How to Calculate and Analyse Variance Analysis
In every business, comparing actual performance with expected results is crucial for financial control. Variance analysis is the process that helps achieve this comparison. It highlights where performance deviates from the budget or standards, enabling timely action to improve profitability and efficiency.
Let’s explore the meaning, types, calculation methods, and practical applications of variance analysis with examples.
What is Variance Analysis?
Variance analysis is the evaluation of differences between actual and planned (budgeted) figures in a company’s financial statements. It identifies whether the variance is favourable (better than expected) or unfavourable (worse than expected).
In simple terms, it answers one question: Why are our actual results different from what we expected?
Importance of Variance Analysis in Business and Finance
Variance analysis plays a vital role in cost control and financial planning. By identifying discrepancies, managers can determine which areas of the business are underperforming and take corrective action quickly.
It also helps in:
- Evaluating performance of departments or cost centres.
- Improving future budgeting accuracy .
- Strengthening accountability through data-backed insights.
Common Terms: Actual vs Budgeted, Favourable vs Unfavourable Variance
- Actual vs Budgeted: The basis of analysis; actual figures come from performance reports, while budgeted ones are planned targets.
- Favourable Variance: When revenue is higher or costs are lower than budgeted.
Unfavourable Variance: When revenue is lower or costs are higher than expected.
Types of Variances Explained
Variance analysis covers several cost and revenue components. Each type reveals different aspects of operational efficiency.
Material Variance
This measures the difference between the standard cost of materials and the actual cost incurred. It includes:
- Material Price Variance (difference in price paid vs standard price).
- Material Usage Variance (difference in quantity used vs expected).
Labour Variance
It tracks the gap between standard labour cost and actual labour cost, helping evaluate workforce efficiency.
Subcategories include:
- Labour Rate Variance – deviation in wage rates.
- Labour Efficiency Variance – difference in time taken vs standard hours.
Overhead Variance
This measures differences in fixed or variable overhead costs compared to standards. Overhead variance analysis ensures cost allocation accuracy across departments.
Sales Variance
Sales variance captures how sales revenue differs from the budgeted target. It can arise due to changes in price, quantity, or product mix.
| Type of Variance | Basis of Comparison | Purpose |
|---|---|---|
| Material Variance | Standard vs Actual Material Cost | Control material purchase and usage |
| Labour Variance | Standard vs Actual Labour Cost | Measure workforce productivity |
| Overhead Variance | Standard vs Actual Overhead | Evaluate cost centre efficiency |
| Sales Variance | Standard vs Actual Sales Revenue | Track sales and pricing performance |
Step-by-Step Guide to Calculating Variances
Accurate variance analysis requires systematic data collection and clear formulas.
Collecting and Comparing Financial Data
Start by gathering actual data from ledgers or reports and compare them with budgeted standards. The more accurate your standard costs, the more reliable the variance results will be.
Standard Formulas for Variance Calculation
| Variance Type | Formula |
|---|---|
| Material Cost Variance | (Standard Cost – Actual Cost) |
| Labour Cost Variance | (Standard Hours × Standard Rate) – (Actual Hours × Actual Rate) |
| Overhead Variance | Standard Overhead – Actual Overhead |
| Sales Variance | (Actual Sales – Budgeted Sales) × Standard Price |
Worked Example: Material, Labour, and Overhead Variances
Example:
A manufacturing firm sets a standard cost of ₹50 per unit of material and expects to use 100 units (₹5,000 total). In reality, it uses 110 units at ₹55 each (₹6,050 total).
Material Cost Variance:
= (Standard Cost – Actual Cost)
= ₹5,000 – ₹6,050 = ₹1,050 (Unfavourable)
Material Usage Variance:
= (Standard Quantity – Actual Quantity) × Standard Price
= (100 – 110) × ₹50 = ₹500 (Unfavourable)
Labour Cost Variance Example:
If standard labour is ₹200 per hour for 10 hours, but actual is ₹220 per hour for 12 hours,
= (10 × 200) – (12 × 220) = 2,000 – 2,640 = ₹640 (Unfavourable).
These examples show how variance highlights inefficiencies in both material usage and labour cost.
Analysing and Interpreting Variance Results
Once calculated, variances must be analysed to understand their root causes and implications.
Identifying Root Causes of Significant Variances
Some common causes include inaccurate standards, poor planning, waste, delays, or unexpected market fluctuations. Distinguishing controllable and uncontrollable factors helps prioritize corrective actions.
Using the Column Method for Analysis
This method lists actual and standard figures side by side for each cost component, along with variances and remarks. It helps management visually assess where deviations occur.
How to Communicate Variance Analysis Findings
Summarize findings clearly for management review. Highlight key variances, their reasons, and recommended corrective measures in concise dashboards or monthly reports.
Applying Variance Analysis in Budgeting and Cost Control
Variance analysis plays a pivotal role in financial management by linking budgets with performance outcomes.
Practical Uses in Business Decision-Making
- Detects operational inefficiencies early.
- Guides management to revise budgets based on performance trends.
- Aids in pricing and cost optimization decisions.
Improving Future Forecasts with Variance Insights
Consistent variance tracking refines assumptions for future budgets. For example, if material price variances occur repeatedly, procurement teams can negotiate better contracts or switch suppliers.
Challenges and Best Practices in Variance Analysis
Like any analytical process, variance analysis has its challenges.
Common Pitfalls and How to Avoid Them
- Unrealistic Standards: Set achievable benchmarks to avoid misleading variances.
- Ignoring Small Variances: Even minor consistent variances may indicate inefficiencies.
- Poor Data Accuracy: Ensure timely and correct data recording for reliable outcomes.
Recommended Tools/Software for Variance Analysis
Modern accounting and ERP software can automate variance calculations, generate detailed reports, and visualise trends. Tools like BUSY simplify variance tracking across departments.
Conclusion
To conclude, variance analysis is not just about calculations; it’s about understanding why differences occur and how to improve future performance. By applying structured methods, reliable data, and digital tools, businesses can turn variance insights into actionable financial improvements.
As a Chartered Accountant with over 18 years of experience, I have honed my skills in the field and developed a genuine passion for writing. I specialize in crafting insightful content on topics such as GST, income tax, audits, and accounts payable. By focusing on delivering information that is both engaging and informative, my aim is to share valuable insights that resonate with readers.
Frequently Asked Questions
-
What is a variance in budgeting and accounting?
A variance is the difference between actual results and budgeted expectations in financial performance.
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How do you calculate material, labour, and overhead variance?
By comparing standard cost or time against actual figures using variance formulas (e.g., Material Cost Variance = Standard – Actual).
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What does a favourable or unfavourable variance mean?
A favourable variance means performance exceeded expectations, while an unfavourable variance indicates higher costs or lower revenues.
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Why is variance analysis important for manufacturing companies?
It helps manufacturers control production costs, improve efficiency, and enhance profitability by tracking deviations in materials, labour, and overheads.
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What are common challenges in variance analysis?
Setting unrealistic standards, inaccurate data, and ignoring recurring variances are common issues.