What is variance analysis?

The variance analysis, also known as a flux analysis, compares two points of financial data. The goal is to take actual costs and quantify any deviations from the initial projections. The information gained from a variance analysis helps finance leaders and their teams understand business activity relative to established financial forecasts. This process can help drive informed decision-making and help organizations build more flexible budgeting and integrated financial planning practices.

Variance analysis falls in the broader field of financial planning and analysis (FP&A). The process is also evolving as artificial intelligence (AI)-driven forecasting and financial analysis become the status quo.

AI in FP&A is reimagining the planning, budgeting and forecasting process, leading finance teams to conduct automated variance analysis. Financial forecasting software uses AI capabilities through real-time data integration, data analysis and predictive modeling, giving finance teams the tools to gain deeper insights and improve accuracy.

Variance analysis plays a crucial role in financial management, especially for accounting teams that maintain accurate historical financial data and track the organization’s financial performance. These individuals must uncover errors or omissions in accounting data so that the data feeding into other analyses across an organization is reliable.

Separately, variance analysis can be valuable for fraud detection. The analysis compares actual results to expected values, so any unusual activity or major discrepancies can be flagged immediately and addressed.

Perhaps the most important role of variance analysis is prioritizing resources. The analysis can target key areas where activity is not going as planned and prioritize corrective actions.

For example, through variance analysis, a company offering five products learns that the actual sales for two of them are lower than projected the projected sales. The accounting team can then focus on what is happening with those two products and take strategic steps forward.

A variance analysis is typically performed at the end of a financial close to compare actual results against a benchmark, such as a budget. The analysis can be applied to any level of financial information, from a financial statement to a single line item.

The analysis can be applied to all three major financial statements: the balance sheet, income statement and cash flow statement. The focus for each one can be on any component. Furthermore, the components that an organization chooses will depend on business priorities.

There are several key terms used in variance analysis that are helpful to understand:

• Purchase costs: The total amount of money it takes to buy a product or service.

• Indirect costs: The operating expenses required to function, such as rent or utilities.

• Favorable versus unfavorable variance: A favorable variance occurs when actual results are better than the projection. An unfavorable result occurs when actual expenses exceed the forecast.

• Variable price: The change in the cost of a good or service.

• Fixed budget variance: The difference between actual financial results and the original, static budget.

There are several types of variance analysis, depending on the organization’s context and goals. The choice will also depend on the industry, the specific drivers and the key performance indicators (KPIs) being prioritized.

A revenue variance analysis is the difference between actual revenue and expected revenue for the specific period.

It is a useful analysis for understanding profitability and how well a company’s actual sales performance aligns with the projected figures.

This variance analysis is the difference between the budgeted cost of a project and the actual cost incurred.

The cost variance (CV) can be a positive CV (meaning the actual cost came under budget) or a negative CV (meaning the actual cost came over budget). A CV can be helpful for implementing cost savings measures and tracking financial progress.

The material variance measures the difference between the expected cost of raw materials and the actual price paid.

The results can be broken into material price variance (MVP) and material quantity variance, which compares actual use to the standard quantity. This analysis can help finance teams analyze procurement inefficiencies and break down material costs.

This variance analysis compares the planned labor cost with the actual labor cost incurred.  

Labor variance takes the actual hourly wage rate and compares it to the standard expected rate and multiplies it by the actual hours worked. These figures, often categorized as a labor rate variance, can help organizations evaluate workforce efficiency and wage cost control. The differences are split into two categories: labor costs and time used.

An efficiency variance measures the difference between the actual quantity of inputs, such as labor hours or materials used and the expected inputs needed for a unit of output.

This approach is similar to labor variance as it uses hours to determine how efficiency affects variable indirect costs. Efficiency variance can help organizations determine productivity, where underperformance occurred or if a process was more efficient than anticipated.

This variance approach, also known as the fixed overhead spending variance, is the difference between the budgeted amount for fixed indirect costs and the actual fixed costs incurred.

The purpose is to determine whether a company spent more or less than forecast. Separately, a fixed indirect cost volume variance can be performed to measure utilization of capacity and the actual level of activity.

A variance analysis requires multiple steps of planning and implementation. Choosing the right variance analysis and plugging in the formulas are just the beginning.

Organizations that want to see real results from conducting variance analysis must analyze the results and take the next step.

Compile all the necessary data. To begin, the analysis requires two sets of data—budgeted and actual—for the variable being focused on.

Finance teams can change out different actual financial data depending on what is being analyzed.

Decide which variance analyses will be most beneficial to the organization. Generally speaking, it involves subtracting the actual figure from the forecasted figure. These formulas will reveal any gaps between expectations and reality.

Here are some examples of variance analysis formulas:

• Cost variance = actual costs - budgeted costs
• Efficiency variance = (actual input - budgeted input) x standard rate
• Volume variance = (actual sales volume - budgeted sales volume) x budgeted selling price
• Budget variance = Actual revenue - budgeted revenue

Determine whether the variance is positive or negative. The analysis has just two results: either favorable or unfavorable.

For example, if the variance analysis is looking at cost variance, a favorable cost would be if the industry-standard costs were lower than the actual costs. If the actual costs were higher, the results would be considered unfavorable.

Figure out why the variance occurred. This step is where AI in financial reporting and predictive analytics can help streamline the analysis process.

AI-driven tools can analyze results and figures in real time by using real-time data to provide in-depth insights. Understanding the root cause of the variance will be key for teams across departments and stakeholders outside of the organization.

Compile the results from the variance analyses and create a report that includes all the details.

It is a key step because the more data available, the more helpful the analysis can be for short-term and long-term projects. Having a detailed account of all the variances can help build a strong base and a use case for this approach to variance analysis.

Conducting a variance analysis can be beneficial to an organization by improving risk management, driving data-driven decisions and eliminating guesswork when identifying performance gaps:

• Enables tighter operations: A variance analysis is a quick, reliable way to identify overspending and underperformance. It gives management the data-driven information needed to act before issues grow and to streamline energy to areas with significant deviations.

• Identifies discrepancies: Variance analysis helps teams get to the crux of the problem quickly and detect anomalies in financial data. Regular analysis of deviations can pinpoint errors in accounting, omissions or potential fraud before they escalate in urgency.

• Improves business strategy: A systematic approach to understanding why actual results deviate from forecast figures drives smarter decision-making. Variance analysis results provide actionable insights that drive decisions on which services to offer, which vendors to use and which customers to target.

• Increases accountability: A variance analysis supports accountability across an organization. Variance analysis alerts team leaders to issues and provides a clear path to identify who or what is responsible for the financial results. In addition, this approach aligns KPIs and drives operational efficiency.

Conducting a variance analysis can benefit an organization by improving risk management, driving data-driven decisions and eliminating guesswork when identifying performance gaps:

• Communicate needs: A variance analysis requires cross-team collaboration and buy-in around variance figures. All team members must know how to report their findings, no matter the results. Even when there are no issues identified in the analysis, it’s important to consistently submit results and communicate the findings.

• Enable AI-driven software: There are various AI-driven software and finance automation tools to assist with the variance analysis process, making it quicker and more efficient. Well-known options include enterprise resource planning (ERP) for integrated data and AI-powered predictive analytics software. AI-powered tools are helping finance teams be more strategic and savvier with the resources they are given.

• Establish clear performance metrics: The variance analysis chosen by the team is only impactful if the results can be compared against an established figure. That’s why it’s important to set clear objectives and KPIs relevant to the selected variance early on. Variance analysis results can then be compared against something immediately and for longer-term projects.

• Set clear policies: Sometimes, the biggest challenge for organizations when implementing a variance analysis is the lack of a policy for when to perform them. Without clear guidance or a schedule in place, an organization might skimp on them and forget them altogether. The analysis should be a routine step in the monthly closing process and establish materiality levels that flag important changes.

• Monitor continuously: Aside from consistently deploying variance analysis, teams will also need to monitor the results regularly. The greatest benefit of a variance analysis is reached when analyses are consistent and reporting is done on a schedule. By doing so, teams can spot patterns and discrepancies and act without hesitation.