4.0. Application of data analytics in specialised areas

4.1. Management accounting

Budget preparation and analysis (including variances)

What is Budget preparation

Budget preparation refers to the process of creating a comprehensive financial plan for an organization, government agency, or individual for a specific period, typically a fiscal year. It involves estimating and allocating financial resources to various activities, projects, and departments to achieve specific goals and objectives. The budget preparation process includes forecasting revenue, estimating expenses, setting financial targets, and establishing spending priorities. Budgets serve as a roadmap for financial management, helping organizations make informed decisions, allocate resources efficiently, and track their financial performance against planned objectives.

Application of Data Analytics in Budget Preparation, Analysis, and Variances in Management Accounting:

Data analytics is a valuable tool in management accounting, specifically in budgeting processes. It enables organizations to streamline budget preparation, conduct thorough analysis, and effectively manage variances. By leveraging historical financial data and predictive modeling, data analytics supports the creation of accurate budgets aligned with organizational objectives. Real-time monitoring and variance analysis are made more efficient through data-driven insights. This approach enhances management accounting practices by providing timely adjustments and optimizing resource allocation, ultimately leading to more effective budgeting processes within the organization.

1. Budget Preparation:

Budget preparation involves the process of creating a financial plan for a specific period, typically a fiscal year, to guide an organization's financial activities. Data analytics plays a crucial role in this phase by providing valuable insights and tools for more accurate and efficient budgeting:

• Historical Data Analysis: Data analytics allows organizations to examine past financial data and performance, helping identify trends, patterns, and areas where cost savings or revenue growth can be achieved.
• Predictive Modeling: Advanced analytics and machine learning can assist in forecasting future revenue and expenses, making it easier to develop realistic budgets.
• Resource Allocation: Data analytics aids in optimizing resource allocation by analyzing historical spending patterns and identifying areas where funds can be allocated more effectively.
• Scenario Analysis: Data-driven simulations enable organizations to assess various scenarios and their financial implications, helping in risk management and decision-making during the budgeting process.

Example

SmartMart, a retail chain, is preparing its annual budget for the next fiscal year. Last year's total revenue was $10 million, and the company aims for a 10% increase this year. SmartMart also plans to invest 20% of the revenue in marketing and wants to optimize staffing costs based on customer foot traffic data.

Budget Preparation Using Data Analytics:

(a). Predictive Sales Calculation:

Last year's revenue: $10,000,000
Target increase: 10%
Predicted revenue = $10,000,000 + ($10,000,000 * 10%) = $11,000,000

(b). Marketing Budget Allocation:

20% of predicted revenue allocated for marketing: $11,000,000 * 20% = $2,200,000

(c). Optimizing Staffing Costs:

Analyzing customer foot traffic data suggests peak hours require 10 staff members per store, while off-peak hours need 5 staff members.
Average hourly wage per staff: $15
Daily staffing cost during peak hours = 10 staff * $15/hour * 8 hours = $1,200
Daily staffing cost during off-peak hours = 5 staff * $15/hour * 8 hours = $600

(d). Total Staffing Costs for the Year:

Peak hours: $1,200/day * 365 days = $438,000
Off-peak hours: $600/day * 365 days = $219,000
Total staffing costs = $438,000 (peak) + $219,000 (off-peak) = $657,000

(e). Remaining Budget (After Marketing and Staffing Costs):

Predicted revenue: $11,000,000

Marketing budget: $2,200,000

Staffing costs: $657,000

Remaining budget = $11,000,000 - $2,200,000 - $657,000 = $8,143,000

In this simplified example, data analytics helps SmartMart calculate predicted sales, allocate budgets for marketing and staffing, and optimize costs. By integrating data-driven calculations, SmartMart ensures a more accurate and efficient budgeting process, allowing them to make informed financial decisions for the upcoming fiscal year.

2. Budget Analysis:

Budget analysis is the evaluation of an organization's financial performance against the budgeted figures. Data analytics enhances this process by providing real-time insights and facilitating a more comprehensive understanding of financial outcomes:

• Real-Time Monitoring: Data analytics tools enable continuous monitoring of financial data, allowing organizations to track budget performance in real time and make timely adjustments when necessary.
• Variance Analysis: Analytics helps identify discrepancies between budgeted and actual figures, enabling organizations to investigate the causes of variances and take corrective actions.
• Data Visualization: Visualization tools create interactive dashboards and reports, making it easier to communicate budget analysis findings to stakeholders and promote transparency.
• Automated Reporting: Automated reporting reduces the manual effort required for budget analysis, freeing up time for in-depth analysis and strategic decision-making.

3. Variances:

Variances represent the differences between budgeted and actual financial figures. Managing variances is critical for effective financial control and decision-making, and data analytics plays a vital role in this aspect:

• Identifying Root Causes: Data analytics helps pinpoint the underlying reasons for budget variances, whether they are due to changes in market conditions, internal factors, or unexpected events.
• Trend Analysis: Analytics tools enable organizations to analyze historical variances and identify recurring patterns, which can inform future budget adjustments.
• Risk Management: By using data analytics to assess variances, organizations can proactively manage risks and develop strategies to mitigate the impact of unexpected financial deviations.
• Continuous Improvement: Data analytics fosters a culture of continuous improvement by providing actionable insights into how budgeting processes can be refined and made more accurate.

Types of variances

1. Sales Variance = (Actual Sales − Budgeted Sales) × Selling Price
   Sales variance measures the difference between actual sales revenue and the budgeted sales revenue. It helps management understand the impact of sales performance on the overall financial picture.
2. Cost Variance = Actual Cost − Budgeted Cost
   Cost variance calculates the difference between actual costs incurred and budgeted costs. Positive values indicate cost overruns, while negative values suggest cost savings.
3. Labor Variance = (Actual Hours × Actual Rate) − (Budgeted Hours × Standard Rate)
   Labor variance measures the difference between the actual labor cost and the budgeted labor cost. It takes into account both the actual hours worked and the actual hourly rate.
4. Material Price Variance = (Actual Price per Unit − Standard Price per Unit) × Actual Quantity Used
   Material price variance assesses the difference between the actual price paid for materials and the standard price. It accounts for changes in the cost of raw materials.
5. Material Usage Variance = (Actual Quantity Used − Standard Quantity Allowed) × Standard Price per Unit
   Material usage variance evaluates the variance arising from using a different quantity of materials than what was budgeted. It considers the standard quantity and standard price per unit.
6. Variable Overhead Variance = (Actual Hours × Actual Variable Overhead Rate) − (Budgeted Hours × Standard Variable Overhead Rate)
   Variable overhead variance measures the difference between the actual variable overhead costs incurred and the budgeted variable overhead costs. It considers both actual hours worked and the variable overhead rate.
7. Fixed Overhead Variance = Actual Fixed Overhead − Budgeted Fixed Overhead
   Fixed overhead variance calculates the difference between actual fixed overhead costs and the budgeted fixed overhead costs. It helps analyze the impact of fixed costs on the budget.
8. Sales Volume Variance = (Actual Sales Volume − Budgeted Sales Volume) × Standard Selling Price
   Sales volume variance isolates the impact of changes in sales volume (quantity) on revenue. It helps assess whether the variance from the budget is due to selling more or fewer units.
9. Mix Variance = (Actual Mix − Budgeted Mix) × Standard Contribution Margin per Unit
   Mix variance evaluates the effects of variations in the product or service mix sold on profitability. It measures the impact of selling different proportions of high and low-margin products.
10. Yield Variance = (Actual Output − Budgeted Output) × Standard Cost per Unit
    Yield variance focuses on the quantity of output produced and how it deviates from the budgeted level. It is commonly used in manufacturing and production environments.
11. Production Efficiency Variance = (Actual Hours − Standard Hours) × Standard Wage Rate
    Production efficiency variance assesses the impact of differences in the time required to produce a certain quantity of units compared to the standard time.
12. Sales Price Variance = (Actual Selling Price − Budgeted Selling Price) × Actual Sales Volume
    Sales price variance evaluates the impact of changes in the selling price per unit on revenue compared to the budgeted selling price.