A deeper insight in some effects in project risk management (original) (raw)
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DEALING WITH INSECURITIES AND RISKS IN PROJECT MANAGEMENT
Current Issues in Management of Business and Society Development - 2011, 2011
Every project contains risks in one of the dimensions costs, time, and quality. Therefore in project management there is the strong need to perform an elaborated risk management. Especially the calculations with all the stochastic estimates (probabilities, densities, distributions etc.) often cause problems in the quantitative risk analysis. In this article we will show how to deal with this by using Monte Carlo simulation and the well-known standard tool EXCEL.
On the Project risk baseline R3
Computers & Industrial Engineering, 2021
Obtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions with the shortest duration are considered the best-known schedule for that project. However, no tools currently select which schedule best performs in project risk terms. To bridge this gap, this paper aims to propose a method for selecting the project schedule with the highest probability of meeting the deadline of several alternative schedules with the same duration. To do so, we propose integrating aleatory uncertainty into project scheduling by quantifying the risk of several execution alternatives for the same project. The proposed method, tested with a wellknown repository for schedule benchmarking, can be applied to any project type to help managers to select the project schedules from several alternatives with the same duration, but the lowest risk.
On the project risk baseline: Integrating aleatory uncertainty into project scheduling
Computers & Industrial Engineering, 2021
Obtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions with the shortest duration are considered the best-known schedule for that project. However, no tools currently select which schedule best performs in project risk terms. To bridge this gap, this paper aims to propose a method for selecting the project schedule with the highest probability of meeting the deadline of several alternative schedules with the same duration. To do so, we propose integrating aleatory uncertainty into project scheduling by quantifying the risk of several execution alternatives for the same project. The proposed method, tested with a well-known repository for schedule benchmarking, can be applied to any project type to help managers to select the project schedules from several alternatives with the same duration, but the lowest risk.
Project risk management: a quantitative approach through simulation tecniques
2009
Risk Management, in general, is a process aiming at an efficient balance between realizing opportunities for gains while minimizing vulnerabilities and losses. Risk Management should be an endlessly recurring process consisting of phases which, when properly implemented, enable continuous improvement in decision-making and performance improvement. This paper examines the use of simulation techniques in a quantitative risk evaluation. Simulation techniques allows to deal with stochastic problems with many variables and easily view the impact of these variables on the results obtained from the construction of a comprehensive model capable of representing all the possible evolutions of the project. This approach is applied to a case study in a telecommunications service company, in which typically the project life cycle is lengthy and there are many constraints to final authorization to proceed. Hence the attempt is to influence the internal variables in the project to improve the outcomes. These results will be briefly described by the cumulative distribution functions showing how markup can be optimized to improve project performance.
Estimating projects duration in uncertain environments: Monte Carlo simulations strike back
Proceedings of the 22. IPMA world congress, 2008
PERT (Program Evaluation and Review Technique), developed in the 1950's, represented the first attempt to incorporate uncertainty in project scheduling. Despite some weaknesses, it is still widely used in project management mostly thanks to the simplicity of its algorithm in operating on activity network diagrams. Today the increasing complexity of projects requires new techniques and the increasing availability of computer power have not brought project simulation into common usage as expected. Although several reviews assert that ...
Influence of Risks on Project Planning Decision
2016
In this research, a "Project planning / Risk analysis" model that consists of four modules has been developed to aid the decision-maker in planning different types of projects and analyze the risks. These modules are: Planning, Decision-Making Process 1, Risk Analysis, and DecisionMaking Process 2.The model can be used to generate different scenarios of project plan according to the decision-maker's opinion in choosing the type of probability distribution, changing the probability/impact of the risk occurrences and/or changing the input values (time/cost) into the probability distribution. These scenarios will be resulted by Monte Carlo simulation as well as the application of qualitative techniques to assess risks and combining their probability of occurrence and impact, and quantitative techniques to numerically analyze the effect of identified risks. Moreover, it gives the decision-maker the ability of avoiding unexpected events through providing a futuristic look o...
KSCE Journal of Civil Engineering, 2019
The Program Evaluation and Review Technique (PERT) model uses parameters such as the specified project completion time, mean, and variance to estimate the probability of project completion time. However, this model uses a weighted average and unweighted value in the variance, which is based on six sigma of the mean. Despite many proposed modifications to improve the traditional PERT model, the hidden error in the calculation of the variance and mean of the PERT approach has not been adequately addressed. This error leads to underestimation of the schedule risk. Considering the impact of variance and mean on the probability of project completion times, this study contributes to the improvement of the accuracy of schedule risk estimation by proposing a modified variance and mean of the original PERT model. The original PERT model was first used to estimate the project completion time. However, using the proposed modified model to estimate the completion time, a 95% confidence interval assumption and the corresponding distribution within ±2 standard deviation of the mean and standard or Z values were employed to model the new mean and variance equations. To prove the validity of the proposed modified variance and mean assumptions, we performed a schedule risk analysis through simulation using Oracle Crystal Ball for comparison. The results showed that the proposed PERT model had a better mean error rate of 2.46% as compared to 3.31% of the original PERT model.
Comparison of Project Scheduling techniques: PERT versus Monte Carlo simulation
Industrial Engineering Journal, 2018
With an increase in the focus on achieving customer satisfaction, manufacturing industries are aiming to optimise their processes to a great extent. In any project the constraints of schedule, budget, scope and quality which form the basis of the project management triangle can be fulfilled by implementing project management planning tools & techniques appropriately. In this research study PERT (Program Evaluation and Review Technique) is applied on a project to evaluate the probability of project completion. Another scheduling tool which has gained popularity in recent times is the Monte Carlo simulation. This technique is applied on the same project to perform schedule risk analysis by evaluating the criticality index. The results of both the techniques are compared using hypothesis test to evaluate the more suitable one which can be used practically as a scheduling tool.
Risk Analysis for Project Management
2018
This paper illustrates various activities, where identification of risk is a primary task before its actual occurrence whereas proper plan can be done and executed to handle the risk during project period and complete the project successfully. The study has been done to find out the common risk, during project. The study was based on data, having experiences of 5 to 20 years from various fields contributing in different project phases. During survey it was found that 72% of respondents think that the budget and resource provided for project are probably sufficient, with margin. It may not be 100% realistic but can be managed with some addition efforts. Budget is complete financial estimation of the cost required for each and every task to achieve the goal or success of the project over project life cycle. 60% of respondents believe that timeline provided by the institute for the project is not enough to compete the scheduling and planning of the project. According to 80% respondents...
2016 Portland International Conference on Management of Engineering and Technology (PICMET), 2016
Project managers are often confronted with the question on what is the probability of finishing a project within budget or finishing a project on time. One method or tool that is useful in answering these questions at various stages of a project is to develop a Monte Carlo simulation for the cost or duration of the project and to update and repeat the simulations with actual data as the project progresses. The PERT method became popular in the 1950's to express the uncertainty in the duration of activities. Many other distributions are available for use in cost or schedule simulations. This paper discusses the results of a project to investigate the output of schedule simulations when different distributions, e.g. triangular, normal, lognormal or betaPert, are used to express the uncertainty in activity durations. Two examples were used to compare the output distributions, i.e. a network with 10 activities in sequence and a network where some of the activities are performed in parallel. The results indicate that there is no significant difference in the output distributions when different input distributions with the same mean and variance values are used.