Project variance analysis is an important technique that allows project teams to constantly compare planned performance with actual project data. Hence, it assists project teams in identifying and analyzing deviations in project performance .
Earned Value Management (EVM) system also offers mathematical equations to calculate variances. However, not all organizations use evm for project monitoring and controlling. Thus, variance analysis becomes an important tool to analyse project performance.
What is Variance Analysis?
The Guide to Project Management Body of Knowledge (PMBOK)® defines variance analysis as
A technique for determining the cause and degree of difference between the baseline and actual performance.
In simple terms, variance analysis is the variation between plan and actual project performance. It further helps to identify causes and assess severity of deviation. Moreover, the planned performance is any project performance metrics like schedule, cost, scope and risk.
An important aspect of variance analysis is
As execution team accomplishes more work, the acceptable range of variance decreases.
Variance Analysis Steps
Analyzing variances is not difficult, however, it requires a great deal of discipline in data collection and interpretation.
To begin with, project team identifies deviation in baseline performance. It further establishes causes of variances and assesses severity of impact. Thereafter, the team implements corrective actions to restore project performance. Finally, the team proposes preventive actions to avoid future occurrences.
A well structured variance analysis should include the following aspects.
- Identify the affected key performance indicators?
- Assess the quantum of deviation?
- Estimate the degree of impact on project performance?
- Identify the causes of variation?
- Establish the corrective action?
- Estimate the resources required to implement the corrective action?
- Establish time schedule required to implement the corrective action?
- Recommend preventive action?
Variance Analysis in Project Management
PMBOK 5th edition identifies variance analysis as one of the eleven analytical techniques. It is an effective tool to control various aspects of project performance such as scope, schedule, cost and risk.
Most of the projects suffer from frequent changes to project scope. At times project teams fail to control changes and consequently fail to ascertain impact of changes. Such uncontrolled expansion of project scope, without adjustments to schedule, budget, risks, and resources is known as scope creep.
PMBOK lists variance analysis as the only tool to control project scope. Steps to control scope changes mainly include the following;
- Implement change control processes
- Regular review of project scope baseline
- Plotting project deliverable on S – curve and tracking progress on weekly basis can also help visualize changes to project scope.
Variance analysis as a schedule control technique is also part of earned value management methodology. EVM is also an analytical tool to control all three critical project performance indicators namely scope, schedule and cost. EVM uses measures such as Schedule Variance (SV) and Schedule Performance Index (SPI) to indicate variances and performance efficiency.
But there are many organizations that do not use evm techniques for project schedule control. However, they use similar techniques to measure deviations. These techniques chiefly include measuring variation in planned start and finish dates. Additionally, it also involves comparing duration spent in achieving planned targets against a baseline plan. Thereafter, the team records this deviation for further analysis and formulating remedial actions.
Undoubtedly, project cost control is a crucial monitoring and control aspect of any project. Moreover, earned value management methodology facilitates easy assessment of project cost performance. It mainly uses Cost Variance (CV), Cost Performance Index (CPI) and Variance at Completion (VAC) to establish cost variances.
However, there are organizations that do not implement earned value management techniques. Therefore, these organizations implement procedures that track actual cost booked on work packages or on various project activities. Thus comparing actual cost booked with approved budget gives the cost variance.
Project risk analysis uses data obtained from variance analysis of scope, schedule, and costs. This not only helps project teams to set risk thresholds but also compare with existing ones. If risk variances exceed the desired threshold then project risk mitigation plans come into effect.
Causes of Variance in Projects
Projects deliver a unique product or a service. However, projects are subject to various changes throughout their life cycle. As a result, actual key performance indicators deviate from the desired project performance. Therefore, key to successful project management lies managing changes. Rejecting changes is not a solution because changes may have a positive impact on project outcome as well.
For factors responsible for variance in project performance refer to the following.
- Changes in project scope triggered by end-user or project team in order to meet the contractual obligations.
- Lack of resources such as skilled manpower, availability of equipment and material.
- Wrong activity duration estimates.
- Improper identification of critical schedule activities.
- Improper project reviews.
- Lack of or poorly implemented project monitoring and controlling processes.
- Poor risk assessment.
- Not adopting change control procedures.
- Changes in regulatory framework?
- Evolving business needs
- Modifications to end-user requirements?
- Market factors such as changes in raw material prices, exchange rate variations etc.
Variance Analysis Formula
The Guide to Project Management Body of Knowledge (PMBOK)® defines variance as:
A quantifiable deviation, departure, or divergence away from a known baseline or expected value.
In other words variance analysis involves calculating difference between planned and actual data. However, Earned Value Management project management system and tools like MS projects make good use of this quantitative technique. Moreover, MS project has dedicated mathematical equations to calculate variances.
For widely used variance analysis formula refer to list below.
Variance Analysis Formula Earned Value Analysis
Variance analysis in earned value management consists of estimating schedule variance, cost variance and variance at completion. The following paragraphs enumerates various earned value formula for variance calculations.
- Schedule Variance (SV) indicates if the project is ahead or behind schedule.
Schedule Variance (SV) = Earned Value (EV) – Planned Value (PV)
- Cost Variance (CV) specifies if the project is experiencing cost overrun or not
Cost Variance (CV) = Earned Value (EV) – Actual Cost (AC)
- Variance At Completion (VAC) indicates if the project will be over or under budget at the time of completion.
Variance At Completion (VAC) = Budget at Completion (BAC) – Estimate at Completion (EAC)
Earned Value Management Variance Analysis Example
In order to understand application of variance analysis in earned value management (EVM) please refer the following post.
Also read: Earned Value Management Example
For more on earned value management please refer to the following posts.
- Earned Value Management System
- Earned Value Management Analysis
- Forecast Project Cost Using EVM Techniques
- Earned Value Management Challenges
Variance Analysis Formula Microsoft Project
Microsoft Project calculates project variances using built-in mathematical functions. Further, it facilitates presentation of data in graphical format. This not only simplifies data presentation but also makes data analysis a lot easier.
The following paragraph describe popularly used variance analysis techniques in Microsoft Project.
Following is the formula that Microsoft Project uses to calculate Start Variance Analysis.
Start Variance: Start – Baseline Start
- (Start Variance = 0) A start variance equal to zero signifies no deviation and the task will start as per the planned date.
- (Start Variance > 0) Positive start variance means that task will start after the baseline start date. This also implies that the task will start after the planned date.
- (Start Variance < 0) Negative stat variance indicates that the task will start before the baseline start date. This is a desirable condition and it means that the task will start ahead of schedule.
The following formula represents mathematical equation for Finish Variance Analysis in Microsoft Project.
Finish Variance: Finish – Baseline Finish
- (Finish Variance = 0) A finish variance equal to zero signifies no deviation and the task will finish as per the planned date.
- (Finish Variance > 0) Positive finish variance means that task will finish after the baseline start date. This also implies that the task will finish after the planned date.
- (Finish Variance < 0) Negative finish variance indicates that the task will finish before the baseline start date. This is a desirable condition and it means that the task will finish before schedule.
The following formula represents mathematical equation for Duration Variance Analysis in Microsoft Project
Duration Variance: Duration – Baseline Duration
- (Duration Variance = 0) It signifies no deviation and that the task will take same amount of time as per the original plan.
- (Duration Variance > 0) Positive duration variance means that task will take more time than the plan.
- (Duration Variance < 0) Negative duration variance indicates that the task will take less time to complete than the original plan. This is the most desired condition.
Also read: Project Management Formulas
Finally, in this post we have seen application of variance analysis in scope, schedule and cost control. This technique also helps organisations that do not implement earned value management system. Further, Microsoft Project provides variance analysis data for each activity in the schedule. However, MS project functions work only when project schedule has a baseline. This further asserts the importance of creating a baseline of project schedule to compare plan and actual data.