business case study variance analysis

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The Importance of Variance Analysis

This critical topic is too often taught to only a handful of students—or neglected in the b-school curriculum altogether.

Variance analysis is an essential tool for business graduates to have in their toolkits as they enter the workforce. Over our decades of experience in executive education, we’ve observed that managers across all industries and functions use variance analysis to measure the ability of their organizations to meet their commitments.

Because variance analysis is such a powerful risk management tool, there is a strong case for including it in the finance portion of any MBA curriculum. Yet fewer than half of finance professors believe they should be teaching this subject; they view it as a topic more typically taught in accounting classes. At the same time, in practice, variance analysis is such a cross-functional tool that it could be taught throughout the business school curriculum—but it’s not. We perceive a worrisome disconnect between the way variance analysis is taught and the way it is used in real life.

Variance Analysis and Its Applications

There are three periods in the life of a business plan: prior period to plan, plan to actual, and prior period to actual. For instance, if a business plan is being formulated for 2019, the “prior period” would be 2018, the “plan to actual” would be the budget for 2019, and the “prior period to actual” would be what really happens in 2019. These three stages are also referred to as planning, meeting commitments, and growth.

For each of these periods, variance analysis looks at the deviations between the targeted objective and the actual outcome. The most common variances are found in price, volume, cost, and productivity. When executives conduct an operational review, they will need to explain why there were positive or negative variances in any of these areas. For instance, did the company miss a target because it lost an anticipated national account or failed to lock in a price contract due to competitive pressure?

Executives who understand variances will improve their risk management, make better decisions, and be more likely to meet commitments. In the process, they’ll produce outcomes that can give an organization a real competitive advantage and, ultimately, create shareholder value.

Most businesses apply variance analysis at the operating income level to determine what they projected and what they achieved. The variances usually are displayed in the form of floating bar charts—also known as walk, bridge, or waterfall charts. These graphics are often used in internal corporate documents as well as in investor-facing documents such as quarterly earnings presentations.

While variance analysis can be applied in many functional areas, it is used most often in finance-related fields. Yet, the majority of finance programs at both the graduate and undergraduate levels don’t cover it at all. We surveyed finance faculty in 2013 and accounting faculty in 2017 to determine how they teach and use variance analysis. Among other things, we learned that:

• More than 80 percent of accounting faculty believe that variance analysis is important to a finance career, and they are far more likely to teach it than their finance faculty colleagues.
• Only 59 percent of finance faculty and 48 percent of accounting faculty are familiar with examples of walk charts from real-world companies. Yet these visual portrayals of operating margin variances are commonplace in quarterly earnings presentations and readily found on investor relations websites.

Because universities mostly fail to teach this important topic, corporate educators have been left to fill the learning gap. Many global organizations, in fact, make variance analysis a key subject in their development programs for entry-level financial professionals.

The University Response

We believe it’s critical for universities to better align their curricula with the skills that today’s employers seek in the graduates they hire. Not only do we think variance analysis should be included in the business curriculum, but we could even make an argument for running it as a capstone business course. We offer these suggestions for ways that faculty could integrate this powerful tool across the business school program:

• Both accounting and finance faculty should, as much as is practical, incorporate variance analysis into their classes, particularly focusing on financial planning and analysis. We acknowledge that a dearth of corporate finance texts on the topic will make this a challenge for finance professors. The two of us employ teaching materials in our graduate business and undergraduate finance classes based on experience in the corporate world, and we would be glad to share them with others.
• Faculty who use case studies should always include a case specific to variance analysis tools. Students who pursue careers in corporate finance will almost certainly be required to use such tools, particularly as data and predictive analytics applications are enhanced to improve forecasting accuracy. Two sources of such case studies are TRI Corporation and Harvard Business Publishing.
• Professors can introduce students to real-world applications of variance analysis by showing how it is used in investor relations (IR) pitches. As instructors, the two of us routinely search IR sites for applications of variance analysis. We specifically look for operating margin variance walks (floating bars, brick charts) for visual applications that can make the topic come to life for students. Here’s an example from Ingersoll Rand:

• Faculty from accounting and finance programs should collaborate on when, where, and how to teach variance analysis. At the very least, this will ensure that students gain an understanding of the topic from either a finance or an accounting perspective, but the ideal would be for them to benefit from both perspectives for a holistic understanding. At Fairfield University, accounting programs introduce students to the theory of variance analysis. Then finance programs take an operational and cross-functional approach that addresses planning, meeting commitments, and growth.
• Both accounting and finance faculty should help finance majors understand variance analysis from a practitioner’s standpoint. Discussions about pricing, supply chain, manufacturing costs, risk management, and inflation and deflation around cost inputs can help students grasp the necessity of making trade-offs and balancing short-term and long-term business goals. To make sure students understand the practitioner’s viewpoint, we use corporate business simulations that are more operationally focused, as opposed to being academic in tone.
• To extend the topic to all majors, not just finance and accounting students, faculty from disciplines such as strategy and operations could also incorporate variance analysis into their classes. For instance, if they use business simulations for their capstone courses, they could add a component that covers variance analysis. At Fairfield, we use a variety of competitive business simulations from the corporate world.
• Finally, professors can bring in guest speakers from almost any business functional area and ask them to explain, as part of their presentations, how variance analysis is relevant in their fields. As an example, we often have senior finance executives from Stanley Black & Decker—a company known well-known for its ability to grow and meet its commitments via variance analysis—present to our graduate program. We tap other companies from Fairfield County as well.

In the graduate classes we teach at Fairfield University, we have always tried to connect theory with practice. And we’ve long believed that creating a culture of meeting and exceeding commitments requires aligning interaction across functions in the workplace. With this article, we hope that, at the very least, we can start a larger discussion about the need for cross-disciplinary teaching of variance analysis.

  For more about variance analysis materials, contact us at  [email protected]  or  [email protected] .

• Variance Analysis: Your Secret Weap...

Variance Analysis: Your Secret Weapon for Better Financial Planning in a Volatile Market

Listen to the blog:

Table of content, key takeaways.

• In-depth understanding of variance in enterprises’ cash forecasting and its significance amid market uncertainties
• How to can calculate variance in your enterprise’s cash forecasts and the key benefits of conducting variance analysis in cash flow forecasting 
• How to reduce variances in cash forecasting with AI in your treasury processes

Deep dive: What is variance?

For enterprises, variance is the difference between the expected cash inflows or outflows and the actual cash inflows or outflows. In other words, it is the difference between the forecasted cash position and the actual cash position of an enterprise at a given time. This variance can be expressed as a positive or negative number, indicating whether the actual cash inflows or outflows were higher or lower than expected based on the forecast.

Variance is an important metric for enterprises to track as it helps understand how accurate your cash forecasts are and whether you need to adjust your financial plans or take corrective actions. By analyzing variances in cash forecasting, enterprises can identify areas where you are overspending or underperforming and take necessary steps to improve your cash flow and financial performance.

Don’t Let Market Uncertainty Derail Your Financial Performance. Analyze Your Variance Now – Download Variance Analysis Template

Types of variances

Different types of variances can occur in cash forecasting due to various reasons such as changes in market conditions, customer behavior, operational issues, and timing issues, among other factors. These variances can impact both sales revenue and expenses. By understanding the underlying causes of these variances, companies can make necessary adjustments to their forecasting models, mitigate risks, and improve their overall financial performance.

Broadly, variances can be classified into two major categories:

• Materials, Labor, and Variable Overhead Variances 
• Fixed Overhead Variances

Materials, labor, and variable overhead variances include price/rate variances and efficiency and quantity variances. Price/rate variances show the differences between industry standard costs and actual pricing for materials while efficiency variances and quantity variances refer to the differences between actual input values and the input amounts specified.

Overhead variances include volume variances and budget variances. Volume variances are differences between actual fixed overhead costs applied and budget fixed overhead costs. Meanwhile, budget variances indicate the differences between actual and budgeted amounts.

Budget variances can be divided into two subgroups: expense variances and revenue variances. Expense variances are all about costs and are easier to control and streamline. That’s why we tend to focus on them more during variance analysis. Revenue variances, on the other hand, are more unpredictable and harder to measure. But they’re also really important because they can help you find ways to improve business processes, increase revenue, and reduce costs. By analyzing revenue variances, you can find opportunities to make your business more efficient and valuable in the long run.

Let’s take a look at an example for variance in budgeting 

Let’s say that your enterprise sells widgets, and you’ve projected that you’ll sell $1 million worth of widgets in the next quarter. However, at the end of the quarter, you find that you’ve only sold $800,000 worth of widgets. That’s a variance of $200,000, or 20% of your original plan. By analyzing this variance, you can figure out what went wrong and take steps to improve your sales performance in the next quarter. Here, variance analysis becomes the vital tool that enables you to quickly identify such changes and adjust your strategies accordingly to manage your financial performance and optimize cash forecasting .

Simplify & Streamline Your Cash Forecasting Across Timelines- 

Download Cash Forecasting Template

Understanding variance analysis

Variance analysis helps you identify the difference between your planned or expected financial outcomes and what actually happened. It’s like checking if you spent more or less than what you planned to spend. This helps you understand where you went over or under budget and why. It’s important because it allows you to monitor your financial performance, identify trends, and make informed decisions about future planning. By using variance analysis, you can stay on track with your financial goals and improve your bottom line over time.

Role of variance analysis

During times of market uncertainty, your enterprise experiences unexpected changes in revenue, expenses, or other financial metrics. The tightening financial conditions are expected to cause the global economy to grow sluggishly, at a rate of 1.6% in 2023, as per JP Morgan’s 2023 Market Outlook . In such cases, one of the most important tools in your financial management toolkit is variance analysis.

Variance analysis basically helps you see how your organization is doing financially and take proactive actions to reduce risks and improve results . It allows enterprises to compare their forecasted cash flow with their actual cash flow, and to determine the underlying causes of variances, such as changes in market conditions, customer behavior, or operational issues. By performing variance analysis, enterprises can gain valuable insights into their cash flow performance and make informed decisions about how to respond to unexpected changes in the market. 

For example, if a company identifies that its actual cash inflows are lower than expected due to a decrease in sales revenue, it can take steps to reduce costs or adjust its pricing strategy to maintain profitability. Similarly, if a company identifies that its actual cash outflows are higher than expected due to unexpected expenses, it can adjust its budget or seek alternative financing options.

How to conduct variance analysis (formula & example)? 

The fundamental formulae for collecting the data required for variance analysis are relatively straightforward. There are two basic formulae for calculating variance: positive convention and negative convention. 

Positive convention measures variance as a positive value, indicating a negative variance. In positive convention, negative figures show that actual figures are under budget, indicating a positive variance. Let’s take a look at the formula for positive convention:

Actual Spending – Budgeted Spending = Variance

Negative convention calculates negative variances as negative values and positive variances as positive values. Here’s the formula for negative convention in variance analysis:

Budgeted Spending – Actual Spending = Variance

Both conventions are acceptable, as long as they are applied consistently. However, obtaining actual figures for a variance is only the first step – contextual analysis is crucial for gaining actionable insights.

For example, if a CPG company has a budgeted purchase order of $10,000$10,0000 for packaging materials from a supplier but ends up spending $12,0000 in a quarter, they could be concerned since they are over budget by 20%.

However, the overage is only $2,0000 in a budget that might measure in the millions. This presents an opportunity for investigation (are there any inefficiencies in the packaging process? Can the enterprise negotiate a better price with the supplier, or consider alternative packaging materials?). This also poses an opportunity to adjust future budgets to optimize the enterprise’s financial performance. While it may not be a major financial emergency, it still warrants attention to ensure optimal cash flow management .

Benefits of conducting variance analysis

Let’s take a look at the top 4 benefits enterprises can reap by conducting variance analysis for cash forecasting: 

Identify discrepancies

Variance analysis helps identify discrepancies between actual cash inflows and outflows and the forecasted amounts. By comparing the forecasted cash flow with actual cash flow, it is easier to identify any discrepancies and take corrective measures.

Refine cash forecasting techniques

Conducting variance analysis allows for a review of past forecasts to identify any errors or biases that may have impacted accuracy. This information can be used to refine forecasting techniques and improve future forecasts.

Improve financial decision-making

Understanding the reasons for variances can provide valuable insights that can help improve financial decision-making, which is critical in a volatile market. For example, if a variance is caused by unexpected expenses, management may decide to reduce expenses or explore cost-saving measures.

Better cash management

By analyzing variances, companies can identify areas where cash management can be improved. This can include better management of accounts receivable or accounts payable, more effective inventory management, or renegotiating payment terms with suppliers.

It can be difficult for treasurers to create low-variance cash flow forecasts for enterprises, especially with traditional variance analysis methods that typically involve spreadsheets, due to the large amount of data involved. Moreover, relying on manual variance reduction approaches can lead to a high variance and can be time-consuming, labor-intensive, and expensive, thus, delaying the much-essential decision making.

Role of AI in variance analysis for cash forecasting

Amid turbulent market conditions, as companies prepare for the latter half of 2023 and 2024, enterprises’ finance chiefs are recommending various enhancements to improve decision-making. The most commonly mentioned improvements are the adoption of digital technologies, AI, and automation, and the enhancement of forecasting, scenario planning, and consistency in measuring key performance indicators, as per Deloitte CFO Signals Survey .

This goes to show the significance of adoption of advanced technologies, such as AI, for companies preparing for uncertain markets. Here’s how AI takes variance analysis to the next level – AI-based cash forecasting software helps in variance analysis by taking additional steps to improve the accuracy of the cash forecast by 90-95% . It compares cash forecasts to actual results to check for variances, aligning the forecast with other horizons such as monthly, quarterly, and yearly forecasts, thus, ensuring that the forecast is accurate across various scenarios. AI also analyzes the accuracy of cash forecasts through a line item analysis across multiple horizons, and makes tweaks to the algorithm through an AI-assisted review process. Finally, AI fine-tunes the forecast model and enhances the data as needed to achieve the desired level of forecast accuracy.

Artificial intelligence (AI) can help you generate low-variance cash flow forecasts. It automates data collection from past cash flows, including bank statements, accounts receivable, accounts payable, and other financial transactions and integrates with most financial systems. This data is evaluated to detect patterns and trends that can be utilized to anticipate future cash flows. Based on this historical analysis and regression analysis of complex cash flow categories such as A/R and A/P, AI selects an algorithm that can provide an accurate cash forecast.

Several leading enterprises turn to AI for variance analysis and thereby ensure accurate cash forecasting. It compares forecast, budget, and actual data to improve accuracy by identifying the key drivers. 

Benefits of leveraging AI in variance analysis

Here are some of the key benefits you can achieve by conducting variance analysis with AI in cash forecasting:

Improved cash forecasting accuracy with real-time cash analysis

AI can deal with large volumes of data quickly, accurately, and consistently. This can improve the accuracy of your cash forecasts and reduce the risk of errors that may occur from manual data entry or human bias. AI simplifies your cash flow analysis by deep diving into and analyzing a vast amount of data coming in from multiple sources, including historical cash flow data, market trends, and economic indicators, in real time. Thus, it provides instant insights into cash flow variances. This can provide a more comprehensive analysis of cash flow variances, allowing you to identify trends and patterns that may not be apparent through manual analysis.

Real-time cash analysis & Better liquidity management 

With AI at its core, cash flow forecasting software can learn from industry-wide seasonal fluctuations to improve forecasting accuracy. AI-powered cash forecasting software that enables variance analysis can also create snapshots of different forecasts and variances to compare them for detailed, category-level analysis. By offering such comprehensive visibility, it helps you respond quickly to changes in cash flow, take corrective action as needed, and manage your enterprise’s liquidity better. 

Faster, data-driven decision-making

With real-time analysis and more comprehensive insights on cash forecasts, AI can help you make faster, more informed decisions about cash flow management. This can help organizations respond quickly to changes in cash flow and take corrective action as needed. This is particularly important during times of market uncertainty when cash flow patterns can change rapidly and unexpected events can occur.

Automated reporting

AI can automate the reporting of cash flow variances, providing regular reports that highlight trends and patterns. By automating the reporting process, organizations can save time and resources that would otherwise be spent on manual reporting. This also ensures that reporting is consistent and accurate, eliminating the potential for human error. With regular, real-time reports on cash flow variances, you can stay on top of your enterprise’s cash flow management and identify areas for improvement to maximize your financial performance.

What Will Your ROI Be from AI-Enabled Cash Forecasting?

Analyze Now

HighRadius’ AI-based Cash Forecasting Software is the perfect solution for enterprises looking to improve their cash forecasting process and achieve the above-mentioned benefits. Here are the cash forecasting solution’s key features that can help you reduce variances in cash forecasting and achieve accurate cash forecasts:

• Enhance visibility at a granular level, providing improved reporting to CFOs, through automated data gathering and consolidation of financial data. 
• Ensure seamless and continuous data access by easily integrating with ERPs, bank portals, FP&A systems, and legacy systems through APIs, sFTP, and dashboards. 
• Improve cash forecasting accuracy with automatic roll-up of forecasts from local to global levels across various cash flow categories, regions, currencies, entities, and horizons. 
• Manage exceptional scenarios with spreadsheet functionality in the solution that allows manual adjustments to the forecast.
• Take course correction measures by performing variance analysis over multiple cash flow categories, regions, and durations, as well as the drill-down capability to find the root causes of variances.
• Reduce variance by using bank statement data and fine-tuning forecasts to boost forecast accuracy with a closed-loop feedback model.
• Customize cash forecasting cadence to ensure that forecasts meet the company’s requirements and goals. For instance, For example, companies facing cash deficits can forecast on a daily or weekly basis to avoid debts, while companies with excess cash can forecast quarterly to invest in business growth.
• Maximize profits during peak seasons and reserve cash for off-seasons with the time series algorithm that helps capture seasonal trends. 
• Track complex cash flow categories like A/R and A/P by capturing customer-specific variables and identifying patterns in customers’ payment behavior.

Conclusion Closing Thoughts for Treasury Leaders

In a rapidly evolving business landscape, market uncertainties and disruptions can have a significant impact on an enterprise’s financial stability. That’s why having a robust cash flow forecasting system with AI at its core to conduct variance analysis is crucial. 

By analyzing large volumes of historical and real-time data, AI-powered cash forecasting software can provide accurate insights into cash flow variances, enabling enterprises to respond quickly to any changes in the market and empowering finance leaders to make informed decisions. It can also learn from industry-wide seasonal fluctuations to provide more accurate cash flow forecasts.

With AI in the cash forecasting process, including variance analysis and scenario modeling, enterprises can better manage their liquidity during times of market uncertainties and prepare themselves for the future, where accurate cash flow forecasting will be critical to their success. It can be a game-changing factor to give you a competitive advantage, improve your enterprise’s financial stability, and help you make data-driven decisions that can help them thrive in the long run.

What is the difference between standard costing and variance analysis?

Standard costing refers to the process of establishing estimated (standard) costs for products or services based on expected levels of input costs, labor costs, overhead costs, and other factors. Variance analysis, on the other hand, is the process of comparing actual costs to standard costs to identify variances, or differences between the expected and actual costs. 

What are the three main sources of variance in an analysis?

The three main sources of variance in an analysis for cash forecasting are timing variance, amount variance, and category variance. Timing variance refers to differences in cash flow timing compared to the expected timing, while amount variance is the difference in the actual cash flow amount compared to the expected amount. Category variance refers to differences in the actual cash flow for a specific category compared to the expected cash flow.

What is P&L variance analysis?

P&L (profit & loss) variance analysis is the process of comparing actual financial results to expected results in order to identify differences or variances. This type of variance analysis is typically performed on a company’s income statement, which shows its revenues, expenses, and net profit or loss over a specific period of time. 

Related Resources

4 Cash Flow Forecasting Best Practices to Navigate Economic Uncertainties

How to Improve the Accuracy of Accounts Payable Forecasting

7 Practical Tips to Improve Your Cash Flow potential

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Variance Analysis: Understanding its Importance in Financial Management

✅ All InspiredEconomist articles and guides have been fact-checked and reviewed for accuracy. Please refer to our editorial policy for additional information.

Variance Analysis Definition

Variance analysis is a financial and quantitative method used to identify and understand the degree of difference between actual and planned behavior in budgeting or financial accounting. It aids in determining the causes and degrees of variances, aiding organizations in decision making and performance improvements.

Purpose of Variance Analysis

Refining budgets.

Variance analysis plays a crucial role in refining business budgets. By comparing the budgeted figures to actual results, it aids managers in identifying where they overspent or underspent. By pinpointing the areas of overspending, businesses can then implement strategies to curtail costs and ensure they remain on-budget in future periods. Similarly, underspending might signal missed opportunities or inefficiently allocated resources, prompting a reevaluation of spending priorities.

Forecasting Future Financial Results

Variance analysis also plays a critical role in financial forecasting. The insight gathered from comparing actual results to budgeted figures can be used to formulate more accurate and realistic projections for future financial periods. It facilitates an understanding of the trends and factors that influence expenditure or income, thereby enabling a business to adjust its predictions and plans accordingly. This enhanced accuracy in forecasting ultimately leads to better performance management and strategic decision-making.

Improving Operational Efficiency

Another significant purpose of variance analysis is to enable businesses to improve their operational efficiency. By identifying the variances in different operations, a business can gain a better understanding of the areas where efficiency can be improved. For instance, if labor cost variances are consistently unfavorable, it might imply the need for staff training or process automation. Ultimately, improving operational efficiency can lead to cost reductions, productivity enhancements, and improved profit margins.

By refining budgets, aiding in forecasting future financial results, and improving operational efficiency, variance analysis serves as a vital tool in strategic financial management for any business.

Types of Variance Analysis

In the realm of finance, there are several types of variance analysis that we can use to better understand a company's performance and help influence decision making.

Sales Variance

Sales Variance measures the difference between the actual sales and the budgeted or expected sales. It enables businesses to understand the reasons for their performance, be it favorable or unfavorable. If there's a negative sales variance, it might indicate the need for a new marketing strategy. Conversely, a positive sales variance can reinforce effective sales tactics currently in place.

Volume Variance

Volume variance, closely related to sales variance, evaluates the impact of changes in the number of units sold on a company's net income. It can be broken down into sales volume and direct materials volume variances. Sales volume variance examines the overall units sold against the budget, while direct materials volume variance analyzes whether the company used more or less materials than planned. Consequently, the information provided by volume variance analysis can be vital in revising production and cost control strategies.

Cost Variance

Cost variance refers to the discrepancy between a project's budgeted cost and the actual cost incurred. Analyzing cost variance helps to keep a project financially on track. A high positive cost variance might indicate good financial performance, but it could also indicate underutilization of resources. Conversely, negative cost variance could suggest overspending, highlighting the need for tighter financial control.

Each of these types of variance analysis provides unique insights into different aspects of finance and contributes to a more holistic understanding of a company's performance. Armed with this information, decision-makers can pinpoint where things are going right or wrong and take appropriate action.

The Process of Conducting Variance Analysis

Step 1: identify the variables.

The first step in conducting variance analysis involves identifying the different variables that contribute to the performance of a certain business operation. Examples of such variables could be the cost of production, selling price, quantity sold et al. It is crucial to understand these variables and ascertain how they impact the overall business or specific project profitability.

Step 2: Determine Actual Performance

At this stage, analysts gather all relevant data and quantify actual performance. This may entail collecting records on sales, expenditures, production volume or any other relevant factors that have been identified in step one. This data needs to be precise and comprehensive in order to provide an accurate representation of the actual performance.

Step 3: Set Standard or Expected Performance

Once you’ve identified variables and have compiled data on actual performance, the next step involves setting standard or expected performance benchmarks. This might involve using historical performance data, industry standards or forecasts. The expected performance serves as a reference point against which actual performance is compared.

Step 4: Calculate Variances

Once both actual and standard performances are clearly defined, you can calculate the variances. This is essentially determining the differences between what was expected and what happened in reality. This could be calculated in units, percentage or monetary value depending on the variables being measured. The variances may be favorable (better than expected) or unfavorable (worse than expected).

Step 5: Analyze Variances

After determining variance, the next step is to analyze the results. This implies digging deeper into the data to discern exactly why variances occurred. The analysis could reveal issues such as inefficiencies, inaccuracies in estimation, unforeseen market conditions or other factors causing the variance.

Step 6: Report the Performance

This step involves collating all the information into a comprehensive report. The report should be clear and concise, summarizing all the findings and providing actionable recommendations. This report will help the management in decision-making, understanding where improvements can be made and how to strategically plan for the future.

Step 7: Implement Strategies and Monitor Effects

Finally, strategies based on the insights from the variance analysis are implemented, and its effects are closely monitored. The ultimate goal of variance analysis is not only to identify and understand discrepancies but to leverage that understanding to enhance future performance.

Role of Variance Analysis in Budgeting

In the budgeting process, variance analysis plays a vital role by rendering an analytical tool for management to assess the budget's effectiveness. Budgets are essential for financial planning and control. They provide a standard against which actual performance can be measured. Variance analysis is a method employed by managers to understand the reasons behind the differences in actual and predicted performance.

Budgeting and Variance Analysis

Quite often, the actual outcomes do not align with the budgeted estimates due to various factors, such as market trends, unexpected expenses, or changes in business strategies. Here, variance analysis comes into play. By analyzing budget variances, managers can compare actual results to expected ones. This comparison enables them to troubleshoot variances that are out of the acceptable range or conspicuous in their recurrence. As a result, they can make more informed decisions about resource allocation, adjusting goals, or revising business strategy.

Value in Identification

Perhaps one of the most valuable aspects of variance analysis is its contribution to identifying areas where actual performance deviates from budgeted expectations. It helps in evaluating the effectiveness of budgeting and also in identifying areas of inefficiencies or better-than-expected performance. For instance, if the actual cost exceeds the budgeted cost for a specific department, it's an indicator that the department could benefit from cost-reduction efforts. On the other hand, if the revenues exceed the budgeted revenue, it indicates an opportunity to capitalize on something that is generating more income than anticipated.

Guiding Future Budgets

Variance analysis can also be utilized for validation of future budget assumptions, making it an essential element in strategic decision-making. The results of the variance analysis—both favorable and unfavorable—provide valuable lessons for building more accurate and realistic future budgets.

In conclusion, variance analysis is a critical tool that allows managers to control business finances better, learn from past budgeting mistakes, and create improved, more accurate financial forecasts. Its role is invaluable for any organization aiming to ensure efficient resource allocation, cost control, and financial stability.

Variance Analysis and CSR

Utilizing variance analysis in the context of Corporate Social Responsibility is essential for businesses striving to make a positive impact on society while still maintaining a profitable model. CSR initiatives often involve considerable investment, so it's vital to effectively evaluate their financial impact and ensure that they don't lead to significant overspending.

Using Variance Analysis to Evaluate Financial Impact of CSR Initiatives

Evaluating the financial implications of any new initiative involves comparing the actual results to the budgeted expectations, and CSR initiatives aren't exempt from this. Variance analysis acts as a critical tool in this process.

You'll first need to determine the budgeted cost of each CSR initiative, including everything from direct spendings, like funding for community projects and infrastructure, to indirect costs like staff time. After implementing the initiative, you'll then use variance analysis to compare the budgeted cost to the actual expenditure.

If you find that the actual cost is more than the planned budget (unfavorable variance), you can look deeper into the elements causing this overspending. Maybe the logistics costs were higher than planned, or perhaps an unexpected expense cropped up partway through the project. Either way, understanding and addressing these disparities can help ensure better financial management of CSR initiatives in the future.

Conversely, a situation where the actual cost is less than the budgeted amount (favorable variance) signifies under-spending. While this may seem positive at first, it could indicate that certain activities were not implemented as planned, possibly undermining the effectiveness of the initiative.

Ensuring CSR Initiatives Stay Within Budget

A critical aspect of maintaining control over financial resources is ensuring spending stays within budget. Variance analysis aids in this process too, by highlighting where your CSR initiatives are overspending, underspending, or staying on track.

By closely monitoring variances, you can identify which areas are straining your budget and take corrective action. This might involve renegotiating vendor contracts, adjusting timelines, or reallocating resources. On the flip side, you can use positive variances as an opportunity to scale up the initiatives that are driving success.

In conclusion, applying variance analysis to CSR initiatives can provide valuable insights that can help keep these initiatives within budget and allow for effective evaluation of their financial implications. This process enables businesses to maintain financial stability while meeting their social responsibility objectives, creating a balanced approach to profit and purpose.

Significance of Variance Analysis for Sustainability

Variance analysis is integral to a business organization's pursuit of sustainability. It can help track and manage spending on green initiatives, sustainable practices, and environmental responsibilities, becoming crucial when trying to balance cost-cutting and sustainability goals.

Economic Sustainability and Variance Analysis

Financial sustainability is closely linked to economic sustainability. Through variance analysis, organizations can identify discrepancies between budgeted and actual expenses to uphold economic sustainability. Such an approach helps keep a check on the allocation and use of resources. In a sustainable setting, variance analysis can indicate if an organization is spending too much on certain sustainability initiatives or not sufficiently funding necessary ones.

Portfolio Sustainability

Additionally, variance analysis plays a key role in the sustainability of a company's portfolio. The shifts in market trends, changes in customer preferences towards sustainable products, or policy changes promoting sustainable practices can lead to variances in projected and actual outcomes of projects. By conducting frequent variance analysis, organizations may anticipate such fluctuations, adjust their budgets accordingly, and maintain the sustainability of their investments.

Variance analysis in Energy Efficiency

Similar significance is seen in the energy sector as well. Variance analysis can highlight deviations in energy consumption, which is crucial for organizations striving to minimize their carbon footprint. It can help identify inefficient energy practices, enabling a shift towards more sustainable usage.

Improving Decision Making

Variance analysis also aids in making informed decisions about future strategies for sustainability. It can provide actionable insights into which practices are having the most positive sustainable impact and are cost-effective. Thus, it assists in strategic decision-making regarding the allocation of resources to various sustainable practices.

To summarize, variance analysis, by scrutinizing and interpreting variations in the anticipated and actual resource usage, plays a vital role in a business organization's sustainability strides. It facilitates efficient resource allocation, comprehends market fluctuations, ensures energy efficiency, and improves decision-making, all of which are crucial to upholding sustainable practices.

Variance Analysis in Performance Management

Variance analysis at organizational level.

Variance analysis is instrumental in measuring organizational or department’s financial health and efficiency. It plays a significant role in performance management as it enables managers to dig deeper into the financial information and isolate the areas that are performing well or need improvement.

By comparing actual results to expected budgeted outcomes, managers can understand the financial performance of different departments or the organization as a whole. The disparities between what was budgeted and what has actually been achieved, or the variances, become key pointers to operational inefficiencies, wastages or even opportunities.

As a Budgeting Tool

Variance analysis helps organizations ensure they are on track with their budgets. If a department overspends in a certain area, variance analysis will signal this to managers. They will then have a chance to probe into the reasons and take corrective actions promptly.

This tool also promotes transparency in budgeting process, as managers re-evaluate the budget regularly, taking into account actual costs and identifying areas of overspending.

Enhancing Productivity and Efficiency

Within operations, variance analysis can be leveraged to improve productivity and efficiency. For example, if a department is consistently producing lower than anticipated results, management can use variance analysis to diagnose the cause.

They could look closer into production processes, labor hours, or raw materials. Depending on the findings, reforms can be made to rectify issues, such as providing additional staff training or streamlining production methods.

Performance Evaluation and Planning

Variance analysis also proves valuable in performance evaluation and future planning. By identifying areas where performance is lagging behind expectations, management gets insights into where improvements are necessary. Employees can be incentivized to meet targets, possibly improving overall performance.

Moreover, variance analysis can aid in setting more accurate future forecasts. By scrutinizing past performances and learning from them, organizations can make more informed and realistic predictions, contributing to better strategic planning.

Challenges and Limitations of Variance Analysis

Handling data incorrectly.

One common issue with variance analysis is improper data handling. Incorrect or incomplete data can lead to inaccurate results, making it hard to take appropriate action or make reliable decisions. It's essential to ensure data is accurate, complete, and up-to-date before proceeding with variance analysis.

To tackle this problem, organizations should develop robust data management practices. Data validation procedures should be in place to minimize the chances of errors infiltrating the analysis. Additionally, continuous data updates are crucial to keep up with any changes that may occur.

Temporal Discrepancies

Timing differences can pose significant challenges in variance analysis. Financial data from one period might not be perfectly comparable to that from another. Factors such as inflation, seasonality, and macroeconomic conditions can significantly affect the comparison, leading to misleading conclusions.

Addressing this problem requires adjusting data for such variables where possible or adding caveats to the analysis to indicate that timing differences may affect the results.

Overemphasis on Quantitative Analysis

Variance analysis is based on numbers and quantitative data; therefore, it could potentially lead to an overemphasis on these aspects, ignoring other qualitative factors that could be informing the variances.

To address this, it's crucial to use variance analysis as part of a broader evaluation strategy. Incorporate qualitative perspectives and industry insights to ensure a comprehensive understanding. Cross-functional team input can also provide different viewpoints, enriching the analysis process.

Neglecting Small Variances

Sometimes, little attention is paid to small variances, which might accumulate over time and create significant issues. This problem arises from the thinking that only substantial variances need attention.

Addressing small variances is through cumulative tracking; even small differences ought to be examined over time. This approach ensures that recurring small variances do not go unnoticed and can help in proactive problem-solving.

High Dependence on Benchmark

Variance analysis relies heavily on an initial budget or forecast benchmark. If the benchmark set is unrealistic, variance analysis might end up indicating problems that are not real or missing issues that are present.

To deal with this challenge, careful and realistic planning and forecasting should be emphasized. By ensuring that the benchmark set is achievable and reasonable, variance analysis can yield more accurate and useful results.

Lack of Action

A common mistake with variance analysis is failing to act upon it. The purpose of variance analysis is to inform decisions and stimulate action where necessary. Failing to act on the analysis renders the whole exercise futile.

Overcoming this obstacle requires a readiness and commitment to adjusting strategies, taking corrective actions, or redefining goals based on the insights drawn from the analysis. Openness to change based on the variance findings is a must for effective use of this analysis.

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What is Variance Analysis: A Frontier for Analysis

There are a variety of ways by which you can assess your business’ overall financial health and success. By utilising  data analytics  and performing variance analysis, you may become aware of business practices or decisions that need to be amended. But, many times, most businesses have a hard time conducting variance analysis because data is in many places, running analytics hasn’t yet been optimised to glean useful insights, and manipulating the data takes too much time. 

We’ll outline what variance analysis means, the ways in which you can carry it out smarter so it is more useful and how automation tools can help perform the work for you so that you can use variance analysis to a greater advantage.

Table of Contents

1. What is the Variance Analysis?

2. Importance of Variance Analysis

3. The Role of Variance Analysis

4. Example of Variance Analysis

5. What are the Key Terms of Variance Analysis?

6. What are the Most Common Types of Variance Analysis?

7. Challenges of Variance Analysis

8. How Automation Tools Can Help

What is the Variance Analysis?

Variance analysis is a method of assessing the difference between estimated budgets and actual numbers. It’s a quantitative method that helps to maintain better control over a business. When using variance analysis, one best practice is to review variances on a trend line so that you can readily pinpoint any dramatic shifts. Once you find anything that is suspect, variance analysis can help you to investigate the reason behind the big difference in what’s planned and what happened financially. 

During a reporting period, you can sum all variances to see if your business is over or under-performing. When you notice a significant shift in the variance trend line, then you can become aware of dysfunction and work to resolve it. But, where do you begin and how can you pinpoint what’s causing the variance? This is where automation can help to assess the data points and highlight the issues. 

The variance analysis that you choose to focus on will depend on the type of business you operate. The reason for variances also are dependent on certain factors, like:

• Market conditions 
• Budgeting standards 
• Difficulty benchmarking 
• Material variances
• Overhead variances 
• Labour variances 

Importance of Variance Analysis

Variance analysis provides organisations with a lot of benefits, including:

• Planning:  Helps managers to budget smarter and more accurately
• Control:  Assists in more significant control management of departments and budgeting 
• Responsibility:  Helps with the assignment of trust within an organisation 
• Monitoring:  Helps to monitor success and failure 
• Sets Expectations : Encourages forward-thinking and helps to set benchmarks 

Variance analysis becomes an integral part of an organisation’s information system. Not only does it help to regulate control across departments, but it also provides a running tab of what can be realistically expected versus what occurs. 

The Role of Variance Analysis

Variance analysis is used to assess the price and quantity of materials, labour and overhead costs. These numbers are reported to management. While it’s not necessary to focus on every variance, it becomes a signalling mechanism when a variance is salient. In this way, management can rely on variance analysis to help to improve the company’s overall performance or  process improvement  protocol. 

More importantly, variance analysis plays a significant role in decision-making and how managers approach tasks and projects. When performed correctly and consistently, it can help to keep teams on the right path to achieve long-term business goals. However, many businesses fail to reap the benefits of variance analysis because it has to be performed consistently and promptly to work. 

To accurately forecast future revenue or costs, it is necessary to have organised data from history.  This calls for automation solutions such as  SolveXia  that can store all data in a centralised location and can automatically be pulled, manipulated and transformed into insights for decision-making. When your financial team is being pulled in so many directions and spends time on low-value time-consuming data entry and repetitive tasks, then variance analysis can easily fall by the wayside. A  data automation tool  can maximise your team’s productivity by pulling data from various sources, providing real-time analytics and reports to key stakeholders. 

Example of Variance Analysis

Let’s take a look at how this works in a real-world scenario with a sample of variance analysis. 

A business uses variance analysis to find there is a $50,000 variance in one of its cost centres. 

To determine how and why this happened, it requires further variance analysis to understand if the difference came from price changes or a difference in the quantity of materials being used. Maybe it is a growing trend or a one-off event. It could also be erroneous data entry. Either way, if the company aims to keep costs low and operate at its maximum efficiency, then it’s necessary to have these results immediately to help manage future operations. 

For accurate variance analysis, data must be correct to reflect what happened. With automation, you will be able to quickly link up all your data systems and compare historical data with current data without human interference and the system will highlight what has since changed allowing the business to find the source of the issue fast and understand quickly if it is a cause for concern, or if there is a risk or opportunity in the business.

What are the Key Terms of Variance Analysis?

With these variance analysis examples in mind, there are some key terms to remember when performing your own analysis and to better understand its purpose. Take a look:

• Overhead costs: Overhead costs are a business; operating expenses, such as office rent costs, insurance costs, and the like. In order to minimise expenses, companies audit their own operating expenses to see where overhead costs may be cut.
• Variable price and rate variance: These refer to the changes in the cost of products or services. They may change because of unforeseen reasons or be adjusted to better reflect consumer demands and supply rates.
• Budgets: Budgets are financial plans used to allocate spending and mitigate against overspending. Budgets can be revised in real-time to meet goals.
• Fixed budget variance: Fixed budget variance refers to the difference between overhead costs in a budget and the actual amount of overhead costs in a variance period.

What are the Most Common Types of Variance Analysis?

Here’s a look at the most common types of variance that occur within organisations: 

1. Material yield variance:

This is the difference between what you expected to use and what you used, multiplied by the cost of the materials. You can calculate this with this formula: (the actual unit used - standard unit usage) x standard cost per unit.

This helps companies determine if they are using more materials than they actually need to be. With this variance known, companies can adjust their purchase orders from suppliers and reduce waste.

2. Labour efficiency variance:

This is a measure of how well you utilise labour relative to what you expect to need. The variance is calculated by (actual hours - standard hours) x standard rate.

With this number in mind, companies can assess how efficiently their labour is being used and if the pricing is a good fit for the business’ needs.

3. Fixed overhead spending variance:

The difference between the actual fixed overhead expense and the budgeted overhead expense. Since this is supposed to be a fixed amount, it shouldn’t vary so much from the budget.

If the variance is high between the budget and the actuals, it signals room for improvement in which the company can revisit its budget plans. It’s useful to more accurate budget allocation to see if more money can go to different places for the business to function more effectively.

4. Purchase price variance: 

‍ Take the actual price paid for raw materials and subtract the standard cost times the number of units used. 

5. Labour rate variance: 

‍ Same as above, but with labour instead of products. Take the actual price paid for a direct job, subtract the standard cost and multiply by the number of units used (wages). 

6. Variable overhead spending variance: 

‍ Subtract the standard variable overhead cost per unit from the actual cost incurred. Then, multiply the remainder by the total unit quantity of output.   ‍

7. Variable overhead efficiency variance: 

‍ This is the difference between how many hours were worked versus what was budgeted for the work. It is calculated by standard overhead rate x (actual hours - standard hours). 

Not every organisation will focus on the same variance calculations. Depending on your service line and business goals, you will choose what variance analysis makes the most sense to track to ensure you are maximising efficiency and minimising costs. 

Challenges of Variance Analysis 

From all we know, there is a lot in favour of using variance analysis to help control business and manage finances well. However, there are challenges to variance analysis. 

• Time delay:  Variances are calculated at each month’s end by the accounting team. Then, the information is shared with management teams. But, organisations don’t pause their inputs and outputs while waiting for variance analysis to happen so that this time delay could result in delayed red flags. 
• Source information:  Some of the information needed to calculate variances don’t always appear in regular accounting reports. Therefore, it may take extra effort on behalf of a finance team to find this information. If management won’t correct problems by using this information, then it is more of a cost (in time) than it is a benefit (in solutions). 
• Standard-setting:  Setting benchmarks and estimations could come from different sources and standards or even be affected by politics. So, when a variance is recorded, it may be possible that it doesn’t also result in useful information.  

How Automation Tools Can Help 

Variance analysis is based on numbers and data. When you have data spread out across spreadsheets and in different records within an organisation, then compiling and assessing data becomes tricky and timely. One of the challenges with variance analysis from the get-go is the timeliness of reporting, so this is where automation tools can come in to maximise efficiency. 

Automation tools such as  SolveXia  help to benefit variance analysis by providing:

• Data integrity:  Allow the software to compile and store data that is unaltered, accurate and safe from errors. It also removes low-value manual tasks so staff can focus on high-value analytics to drive more significant insights and success.
• Visibility:  Anyone who is granted access to an automation tool can take a look at the numbers themselves, which helps to improve transparency within a business. It provides real-time dashboards and real-time alerts so that variances that pose strategic risks or opportunities are flagged up as they happen, and the correct people are notified in real-time. This also improves compliance and enables processes to be mapped out, removing critical man dependency.
• Timeliness:  Automation tools can collect, transform and process data in seconds. This means that information can be accumulated and shared much more quickly than when relying on it being done manually. Reports can be sent out by the system automatically to designated people, so the right people have the correct information at their fingertips. 
• Connected data:  Data is connected and all in one place when it’s being stored in an automated software solution. It connects with all legacy systems, so everything is in one place to run analytics to gain more precise insights than ever before.

Wrap Up: Measure to Manage 

Managing a business comes down to measuring inputs and outputs. By keeping track of budgets and actuals, you can utilise variance analysis to flag any significant fluctuations from what was otherwise expected. 

You can leverage automated software solutions like  SolveXia  to help store and manage data and information. These tools also help businesses thrive by maximising productivity and lowering costs. Automation solutions can quickly collect, transform and process mass amounts of data in seconds, relieving your team of having to perform time-consuming data entry and manual manipulation. With all data stored and centralised, you can standardise processes and automate workflows to reduce errors and adhere to compliance. There’s a lot you can accomplish when you include automation solutions into your day-to-day workflows. Stakeholders, customers and employees all reap the benefits of automation solutions.

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How to Conduct Variance Analysis in Excel

Variance analysis is a fundamental tool used by businesses to analyze changes in financial results or operational efficiencies over time. It helps businesses understand the root cause of differences between actual and budgeted results, which can be used to make informed decisions for future planning. Conducting variance analysis in Excel is a straightforward task that can be achieved with just a few clicks. In this article, we will explore the concept of variance analysis and learn how to use it in Excel to improve business performance.

Table of Contents

What is Variance Analysis and Why is it Important?

Variance analysis is a financial management tool used to analyze differences between actual and budgeted results. These variances can be either favorable (in which actual results exceed budgeted results) or unfavorable (in which actual results fall short of budgeted results). In both cases, it is essential to determine why the performance differed from expectations, so that corrective measures can be taken for future budgeting and planning.

Companies use variance analysis to measure financial performance at the end of each reporting period and to highlight potential areas of improvement. It is a powerful tool to analyze profit margins and manage expenses, which can ultimately enhance the overall business performance.

One of the key benefits of variance analysis is that it helps businesses to identify trends and patterns in their financial performance. By analyzing variances over multiple reporting periods, companies can gain insights into the factors that are driving their financial results. This can help them to make more informed decisions about future budgeting and planning, and to identify areas where they may need to adjust their strategies.

Another important use of variance analysis is in identifying areas of risk within a business. By analyzing variances in different areas of the business, such as sales, expenses, and production costs, companies can identify potential areas of risk and take steps to mitigate them. This can help to reduce the likelihood of financial losses and improve the overall financial health of the business.

Understanding the Types of Variances

There are two main types of variances in variance analysis: price variances and quantity variances.

Price variances relate to changes in the price of goods or services, which can be caused by changes in market conditions or operational inefficiencies. Quantity variances, on the other hand, are based on changes in the volume of production or service delivery.

It is crucial to understand the type of variance you are dealing with, as this can help you to identify the underlying cause and take appropriate action.

Price variances can be further broken down into two subcategories: favorable and unfavorable. A favorable price variance occurs when the actual price paid for a good or service is lower than the expected price. This can be due to negotiating better deals with suppliers or taking advantage of market fluctuations. An unfavorable price variance, on the other hand, occurs when the actual price paid is higher than the expected price. This can be caused by factors such as inflation or unexpected price increases from suppliers.

Quantity variances can also be broken down into favorable and unfavorable categories. A favorable quantity variance occurs when the actual quantity produced or delivered is higher than the expected quantity. This can be due to increased efficiency in production or better demand for the product or service. An unfavorable quantity variance occurs when the actual quantity produced or delivered is lower than the expected quantity. This can be caused by factors such as equipment breakdowns or labor shortages.

Identifying the Causes of Variances

Once you have identified the type of variance, it is time to establish the root cause. Conducting a variance analysis in Excel can help you identify the key drivers contributing to the variance. Possible causes could include changes in the market, pricing strategy, changes in the cost of labor or raw materials, or operational inefficiencies.

Identifying the cause of a variance is crucial to the success of any business. Once you have identified the root cause of the problem, you can develop effective solutions to correct the issue and prevent it from recurring in the future.

One important factor to consider when identifying the causes of variances is the timing of the variance. For example, if the variance occurred during a specific time period, it may be related to a seasonal trend or a specific event that occurred during that time. Understanding the timing of the variance can help you narrow down potential causes and develop targeted solutions.

Another factor to consider is the impact of external factors on the variance. For example, changes in government regulations or economic conditions can have a significant impact on a business’s performance. It is important to consider these external factors when identifying the root cause of a variance and developing solutions to address it.

Setting up the Spreadsheet for Variance Analysis

The first step in conducting variance analysis in Excel is to set up the necessary worksheet. The area allocated for variance analysis should contain the actual results, budgeted results, and the calculations used to determine the variance amount and percentage.

It is essential to ensure that the data is correctly formatted and that the formulas are accurate to ensure that the information generated from the analysis is reliable. You can use the simple functions available in Excel, such as SUM, AVERAGE, and IF statements, to perform the required calculations.

Additionally, it is recommended to label each column and row clearly to avoid confusion and make it easier to interpret the results. It is also important to regularly update the data in the spreadsheet to ensure that the analysis is based on the most recent information available. By following these steps, you can effectively set up a spreadsheet for variance analysis and make informed decisions based on the results.

Creating a Budget vs Actual Variance Report in Excel

Creating a Budget vs Actual Variance Report involves comparing actual results against the budgeted results to determine the variance. The report usually contains the following information:

• Budgeted amount
• Actual amount
• Variance amount
• Percentage variance

The report can be presented in the form of a table or a chart for easy interpretation and analysis. You can also customize the report by adding additional columns or including conditional formatting options to highlight significant variances.

It is important to note that creating a Budget vs Actual Variance Report is not a one-time task. It should be done regularly, such as monthly or quarterly, to track the progress of the budget and identify any potential issues early on. Additionally, the report can be used to make informed decisions about future budgeting and resource allocation.

Analyzing the Variance Report: Key Metrics to Look For

While analyzing the variance report, there are a few key metrics to look for, including:

• Excessive variances: When the variance is significantly high or low, it signals an issue that needs to be addressed.
• Trending: Analyzing the variances over time can help identify trends and underlying issues.
• Root cause analysis: Digging deeper into the variances can help establish the causes so that corrective action can be taken.

Identifying trends and the root causes of the variances is crucial to understanding the financial performance of the business. It enables businesses to take corrective measures quickly and develop long-term strategies for improvement.

Another important metric to consider when analyzing the variance report is the impact of external factors. External factors such as changes in the market, economic conditions, or government policies can significantly affect the financial performance of a business. By analyzing the variance report in conjunction with external factors, businesses can gain a better understanding of the impact of these factors on their financial performance and make informed decisions accordingly.

Using Conditional Formatting to Highlight Significant Variances

Conditional formatting is a powerful tool that you can use to highlight significant variances. It draws attention to the most important data by automatically highlighting cells that meet certain criteria.

You can use conditional formatting to highlight excessive variances, negative variances, or positive variances, depending on your requirements. This technique simplifies the analysis process and makes it easier to identify potential issues quickly.

Tips for Effective Variance Analysis in Excel

Here are a few tips to consider when conducting variance analysis in Excel:

• Ensure that the data used for the analysis is accurate and reliable.
• Use a consistent format for presenting the report.
• Include visual representations to make the data more accessible.
• Review the report periodically to identify trends and make necessary adjustments.

Avoiding Common Mistakes in Variance Analysis

Common mistakes when conducting variance analysis in Excel include:

• Using incomplete data sets
• Using erroneous calculations
• Not including all relevant data

To avoid these mistakes, always begin with a complete data set, double check your calculations, and ensure all relevant data is incorporated into the analysis.

Best Practices for Interpreting and Presenting Variance Analysis Results

To effectively interpret and present variance analysis results, keep the following best practices in mind:

• Ensure that the report is easy to read and understand by incorporating visual aids.
• Ensure that the report is accurate and reliable.
• Share the report with stakeholders to ensure that everyone is on the same page.
• Identify the root cause of variance and develop an action plan to address the issue.

Automating Variance Analysis with Pivot Tables and Macros

Excel has several features that allow you to automate variance analysis, including Pivot Tables and Macros.

Pivot Tables can help you quickly summarize and analyze large data sets. You can use them to create custom reports from the data, making it easy to identify variances and trends. Macros can automate repetitive tasks, such as formatting reports, saving files, generating invoices, and other activities to help streamline your workflow.

Advanced Techniques for Forecasting and Predictive Analysis using Variance Data

Advanced techniques include using variance data to create forecasts for future performance. Based on historical trends and patterns, you can use the data to make accurate predictions about future performance.

Predictive analysis provides insights into what to expect and enables businesses to make data-driven decisions.

Case Study: A Practical Example of Variance Analysis in Excel.

To illustrate the practical application of variance analysis in Excel, consider the case study of a brick-and-mortar retailer. The retailer wanted to analyze the variance between actual and budgeted sales figures for each of its stores. After conducting a variance analysis, the retailer discovered that one of its stores was consistently underperforming, and the cause was found to be related to the location of the store.

The retailer used this data to develop a data-driven relocation strategy that resulted in the store’s revenue increasing by 20% year-over-year.

Conclusion: The Importance of Incorporating Variance Analysis into Your Business Strategy

Variance analysis is a powerful tool that helps businesses identify strengths, weaknesses, and opportunities for improvement. It can help businesses make informed decisions, optimize financial performance, and plan for the future. With the use of Excel, it is much easier to conduct high-quality variance analysis and draw actionable insights from the data.

By incorporating variance analysis into your business strategy, you can gain a competitive advantage in your industry and drive better decision-making for future success.

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Variance Analysis

Last updated 22 Mar 2021

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In this short revision video I explain the basics of variance analysis.

• Variance analysis
• Favourable variance
• Adverse variance

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Variance Analysis and Flexible Budgeting

By: Robert S. Kaplan

Facilitates the teaching of cases on variance analysis and flexible budgeting. Uses algebra, diagrams, and numerical examples to illustrate the calculation of price, quantity, and mix variances for…

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Facilitates the teaching of cases on variance analysis and flexible budgeting. Uses algebra, diagrams, and numerical examples to illustrate the calculation of price, quantity, and mix variances for revenues and costs, and a flexible budget for analyzing indirect and support costs.

Oct 26, 2000 (Revised: Mar 9, 2004)

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Harvard Business School

101039-PDF-ENG

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Edward meinert.

1 Department of Primary Care and Public Health, Digital Global Health Unit, Imperial College London, London, United Kingdom

2 Department of Paediatrics, Healthcare Translation Research Group, University of Oxford, Oxford, United Kingdom

Abrar Alturkistani

Kimberley a foley, david brindley, associated data.

Case study protocol – educating administrative staff to engage with young patients.

Case study protocol – the impact of climate change on public health.

Case study protocol – data science in healthcare using real world evidence.

Ingredient costs variance calculation.

The World Health Report (2006) by the World Health Organization conveys that a significant increase is needed in global health care resourcing to meet the current and future demand for health professionals. Electronic learning (e-Learning) presents a possible opportunity to change and optimize training by providing a scalable means for instruction, thus reducing the costs for training health professionals and providing patient education. Research literature often suggests that a benefit of e-Learning is its cost-effectiveness compared with face-to-face instruction, yet there is limited evidence with respect to the comparison of design and production costs with other forms of instruction or the establishment of standards pertaining to budgeting for these costs.

To determine the potential cost favorability of e-Learning in contrast to other forms of learning, there must first be an understanding of the components and elements for building an e-Learning course. Without first taking this step, studies lack the essential financial accounting rigor for course planning and have an inconsistent basis for comparison. This study aimed to (1) establish standard ingredients for the cost of e-Learning course production and (2) determine the variance instructional design has on the production costs of e-Learning courses.

This study made use of a cross-case method among 3 case studies using mixed methods, including horizontal budget variance calculation and qualitative interpretation of responses from course designers for budget variance using total quality management themes. The different implementation-specific aspects of these cases were used to establish common principles in the composition of budgets in the production and delivery of an applied health professional e-Learning course.

A total of 2 case studies reported significant negative budget variances caused by issues surrounding underreporting of personnel costs, inaccurate resource task estimation, lack of contingency planning, challenges in third-party resource management, and the need to update health-related materials that became outdated during course production. The third study reported a positive budget variance because of the cost efficiency derived from previous implementation, the strong working relationship of the course project team, and the use of iterative project management methods.

Conclusions

This research suggests that the delivery costs of an e-Learning course could be underestimated or underreported and identifies factors that could be used to better control budgets. Through consistent management of factors affecting the cost of course production, further research could be undertaken using standard economic evaluation methods to evaluate the advantages of using e-Learning.

Introduction

The World Health Report (2006) by the World Health Organization (WHO) [ 1 ] conveys that a significant increase is needed in global health care resourcing to meet the current and future demand for health professionals. Current challenges to health care resourcing include the increasing demand resulting from the aging population’s need for chronic disease management, in addition to the growing population placing an increased demand on primary care [ 2 ]. This increased demand on resources requires a scalable means to train resources; opportunities to optimize training through alternatives to face-to-face instruction present the possibility of increasing the pace and breadth of education to health care resourcing. A 2015 WHO systematic review of e-Learning for undergraduate health professional education concluded that “computer-based and Web-based e-Learning is no better and no worse than face-to-face learning with regards to knowledge and skill acquisition” [ 3 ]. e-Learning is defined as “an approach to teaching and learning, representing all or part of the educational model applied, that is based on the use of electronic media and devices as tools for improving access to training, communication, and interaction and that facilitates the adoption of new ways of understanding and developing learning” [ 4 ]. It presents a possible opportunity to change and optimize training in health professions (including clinical, allied, and applied health sciences, as well as patient education) by providing a scalable means for instruction, thus reducing the costs necessary in delivery and implementation. If we accept that pedagogically e-Learning can result in a positive educational effect used under optimal circumstances, which is still subject to ongoing investigation, there remains the possibility that deployment of e-Learning could affect the scale, cost, and reach of health professions education.

Research Problem

One of the motivations for implementing e-Learning is the potential long-term efficiency gain in its delivery model [ 5 , 6 ]. A course delivered digitally versus the cost of a lecturer providing face-to-face instruction appears to have long-term cost favorability [ 7 ]. The literature often suggests that a benefit of Web-based learning is its cost-effectiveness compared with face-to-face instruction [ 8 ]; however, there is limited evidence validating comparison with other forms of instruction or standards for the budgeting of the costs in the production and execution of e-Learning courses. In the case of massive open online courses (MOOCs), there is limited evidence on the costs associated with their production [ 9 ]. In addition, the costs to develop an e-Learning course are significant when executed to a high standard. Although there are studies that capture data relating to factors associated with educational costs, measurement in these studies are collected inconsistently and include a wide variety of factors [ 3 , 10 ]. There is limited transparency in costing models because of sensitivity on where direct costs should be applied [ 11 ]. A systematic means is required to comprehensively record costs that can then subsequently enable testing of whether the e-Learning course has desirable economic properties and under what scenarios [ 12 ]. If proven so, this could assist in addressing the high cost of delivering health professions education. By contrast, should evidence point the other way, having discrete data points will allow those involved in online health education to identify ways to optimize costs in delivery. The primary issue here is identification of the direct and indirect costs in implementation, which then allows the execution of further economic evaluation.

Aims and Objectives

This aim of this study was to establish an approach for identifying costs in the design, development, and deployment of applied health (defined as applied health subjects) sciences e-Learning courses and to subsequently propose a budgeting framework for the planning and management of e-Learning course implementations. The costs in this study include the direct and indirect costs from inception through course delivery. This approach will allow course designers and implementers to leverage knowledge gained from the study’s e-Learning case studies across different implementation contexts to better plan and manage future implementations, which will also create a reusable framework to apply cost planning. This work will demonstrate the effect in pre-implementation budget management against the proposed framework and should result in better course planning.

The study’s objectives are as follows:

• Establish an approach to capture standard components or ingredients for the cost of the production of an e-Learning course.
• Determine the effect that instructional design has on the production costs of e-Learning courses.

The study’s aims and objectives intend to address a gap in the research literature concerning implementation details on planning and executing e-Learning in health professions education [ 8 ]. In addition to limited cost-centered studies on e-Learning for health professions education, there are limited details on how course designers and producers are calculating the associated costs for production of these course types. Developing models will allow for the adoption of data sharing and course planning for improved management in execution of this course method and for further refinement and analysis. To explore this issue, this research examines the following 3 distinct e- Learning implementations as case studies.

Educating Administrative Staff to Engage With Young Patients

The course was created as a small private online course (SPOC) to prepare general practice administrative staff for issues in the management of adolescents. The course used case studies to provide training to help general practice staff feel confident in helping adolescents with a goal of improving the patient experience.

The Impact of Climate Change on Public Health

This course was created as an MOOC to educate citizens on the relationship between climate change and public health by using a multidisciplinary academic framework in data science to analyze, interpret, and present evidence. Core case studies focused on climate change and its health economic effect on local, regional, and national health systems.

Data Science in Health Care Using Real-World Evidence

This course was created as a blended MOOC to make learners aware of the effect data science can have on medicine and inspire the application of these methods across various undergraduate curriculum disciplines, the UK National Health Service commissioning support organizations, health care regulation organizations, and life sciences industries (ie, pharmaceuticals, biotechnology, and medical devices).The implementation of the blended MOOC was executed as a face-to-face course for learners; learners first took part in the MOOC and were then offered a residential course examining case studies. The target audience of the MOOC was allied health professionals or citizens looking to transition or enhance skills in data science in health care–related industries such as the pharmaceutical industry or biotech organizations. One of the key objectives of the course was to establish a global network of people to continue and advance the dialogue on data science in health care. Some of the course outcomes include the use and application of real-world evidence data collection and analysis techniques in health care settings.

A mixed-methods case study design was selected to support a systematic means of observing the subject of investigation [ 13 ] and the ability to combine quantitative and qualitative approaches [ 14 ]. Mixed-methods research presents an opportunity to combine the strengths of quantitative and qualitative research to counteract the limitations inherent when each method is used in isolation [ 14 ]. In this study, for example, the limitations of quantitatively isolating cost differences in the 3 cases are strengthened by the repeatable and generalizable nature of the qualitative approach used to interpret results. Case studies were selected based on their relevance to the study inquiry and the ability to capture, record, and analyze data from each case. Each study was structured through a study protocol to govern the case execution.

Case Study Overview

Case overview.

The objective of the case study is to inform the way future costs are budgeted in the development of e-Learning courses. The research forms part of a broader investigation into the costs associated with e-Learning course production; the main focus of each case was to collect primary evidence in the construction of these costs to allow for further research comparing results with other Web-based learning implementation types.

• Study question: how are the total costs for the production and delivery of an e-Learning course (dependent on type) calculated?
• Proposition: actual and budgeted costs will vary in the production or delivery of this course type.

Existing research literature indicates challenges in the capture of total costs for the production of Web-based learning despite standard methods for cost calculation [ 8 ]. The reason for this variance is likely because the skills required to create instructional learning design and to capture costs are different, and educators are not trained in cost accounting methods.

The analytical framework for this investigation is based on the cost analysis methods underpinning education economic evaluation developed by Levin [ 15 ], which extends the standard costing and variance calculation principles of activity-based costing [ 16 - 18 ]. The ingredients method [ 15 ] is used to capture total cost production against cost categories. It examines the core composition of costs in the delivery of an education intervention; this is an activity-based costing approach that seeks to understand the core components required for delivery. Defining core costs is critical to performing further economic evaluations, though it is important to note that the scope of this research is limited to cost identification and not further economic analysis (eg, cost-benefit analysis, cost-effectiveness analysis, cost-utility analysis, and cost-feasibility analysis).

Case study protocols ( Multimedia Appendices 1 , 2 , and 3 ,) were developed at study commencement to demonstrate the way costs would be captured and analyzed. These protocols, in addition to a protocol for qualitative and quantitative analysis of learning effect (which is outside the scope of the cost investigation) [ 19 ] were drafted, submitted, peer reviewed, and approved.

Data Collection Procedures

Evidence to be expected.

To validate the costs reported in the actual budget (which was an actual cost report), at least 2 separate sources confirming the final reported amount were sought (eg, for a reported incurred cost for staff, timesheets were reviewed to match hours to costs, task completion, and assignment in a project plan). These data comparisons increased the likelihood that reported data were accurate.

Events to Be Observed

Although the course implementation was observed and additional studies completed investigating the education effect, the scope of this study was centered on the cost decision making, and the way production affected cost delivery. Therefore, the observation scope for this study focused on reported costs and the way these correlated data to time actuals.

Documentation to Be Reviewed

The project budget, actual costs, and timesheets were reviewed for this study. Although there will be a review of the completed course and observation of the way the course uptake is completed, the latter shall be excluded from this study. A traceability log was maintained in Microsoft Excel linking the research questions to data sources and the study findings.

Protocol Questions

Study question: how are the total costs for the production and delivery of an e-Learning (type dependent on implementation type) course calculated?

• The costs will be measured and ingredients captured and analyzed to understand the factors affecting course production.
• Data will be collected to support the cost analysis categories.
• The corresponding evidence will be used to summarize ways that cost capture practices could be improved.

Study Framework

Each case study followed a 6-stage process in the investigation ( Table 1 ) [ 13 ]. The research question centered on identifying the total costs of production and delivery in these e-Learning implementations, and the effect of factors on variance from anticipated budgets. It was selected because evidence from the literature suggests inconsistency in the determination of costs for the delivery of Web-based courses [ 20 ]. This is significant because the lack of consistent cost capture mechanisms for Web-based learning compromises any further evaluation. Despite available methods to avoid this outcome, the literature presents research with claims that Web-based learning is more cost effective than face-to-face learning. This research provides a structured means to generate evidence to subsequently evaluate such claims by collecting baseline data on course production for further evaluation.

Case study framework .

The research design ( Table 2 ) was structured on 4 components (proposition, the case definition, logic linking data to the proposition, and criteria for interpreting findings) to explore the following research question: how are the total costs for the production and delivery of e-Learning calculated (with the e-Learning implementation type variant depending on the case study)? Given the inconsistency in the presentation of costs in the literature and recognizing that using budgets to determine the cost of educational delivery is insufficient [ 21 ], the governing proposition of the investigation was that there would be variance between the budgeted costs and the actual costs to produce the course. This was explored through cases that would examine the cost and the measurement of costs and place value on ingredients. Levin developed this ingredients method to capture and analyze the costs in the delivery of an educational program. To link the case to the proposition, the cost calculation was completed and then interpreted via a variance calculation of actual to budgeted costs, and rationales were developed to justify variations.

Case study research design .

Examination of these cases provides data to analyze the relationship between course production and budgeting in the delivery of e-Learning and provides evidence for constructing accurate budget models.

Each case was tested for construct validity (testing that data sources come from multiple sources), external validity (testing that demonstrates how principal findings could be extensible) and reliability (testing that shows how the activities of the study can be replicated) to ensure data triangulation, the ability for study replication, and standardization for project data collection [ 13 ]. Ethical approval for each study was obtained through the Imperial College Education Ethics Research Committee (case 1: EERP1516-005; case 2 and 3: EERP1617-030).

The investigation was focused on cost measurement and analysis, structured by 3 cost categories, and further subdivided using a 7-step process (illustrated in Table 3 below) to analyze the pre- and postproduction budget [ 21 ]. Levin’s model uses an activity-based standard-costing accountancy approach, which assigns costs as they are consumed per implementation area [ 25 , 26 ].

Course production ingredients cost analysis.

Evidence from the course was retrieved from project documents and records of finance activity. The data collection strategy was executed from a realist perspective to capture the decisions made by the course designers; however, it did not incorporate a relativist perspective with regard to stakeholders, through further qualitative investigation. This decision was made to avoid interference in course delivery. To control biased selectivity and reporting bias, the data were sourced through multiple sources, including finance logs (and notes), data submitted to the employer, the funder, and timesheets. A traceability log was maintained linking the study questions to the relevant data sources and the study findings.

Data analysis centered on the 3 cost categories and followed the 7-step process for cost definition.

Category A: Concept and Measurement of Costs

The preproduction budget was analyzed for the following ingredient categories: (1) personnel, (2) estate charges, (3) equipment and materials, (4) indirect costs and (5) stakeholder costs. The initial budgets did not reflect time for stakeholder costs (effort from third-party lecturers); therefore, this was captured as the additional time that was monitored in the study (and added for budget variance calculation), as there was no value for this in the data submitted to the funder.

Category B: Placing Values on Ingredients

With the full cost of production defined, values were associated with each ingredient subcategory to reflect the chargeable cost (including direct and indirect costs).

Category C: Calculating Costs

As each course was implemented in 1 year, and the courses were Web-based, there were no multiyear costs to calculate; the one-time cost of the project and the variance of the projected budget to the actual budget were the only variables under consideration. To accomplish this, the variance calculation of the budget to the incurred costs was undertaken at the completion of the project. The variance calculation compares actual costs to adjusted standard conditions based on occurrence [ 28 ].

The variance calculation formula is as follows: Variance = Actual spending − Budgeted spending.

Analyzing Costs of Observed Budget Variance Calculations

To determine the reasons for favorable or negative budget variance, the course designers were interviewed to determine the factors contributing to budget variance. This qualitative work was planned via the consolidated criteria for reporting qualitative research [ 29 ] to ensure that the appropriate trained staff conducted interviews, study design included the purposeful sampling of the course designers, sessions could be validated in the interviews, and the resultant analysis and findings would be repeatable [ 29 ]. The sessions were conducted as semistructured interviews transcribed and coded using thematic analysis [ 30 ] using total quality management (TQM) as coding criteria. TQM [ 31 ] is a quality appraisal method used to analyze factors affecting operational efficiency [ 32 ]. TQM provides a means to categorize issues relating to people, process, or technology through applying a systems approach to management (see Figure 1 ). For each area of cost variance, the course designers were asked to review budget reports to identify stages in the project lifecycle for variances in forecast and to describe the contributing factors. After the interview, these were coded independently by 2 researchers to create a novel means of interpreting the cost calculation variance. For example, if a cost variance was attributed to stakeholder costs, the researchers would examine reported quarterly budgets (or at the project time interval) and determine where the variance began occurring. If the variance commenced during the build stage of the project, the project plan was analyzed, and questions surrounding the activities of the project were asked of the course designers to determine the root cause.

Isolating variance during project stage to total quality management criteria.

The key themes for the TQM analysis are presented in each case indicating the summary perspective of areas for improvement or efficiency in e-Learning budget creation.

The findings of the variance calculation and the deductive-inductive interpretation of reasons leading to variation were presented in a case report to the course design and production team. Feedback was gathered on analysis and results; the key findings for each report were prepared for publication for a peer-review journal.

Cross-Case Synthesis

To derive results from the composite analysis of the cases, this study makes use of cross-case study synthesis [ 13 ] as illustrated in Figure 2 . The standard variables in the cases are centered on ingredients and their incurred cost variance from budget.

Cross-case synthesis. MOOC: massive open online course; SPOC: small private online course.

Course Production Costs

Costs for each case were summarized into components and separated into ingredient cost categories ( Table 4 ).

Ingredient categories .

Upon completion of the analysis of the ingredients of the course production, initial budgets were created and submitted to the funder.

Category C: Analyzing costs

Budget variance calculation.

The project implementation costs, in this case, had a negative variance of 41% ( Multimedia Appendix 4 ). The most significant negative variance (135%; Multimedia Appendix 4 ) was in equipment and materials, primarily from the costs of app development in the creation of a Web-based course. As the production team had not created a Web-based course before, there was a significant underestimation of the amount of time required to build and configure the system (which was developed using the Open edX learning management system platform) and complete course editing. In addition, specialist recording equipment had to be procured that was not understood at the time of budget completion. The next most substantial negative variance (76%; Multimedia Appendix 4 ) was the amount of time required from third-party stakeholders in the production of learning materials. The amount of time allocated for recording the lecturers was underestimated; there had to be several re-runs of the recordings to address content changes. The lowest negative cost variance (31%; Multimedia Appendix 4 ) was in the personnel costs to deliver the course. Although the variance was the smallest of the 3 categories, it was significant because the course production team did not receive any additional compensation for their additional work; this extra work was captured in the project timesheets but not submitted to the funder for reimbursement.

The actual costs varied from the budgeted cost in personnel, equipment and materials, and stakeholder costs, and the total cost of production has a negative variance of 113% ( Multimedia Appendix 4 ) from the budgeted amount. The most significant variance was in stakeholder costs, where the total time for external lecturers and subject matter experts to deliver work was significantly underbudgeted, with a negative variance of 190% ( Multimedia Appendix 4 ). The reason for this underestimate was that videos had to be reshot twice and the amount of time allocated to retrieve stakeholders and complete associated course updates dramatically affected the budget. The second largest variance was in personnel; the cost variance was directly related to the additional production time required for the video reshoots, in addition to the iteration of the development of the platform. The course implementation online learning provider also switched from edX to FutureLearn learning management system during the project, requiring rework of previously completed tasks. As the team was not experienced on the FutureLearn platform, this further accounted for additional effort and the unfavorable budget variance; a team with experience and training on design for the course material would most likely have attained different results. Finally, equipment and materials were also underestimated with a negative variance of 133% ( Multimedia Appendix 4 ), having to do with additional software required for video editing and additional workstations gathered to deal with additional editing required in the course development.

In contrast to the previous case studies, this case demonstrated a positive variance of 16% ( Multimedia Appendix 4 ) from the initial budget. Stakeholder costs for subject matter expert lecturers were slightly overestimated but close to budget. It is important to note that the third-party stakeholder team had significant previous experience working together for producing related coursework, and this could have led to the precision in effort estimation. Equipment and materials had a significant positive variance of 37% ( Multimedia Appendix 4 ); the reason for this is that not all the equipment planned for the course development was necessary because there was efficiency derived in the course production and streamlining of data science modules that were thought to have required custom app development. Personnel had a negative variance of 13%; this was related to additional effort required in video editing. In addition, the course was completed ahead of schedule and in less time than was anticipated.

The construction of the cost ingredients and subsequent cost analysis underwent 3 validation tests ( Table 5 ).

Cross-case results validation tests .

Issues affecting budget variance were classified using TQM to categorize factors influencing the budget ( Table 6 ). Although each course was implemented with a varying form of e-Learning, the issues affecting each case were similar and cross-applicable. The critical consideration in budgeting is less an aspect of the type of e-Learning, but more the planning associated with the project management of the creation of the course.

Total quality management category of issues affecting budget adherence to the model.

a Not applicable.

b Applicable.

Project Management

Each case implemented project management methods for the organization of crucial deliverables and tasks in their design and integrated learning design methodology in different ways. Case 1 employed project-related task-centered actions constructed to match each learning outcome. Case 2 integrated the analysis, design, development, implementation, and evaluation (ADDIE) model, and course planning was structured along each of these design stages, whereas case 3 implemented an agile project management model (with iterations) while using the ADDIE model in course construction.

Participant Information

A total of 124 learners enrolled in the SPOC from September 2016 to December 2016 ( Table 7 ). Of these, 84% completed the course and received a postcourse certificate. The course uptake and completion, however, did not influence the production costs postcourse implementation as the course was designed as a self-managed SPOC not requiring further administration after deployment.

Electronic learning implementation participation summary .

A total of 968 learners participated in the MOOC from November 2017 to December 2017 ( Table 7 ). Of these, 17% completed the course. The course completion ratio was in line with completion rates for MOOCs [ 33 ], where although there is a high uptake of initial learners, completion of course activity ranges from 8% to 20%.

A total of 5036 learners participated in the MOOC from September 2018 to December 2018 ( Table 7 ). Of these, 12% completed the course. The course completion ratio was also in line with completion rates for MOOCs [ 33 ]. A blended residential course was held in November 2018, with the participation of 14 learners (these learners were inclusive in the MOOC set). In this residential course, the participants completed the MOOC as prelearning and then undertook case studies, putting course learning into practice.

Principal Findings

This study aimed to establish an approach for identifying the costs in the design, development, and deployment of applied health professions e-Learning courses. The standard components for the construction of an e-Learning course were determined by the methods used in this study, which combined existing approaches for cost budgeting with qualitative methods for the interpretation of results. Although Levin’s ingredients method provides a mechanism for categorizing costs design and implementation costs for budgeting, TQM provides a qualitative framework to examine the effect of the design and production decisions on the budget. The key issues affecting the ability of the budget to deliver in line with expectations at the close of the project were related to process issues. Familiarization with technology was also a key issue in cases 1 and 2, where familiarity with production methods and learning technology had an effect on anticipated effort.

The key recommendations made from examination of these cases center on 3 areas of process-related enhancement, 1 having to do with project management and the remaining 2 having to do with budget management, both related to the course production and instructional design:

Project Management: Linkage of Instructional Design Method to Stages in the Project Lifecycle With Time Tracking

Project management enables the planning and prioritizing of activities; management of risk, issues, and actions; and ensuring quality. In these observed cases, the use of robust project management methods and the development of iterative methods to validate learning materials tended to create favorable results. In addition, linking an instructional design approach to project stages and tracking tasks by time to each component creates awareness and links the associated financial effect of delivery to course building.

Budget Planning: Use of Confidence Factors in Budget Time Estimating

A vital issue in all cases was overestimating the amount of effort required to build tasks. To better manage time tracking, we have suggested tracking task by time linked to learning design, but as an additional measure, building confidence factors into budgets allows a degree of error and contingency when building initial budgets. A confidence factor is a percentage of variance added to an initial cost forecast that can be added as a contingency; applying confidence factors based on requirements, the familiarity of approach, and other factors can lead to higher estimation precision.

Budget Planning: Modeling Budget Forecasting on Similar Implementations

Case 3 was the most successful in delivery because the course team had worked together delivering similar content, was able to gain efficiency in having preexisting relationships, and had an evidence base to build their cost models from. When planning e-Learning implementations, the starting point should similarly be previous projects or using data from the literature on factors influencing costs, so budgets are not determined from scratch. Part of the observed budget variance issues in cases 1 and 2 had to do with estimates for costs not built on prior evidence; this can be controlled by using an experience-driven starting point.

Strengths and Limitations

This study analyzes 3 distinct cases of e-Learning covering 6128 applied health learners in 3 years and provided a comprehensive summary of the issues affecting the production and development of a course. This information could be useful for course designers in the planning of their e-Learning implementations and for drawing on lessons learned to plan budgets that ensure projects meet their objectives.

We noted 4 limitations with this study. Case study research can only provide a snapshot of activities as observed in each case, and there is a possibility that these cases may have limited applicability to other contexts. This has been mitigated using construct validity, external validity, and reliability tests in each case, but it is important to note that case study research has an inherent limitation in the observation of events under consideration due to the design; experimental methods deliver more rigorous results to test results. In addition, the selection of the case studies was opportunistic, as they were e-Learning projects accessible within the first author’s research unit. The second limitation is that further qualitative investigation of attitudes, views, and perceptions of stakeholders was not undertaken. This would have added an additional dataset to analyze factors affecting budgeting, meaning that the researchers drew conclusions from data that may have been viewed differently with further direct inquiry from stakeholders. It is important to note however, that stakeholders did review final case reports for accuracy and consistency with events. The third limitation is that the study did not undertake critical examination of the decisions made by the course designers in authoring tools, license costs, expertise, and other factors affecting the direct costs; examination of these costs including triangulation among the 3 sources would lead to further evidence affecting results. Finally, the study made use of a mixed-methods approach to analyze horizontal budget analysis but did not undertake an analysis for offsetting or magnifying variances, return on investment, forecasting, sensitivity analysis, or other financial planning and analysis methods. An economic study focused on outcomes and cost could provide further data that would potentially influence implementation considerations.

Further Research

The outputs of this study, in addition to the process of execution and reflection on both strengths and limitations, suggest 3 possible areas for future research:

Standards for Costing Economic Evaluations of e-Learning Implementations

Limited economic evaluations are conducted on e-Learning, most likely because educators focus on content delivery and educational effect rather than creating cost evidence. This study has created an extension of existing costing methods and demonstrated how it can be applied to e-Learning, allowing future researchers to reuse this approach to create consistent costing data, which could be subsequently benchmarked. With a growing evidence base of e-Learning cost data, this could also promote further research into various forms of economic evaluation, to create possible business cases for future investment in e-Learning, should value be demonstrated.

Integration of Project Management, Instructional Design Methods, and Costing

This study observed benefits in the combination of project management methods and instructional design methods; further research investigating ways of adopting existing instructional design methods with project management methodologies and linking these methods with cost management approaches could help address the high investment cost required in e-Learning.

Cost and Value Perceptions of Students and Educators

Using improved cost data from the approaches in this research, further research could attempt to identify perceptions of cost and value by comparing the perspectives of students and educators.

e-Learning research consistently refers to the promise and opportunity of its cost-effectiveness in contrast to face-to-face instruction; however, the underlying data supporting the costs necessary for their delivery are not well understood [ 8 ]. To implement further economic evaluation to understand proprieties demonstrating the value of e-Learning in contrast to other learning types, it is first necessary to develop a standard means of calculating costs in the delivery of these types of projects. Through consistent management of factors affecting costs in course production, further research could be undertaken using standard economic evaluation methods to evaluate the advantages of using e-Learning. This study enables an understanding of the issues affecting cost planning for the design, development, and deployment of e-Learning courses and also provides recommendations on controlling cost variance within e-Learning projects. This study contributes a systematic approach to costing in e-Learning that course designers and researchers could use to design and calculate costs in the production and deployment.

Acknowledgments

This project was supported by the European Institute of Innovation and Technology—EIT Health Knowledge and Innovation Community and the Higher Education Funding Council for England—Catalyst Fund. Boris Serafimov and Mel Toumazos provided detailed data contributing to the study in the design, development, and deployment of the e-Learning courses examined in this study. Yusuf Ermak and Hassan Chaudhury provided valuable contributions to the Data Science Courses as lecturers.

Abbreviations

Multimedia appendix 1, multimedia appendix 2, multimedia appendix 3, multimedia appendix 4.

Authors' Contributions: EM conceived the study topic, wrote the first draft, responded to peer-review feedback, and is the principal investigator on the research project. DB, KF, and AA reviewed the completed draft manuscripts and provided feedback on iterations. JC supervised the investigation. EM is the guarantor.

Conflicts of Interest: None declared.

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