2004 through 2010. In particular, copula-function methodology is introduced and employed to examine the nature of the bivariate distribution. Our results show a significant nonlinear relationship between Parker ratings and wine prices, characterized by significant positive tail dependence and higher correlation between high ratings and high prices. Marginal distributions for Parker ratings and wine prices are then identified and Monte Carlo simulation is employed to operationalize the relationship for risk-management purposes. (JEL Classifications: C19, G13, L66)

Mathematical Modeling for Contingency Determination for Nuclear Power Plant Construction Projects

2014 ◽  
Author(s):  
Wei Zhao ◽  
Shenjun Xu
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Construction Projects ◽  
Nuclear Power ◽  
Historical Data ◽  
Plant Construction ◽  
Lump Sum

This paper uses the China AP1000 project as an example to exhibit the application of quantitative risk management in nuclear power plant construction projects. For those lump sum contracts, one of the most significant purposes of quantitative risk management is to determine the contingency, i.e. the reserved money and time for projects. This paper studies the application of Monte Carlo simulation in determining the contingency, taking into account the distinctive features of nuclear power projects. Most nuclear power projects, especially advanced ones such as Generation III and above, meet one common obstacle in estimating key economic indicators — the absence of historical data due to its avant-garde design. As cost estimators of the coal power plant contractors may collect their data from thousands of previous cases, nuclear power plant contractors, especially in many developing countries, do not have a shared database of financial data. Some first-of-a-kind nuclear power plants have absolutely no historical data to look up. This paper aims to provide a resolution to this problem. First, the feasibility and representativeness of different probability distributions are compared based on their respective skewness and kurtosis to determine the best-suited distribution in nuclear power projects. This paper also analyzes the use of second-order Monte Carlo simulation in reducing the error caused by the biased estimation of inexperienced risk assessment engineers.

Construction Engineering Bidding Risk Analysis

2013 ◽  
Vol 671-674 ◽  
pp. 2990-2994
Author(s):  
Fan Jing Yu ◽  
Jun Jie Li
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Construction Projects ◽  
Simulation Method ◽  
Bidding Strategy ◽  
Monte Carlo Simulation Method ◽  
Tender Offer ◽  
Construction Engineering ◽  
And Control

In the Bill of Quantities mode, the bidder must take risks of his tender offer. This paper researched on the uncertainty of construction projects costs, proposed Monte Carlo simulation method and procedure to simulate the construction projects costs on the basis of the construction projects costs conform to the characteristics of normal distribution, and also put forward a method to calculate risks of the tender offer in accordance with the established bidding strategy, which are helpful to the risk management and control of project contracting enterprises.

scholarly journals Project Management vs Systems Engineering Approach to Project Risks Management

2020 ◽  
Author(s):  
D.V. Zlokazov
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Project Management ◽  
Systems Engineering ◽  
Project Manager ◽  
Management Systems ◽  
Negative Consequences ◽  
Project Risk ◽  
Body Of Knowledge ◽  
Project Risks

Risk management is a dynamically developing type of management. Risk management refers to processes associated with identification, risk analysis and decision-making, which include maximizing the positive and minimizing the negative consequences of risk events. Risk elimination is necessary to complete the project on time. Managing risks for a project manager can be easier with using several approaches described in this article. The article presents comparison of widespread approach to managing risks in projects with the set of instruments derived from systems engineering. These approaches are SEBoK (System Engineering Body of Knowledge) PM BoK and OMG Essence. Author tries to integrate sets of instruments present in various project management and systems engineering bodies of knowledge and show how ones derive from the others. Keywords: project, project management, risks of the project, risk management, systems engineering, stakeholders, project requirements, SEBoK

scholarly journals EVALUATION OF CRITICAL SUCCESS FACTORS FOR CONSTRUCTION PROJECTS – AN EMPIRICAL STUDY IN LITHUANIA

2013 ◽  
Vol 17 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Neringa Gudienė ◽  
Audrius Banaitis ◽  
Nerija Banaitienė
Keyword(s):  
Project Management ◽  
Literature Review ◽  
Construction Projects ◽  
Critical Success Factors ◽  
Success Factors ◽  
Project Manager ◽  
Management Team ◽  
Team Members ◽  
Critical Success ◽  
Survey Results

This paper aims to identify a comprehensive list of critical success factors for construction projects in Lithuania. Based on the available literature review, this paper identified 71 success factors under 7 broad groups. Based on the survey results, ten factors including project manager competence, project management team members' competence, project manager coordinating skills, client clear and precise goals/objectives, project value, project management team members' relevant past experience, project manager organising skills, project manager effective and timely conflict resolution, client ability to make timely decision, and project manager experience were determined as the most important success factors for construction projects. These critical success factors are of great significance both to researchers and industry practitioners.

Risks of Using Statistical Analysis Tools in Project Management Environments

2020 ◽  
Vol 7 (2) ◽  
pp. 1-19
Author(s):  
Brian J. Galli
Keyword(s):  
Risk Management ◽  
Project Management ◽  
Statistical Analysis ◽  
Common Type ◽  
Systematic Search ◽  
Statistical Tools ◽  
Early Stages ◽  
Analysis Tools ◽  
The Common ◽  
The Relationship

The purpose of this study is to examine the risks of using statistical tools in a project basis. A systematic search of certain academic databases has been conducted for this study. Statistical tools could be used in a project, and they should be properly planned and designed. Statistical tools include major activities, such as collecting and analyzing data, providing meaningful interpretation, and reporting findings. When dealing with statistical tools, there are several risks that may exist and impact the project either positively or negatively. This study covers a brief outline of the risk management, statistical tools, and the relationship between the two concepts. Finally, a discussion of the common type of risks that are initiated by using statistical analysis tools are provided, which could be planned, identified, and analyzed in the early stages of the project.

scholarly journals A Correlational Study on Project Management Methodology and Project Success

2019 ◽  
Vol 9 (2) ◽  
pp. 56-65
Author(s):  
Michael Pace
Keyword(s):  
Project Management ◽  
Project Success ◽  
Project Managers ◽  
Project Manager ◽  
Correlational Study ◽  
Demographic Information ◽  
Moderating Variables ◽  
Multiple Project Management ◽  
The Impact ◽  
The Relationship

Abstract This non-experimental correlational study extends previous research investigating the relationship between project management methodology and reported project success, as well as the moderating variables of industry and project manager experience. The sample included North American project managers with five years’ experience, 25 years of age or older, and experience with multiple project management methodologies. The survey instrument consisted of 58 questions, utilizing a 5-point Likert scale to record responses. The survey contained three sections, including demographic information, questions related to a successful project, and questions related to a less-than successful (failed / challenged) project. 367 usable responses were received. The examination of the constructs included Pearson’s correlation coefficient as well as linear regression to determine the impact of moderating variables. Results indicated that project management methodology has a weak correlation with reported project success, and this correlation is not moderated by industry nor project manager experience. The results did not align with previously conducted studies, illustrating a need to continue the study of methods impacting success including investigating additional moderating variables.

scholarly journals PROJECT RISK MANAGEMENT BASED ON A SET OF BEST PRACTICES

2021 ◽  
Vol 26 (3) ◽  
pp. 79-86
Author(s):  
Agnieszka JĘDRUSIK
Keyword(s):  
Risk Management ◽  
Life Cycle ◽  
Project Management ◽  
Best Practices ◽  
Project Manager ◽  
Project Risk Management ◽  
Project Risk ◽  
Golden Triangle ◽  
Project Life Cycle ◽  
Project Life

The purpose of this article is to present the process of risk management in project management. The analysis was based on a comparison of two best practices of IPMA and PRINCE. Risk management differs significantly between the two approaches, but it is up to the organization to choose its own management, monitoring and methodology tailored to the specific industry or sector. Risk management is an important aspect of the entire project life cycle and must be monitored throughout the project life cycle to protect not only the budget but all areas of the so-called "golden triangle". A very important aspect is the organization's awareness that risk management is everyone's responsibility, not just the project manager. This paper presents two different approaches to project risk management in two different methodologies.

Sign in / Sign up

Export Citation Format

Share Document