Schedule Risk Analysis

Correct Answer: The degree of shared risk factors and their combined impact on activity duration estimates

When evaluating correlation coefficients between schedule items using triangular distributions, the key consideration is understanding shared risk factors and their combined impact on activity durations. This is correct because: - Correlation coefficients measure the strength and direction of relationships between activities - Shared risk factors (like weather, resource constraints, or vendor delays) affect multiple activities simultaneously - Understanding these relationships is crucial for accurate schedule risk analysis - The combined impact helps determine realistic duration estimates The other options are incorrect because: - Mathematical products of risk probabilities oversimplify the relationship and don't accurately reflect real-world correlations - Standard deviation calculations from historical data alone don't properly account for current project-specific correlations - Resource availability patterns, while important, are not the primary focus when evaluating correlation coefficients in schedule risk analysis using triangular distributions

Correct Answer: Evaluating the criticality index through Monte Carlo simulation iterations

Monte Carlo simulation through criticality index evaluation is the most reliable method for determining potential critical paths during project execution. Here's why: The criticality index, calculated through Monte Carlo simulation iterations, shows the percentage of time a particular path becomes critical across multiple simulation runs. This provides a statistical probability of each path becoming critical, accounting for various uncertainties and risks. Why other options are not optimal: Comparing total float values to baseline estimates is static and doesn't account for the dynamic nature of schedule risks and uncertainties. Deterministic schedule compression techniques don't consider probabilistic elements and multiple scenarios that could affect the critical path. Schedule sensitivity analysis based on resource constraints, while valuable, doesn't provide the comprehensive statistical insight into path criticality that Monte Carlo simulation offers. Monte Carlo simulation stands out because it: - Runs hundreds or thousands of iterations - Considers multiple scenarios and uncertainties - Provides statistical confidence levels - Accounts for complex schedule relationships - Identifies paths that may become critical under different conditions

Correct Answer: Apply correlation coefficients based on historical relationships

In Schedule Risk Analysis, applying correlation coefficients based on historical relationships is the best approach for handling dependent activities with different probability distributions. This is correct because: - Historical data provides real-world evidence of relationships between activities - Correlation coefficients mathematically represent the strength and direction of dependencies - This approach maintains the statistical integrity of the analysis - It accurately models how changes in one activity affect related activities Other approaches are incorrect because: - Using weighted averages oversimplifies the relationship and loses important statistical information - Simply summing probability distributions is mathematically incorrect as it fails to account for the nature of dependencies - Applying the most pessimistic distribution to all activities is overly conservative and does not reflect reality of project variations