The Case for Truly Integrated Cost and Schedule Risk Analysis

2017 ◽  
pp. 76-108
Author(s):  
Colin H. Cropley
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Complex Projects ◽  
Schedule Risk ◽  
Project Risks ◽  
Mc Simulation ◽  
Integrated Cost ◽  
Schedule Risk Analysis ◽  
Relationship Of ◽  
Cost Outcomes

Time and cost outcomes of large and complex projects are forecast poorly across all sectors. Over recent years, Monte Carlo (MC) simulation has increasingly been adopted to forecast project time and cost outcomes more realistically. It is recognised that the simultaneous analysis of time and cost impacts makes sense as a modelling objective, due to the well-known relationship of time and money in projects. But most MC practitioners advocate the use of Schedule Risk Analysis (SRA) feeding into Cost Risk Analysis (CRA) because they believe it is too hard to perform Integrated Cost & Schedule Risk Analysis (IRA) realistically. This chapter elaborates an IRA methodology that produces realistic forecasts without relying on questionable assumptions and enables identification and ranking of all sources of cost uncertainty for risk optimisation as part of the process. It also describes an extension of IRA methodology to include assessment of the assets produced by the project as well as the project itself, thus enabling the analysis of business risks as well as project risks.

Monte Carlo Project Risk Analysis

2017 ◽  
pp. 109-129 ◽  
Author(s):  
Ruchi Agarwal ◽  
Lev Virine
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Monte Carlo Simulations ◽  
Statistical Distributions ◽  
Project Risk ◽  
Process Convergence ◽  
Schedule Risk ◽  
Schedule Risk Analysis ◽  
Event Based ◽  
Project Risk Analysis

Monte Carlo simulations of project schedules have become one of the foundations of quantitative project risk analysis. Monte Carlo method helps to determine the chance that project will be completed on time and on budget, expected project cost and finish time given risks and uncertainties, as well as identify critical risks and crucial tasks. There are a number of ways how Monte Carlo schedule risk analysis can be conducted. “Traditional” Monte Carlo schedule analysis is performed based on statistical distributions of task duration, cost and other input parameters. Event-based quantitative risk analysis incorporates risk events, which can affect project schedules. The chapter discusses a number of important concepts related to Monte Carlo simulations: statistical distribution, sampling process, convergence monitoring, sensitivity analysis, probabilistic and conditional branching and others.

3.5.5 Integrated Cost / Schedule Risk Analysis

2002 ◽  
Vol 12 (1) ◽  
pp. 943-951 ◽  
Author(s):  
David T. Hulett ◽  
Bill Campbell
Keyword(s):  
Risk Analysis ◽  
Schedule Risk ◽  
Integrated Cost ◽  
Schedule Risk Analysis

Analysis of Project Shedule Risk Indexes in PERT Network Using Monte Carlo Simulation

2013 ◽  
Vol 760-762 ◽  
pp. 2205-2211 ◽  
Author(s):  
Yuan Liu ◽  
Zhuo Fu Wang
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Risk Index ◽  
Critical Index ◽  
Project Schedule ◽  
Schedule Risk ◽  
Schedule Risk Analysis ◽  
Construction Schedule

It is convenient and effective to use Monte Carlo simulation (MCS) technique in project schedule risk analysis and assessment, but at the same time, the indexes put forward by scholars up to now is quite few, leaving construction schedule risk assessment still difficult to carry out. Therefore, based on PERT network assumption, the shortcomings of current project schedule risk indexes are summarized and new project schedule risk index is put forward to estimate the criticality of each activity and path to provide more information for project schedule controllers. In the case studied, with the application of the new index, the critical index of each activity is given and divided into five levels, and the new index put forward in this paper shows great superiority over the classic indexes.

SCHEDULE RISK ANALYSIS OF RAILWAY PROJECTS USING MONTE CARLO SIMULATION FOR IMPROVED PROJECT MANAGEMENT

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Motlatso Mabeba ◽  
Jan Harm C Pretorius ◽  
Leon Pretorius
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Road Transport ◽  
Time Frame ◽  
Rail Transport ◽  
Logistics Industry ◽  
The Poor ◽  
Schedule Risk ◽  
Schedule Risk Analysis

Railways have been used throughout history for the transportation of goods. Even though the inception of rail transport improved civilization, due to its inefficiencies, road transport is at present dominating the freight and logistics industry. Company A, which has the largest market share in the rail freight business, has embarked on projects to improve rail efficiencies by moving higher volumes of freight timeously. Most of the projects embarked on by Company A have failed largely due to the poor planning of the projects in the feasibility stages. Most of the planning schedules are overoptimistic and unrealistic making them unreliable and difficult to track. The scope of this study was to investigate the way in which planning schedules of Company A are developed by undertaking a schedule risk analysis on one of the planning schedules titled 'Design of railway exchange yard' and using Monte Carlo simulation to validate the schedule. If projects of Company A can be planned better, using schedule risk analysis, projects can become more successful and completed within the required time frame.

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