Variance Analysis
Variance analysis is a data analysis technique that compares actual project performance to the planned baseline to determine the magnitude and cause of deviations.
Explanation
Variance analysis is the process of comparing planned or expected results with actual results to identify and quantify deviations. In project management, it is primarily used to evaluate cost and schedule performance by calculating schedule variance (SV) and cost variance (CV), as well as their respective performance indices (SPI and CPI). Scope, quality, and risk variances can also be analyzed.
The technique goes beyond simply identifying that a variance exists. It involves investigating the root causes of the variance, assessing its impact on the project, and determining whether corrective or preventive action is needed. Significant variances may trigger change requests or require updates to the project management plan.
Variance analysis is a core technique in the Monitor and Control Project Work, Control Schedule, and Control Costs processes. It is most meaningful when performance is measured against approved baselines, making baseline management critical to effective variance analysis.
Key Points
- •Compares actual performance to planned baselines
- •Key metrics include SV, CV, SPI, and CPI
- •Involves root cause investigation and impact assessment
- •Critical for monitoring and controlling scope, schedule, and cost
Exam Tip
Know the variance formulas: SV = EV - PV, CV = EV - AC, SPI = EV/PV, CPI = EV/AC. Positive values mean favorable; negative means unfavorable.
Frequently Asked Questions
Related Topics
Earned Value Analysis
Earned value analysis (EVA) is a data analysis technique that integrates scope, schedule, and cost data to objectively measure project performance and progress against baselines.
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Trend analysis is a data analysis technique that examines project performance data over time to identify patterns and forecast future performance or outcomes.
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Data analysis techniques are methods used to process, evaluate, and draw conclusions from project data to support informed decision-making and performance assessment.
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