Business Owners – Why Break-Even Points (BEP) are an effective tool for your Business. 

How can you plan ahead with managing one of the most important aspects of your business – paying your monthly expenses, if you don’t understand the amount of sales required to pay these expenses?

Further to this, you should create a BEP tool that is flexible enough that allows “what-if” results. What happens to my BEP if I forecast varying levels of sales or units produced, as an example. More on this later in the article.

BEP allows you to forecast those months when sales are expected to decline putting pressure on cash or when shortfalls are likely to occur, so you can plan ahead. If you’re forecasting cash shortfalls in the next 2-3 months, this is an opportunity to make adjustments or finance this (temporary) shortfall with a Line of Credit (for example).

This is the real value in having such a tool – to give you time to plan.

Introduction and the value of Break-even Points explained: 

Next to knowing the Gross Margins for your products and/or services, understanding the amount needed to cover the total amount of expenses, every month is key. Why? Because it fixes the amount of sales revenue required to cover all your expenses and the amount of activity needed by your organization or company to bring in and collect these sales.

One word. BEP’s are tools that use the level of sales activity (expressed in currency – USD or the number of units sold) to estimate those expenses that vary with sales, in other words, when sales increase/decrease, some expenses increase/decrease by a proportional amount. In this case, sales and these expenses are said to be correlated and sales is termed a “cost driver,” since sales is driving the amount of expense. The strength of this correlation is used to estimate the expense.

In addition, there are other cost drivers that may be better at predicting some expenses. Expenses that vary in some proportion with a cost driver are termed variable expenses.

Using these estimated expenses tells the user the amount of sales  required to pay these (monthly) expenses. A word of caution – Sales are not the same as cash, particularly if the company invoices and waits for payment.

Break-Even Points – How Calculated: 

Sales – Cost of Goods Sold (or Cost of Sales) – Total Expenses (those expenses below COGS or COS) = $0 = Break-Even Point (BEP).

So, algebraically, a company’s BEP is the point where Sales = COGS (or COS) + Total Expenses (below COGS or COS).

BTW, As mentioned, sales are not the same as cash collected, unless 100% of your sales revenue is determined through a Point of Sale. Services such as Auto-repair shops typically do not invoice their customers. They don’t relinquish control of the customer’s vehicle until they get paid.

Organizations that invoice their clients/customers have another step in their process – the amount of time required to collect payment, which needs to be factored in to determine the timing of cash in-flows compared to the amount needed to pay all expenses (usually, monthly) – which is a cash budget. BEP analysis can be used to create a cash budget but it is not a cash budget and not the focus of this article.

• So, BEP’s use sales and other cost drivers to estimate variable expenses.
• In contrast, fixed expenses, by definition do not vary with sales or other cost drivers.
• A cash budget takes the BEP concept one step further, by comparing the amount of cash likely to be collected by month vs. the amounts needed to pay all expenses and obligations in that same month.

Variable Expenses: 

As mentioned, variable expenses are those that change based on the amount of sales activity or other correlated cost driver. Sales activity can be expressed as the number of units sold (for product manufacturers) or as sales by currency (USD, CAD, etc).

Cost of Goods Sold (COGS) or Cost of Sales (COS) are the best examples of variable expenses that are highly correlated to sales activity. The key here is that:

• Historical data is used to create a mathematical relationship between sales (expressed in units or currency) and an amount of variable expenses, expected.

Determining the relationship between Sales Activity and Direct Variable Expenses (Cost of Goods Sold and Cost of Sales):  

If historical data suggests that Cost of Goods Sold (COGS) or Cost of Sales (COS) is fairly consistent at 45% of sales, then this means that the company has a Gross Margin of 55%. Percentages are very useful.

• Every sales dollar earned means that 45 cents is attributed to Cost of Goods Sold or Cost of Sales, and $1.00 in sales – .45 in COGS/COS = 55 cents of Gross Margin.
• This also works for sales/unit for product based businesses. If a product line in a business sells a product for $150/unit and details its Cost of Goods Sold (using a Bill of Material) at $51.75/unit, then it’s Gross Margin can be expressed as $98.25/unit or 65.5% as a percentage of sales.

More – 

The mathematics of this usually involve an equation that attempts to describe the strength of the relationship between sales and variable expenses.

So, as in the prior example, if Cost of Goods Sold is estimated to be 45 cents for every dollar of sales made, then $25,000 in forecasted sales in a given month, would equate to $11,250 in Cost of Goods Sold, expected. A simple linear relationship.

However, these mathematical models can get very involved – they can be non-linear, with 2nd or 3rd order (additional variables) that are included to better describe the relationship between how two variables behave – an independent variable (in this case, sales) and a dependent variable (variable expenses).

But this is not a math course. I’ll keep it focused and practical.

Other Variable Expenses:

Labor and electricity (utilities) are two other variable expenses that come to mind. But (hourly) labor costs are typically not based on sales dollars or sales volume, per se. Labor costs are based on hours worked. So, some expenses may have a different cost driver, that is, the expense may be determined more accurately using a different variable, not sales, but hours worked.

Units produced (not sold) is probably the best indicator of the amount of electricity consumed for a product-based company and the amount of expense that will be incurred, given that billing cycles usually mean that the expense incurred will be due in the future.

So, simplifying this, and to ensure the best possible accuracy of the model, a relationship needs to be determined for electricity and labor costs (using historical data) where these expenses can reasonably be predicted using cost drivers such as the number of units produced and hours worked.

Fixed Expenses: 

Fixed expenses, like rent, insurance, recurring office expenses, and subscriptions are the same no matter the amount of sales or production activity. Straightforward. These expenses are usually termed, Overhead because these expenses cannot be tied to any specific product or service – they apply to all products and services.

Overhead (OH) Defined: 

There is fixed (FOH) and variable overhead (VOH); examples of fixed OH expenses were just described.

Again, a good example of variable overhead expenses are utilities, specifically electricity. As mentioned, sales activity is not the best cost driver for estimating monthly utility expenses, like electricity, because electricity costs result from production activity, not sales.

Product-based Companies: 

To restate – Units produced is the best cost driver to estimate variable expenses like electricity for those processes that use equipment and machinery. Electricity bills are normally stated as the amount of kilowatt-hours (Kwh) consumed. Equipment and machinery operating parameters can be used to calculate the amount of (Kwh) consumed for every unit produced.

Other utility costs that are not highly correlated to production or sales activity, would best be treated as fixed overhead expenses (FOH).

Service-based Companies: 

So, expenses that are not dependent on sales or (production) activity should be treated as fixed OH.

So to keep this practical, it might be better to look at historical utility bills to see if there is a pattern in the data that suggests seasonality for those expenses (do these expenses vary by season).

Some expenses may be somewhat variable but the cost and effort of modeling these types of expenses make it impractical compared to the additional accuracy gained. In this case, estimate these expenses as a flat percentage of a specific cost driver or a fixed amount.

Spring or summer months usually have more sales activity for Construction Firms, but Auto-repair Shops normally see more activity during the winter.

Creating a Mathematical Model: 

The concept of a BEP is pretty simple, but the value of such a tool lies in its predictive accuracy, and this where complexity creeps in.

Samples of (historical) data are sometimes used to understand and quantify the strength of the relationship (the amount of correlation) between sales and other cost drivers to these variable expenses.

Regression analysis tools that are widely available, can be used to create this mathematical relationship. The key here is that more historical data may or may not be useful.

Organizational changes and seasonality can distort and complicate how sales and other cost drivers used to predict some expenses behave.

Keeping it Simple: 

The goal here is to get something that a firm, company or organization can use that is within a reasonable amount of error (the estimates created vs. actual values) such that the size of the error doesn’t invalidate the tool.

As mentioned, model complexity can be expensive. The idea here is to create something that has more predictive value (to make informed decisions) than the cost both in time and effort to develop it.

More about modeling Break-even Points:

To reinforce the prior point, for most mathematical models, accuracy increases as more historical data is added and as comparisons between predicted amounts and actual amounts are fed back into these models. These differences (predicted amounts vs. actuals) are used to make adjustments to improve the correlation between cost drivers and expenses.

In addition, as previously mentioned, models must evolve and change as the organization changes. Adding or replacing equipment to improve efficiency, expanding or downsizing an operation, or changing processes that significantly impact the amount of time needed to complete products or services are just some examples. Applying models that have been invalidated due to organizational changes yield bad decision criteria.

Scenario 1: ABX, Inc., a Small Manufacturer 

Facts – 

ABX, Inc. manufactures a subassembly (SA-1) used in commercial jet engines as a subcontractor. Demand has been spotty for a particular product-line so, ABX wants to create a flexible BE calculator/tool to understand what they can expect if demand varies during their next fiscal year.

• The unit sales price is $983.22/each
• ABX has a detailed Bill of Material (BOM) and the cost to produce and prepare each subassembly for delivery is: $775.59/each
• Its BOM (Cost of Goods Sold) varies depending on the amount of parts they purchase from their suppliers to build these sub-assemblies. It’s Average Inventory Cost (the method they use for their Inventory Costing) decreases 7% for orders of 150 and more
• Variable overhead (per unit) is $77.85. Based on machine-hours, 2.4 hours of machining is required per unit or $32.44 VOH cost per machine-hour.
• Fixed overhead (per unit) is $66.25

ABX wants to construct a flexible BEP analysis tool based on monthly production volumes of 100, 150, 200, and 250 units.

Creating the Tool and the Result – 

Since ABX has detailed cost processes and systems, applying percentages to the sales price and production volumes for this what-if analysis in an Excel sheet would provide the type of visibility they want.

Break-even Points – The Analysis: 

At production volumes of 100 units, by month – 

• Cost of Goods Sold = $77,559
• Variable overhead = $7,785
• Fixed overhead = $6,625

ABX’s BEP = $77,559 + 7,785 + 6,625 = $91,969.

At production volumes of 250 units, by month –

• Cost of Goods Sold = $775.59 less 7% based on a reduction in Average Inventory costs x 250 units = $180,325
• Variable overhead = $77.85 x 250 = $19,463
• Fixed overhead = $66.25 x 250 = $16,563

ABX’s BEP = $180,325 + 19,463 + 16,563 = $216,351.

This helps ABX to understand the amount of sales needed to just cover their SA-1 costs.

More – 

If ABX decides to buy additional equipment to decrease their machining time and increase the number of units they can complete and ship, they need to factor this in and redo their existing model.

Scenario 2: John’s Auto Repair, Inc.  

John’s Auto Repair, Inc. has been in business for 15 years. John, the S-Corporation Owner, wants to understand how much his business needs to generate in monthly sales to cover his slowest months – June, July and August.

John’s historical data has been used to model his expected costs. Based on history:

• June sales are estimated to be 15% lower than their average monthly sales revenue of $166,000
• July sales are approximately 12% lower
• August sales are 8% lower
• Cost of Goods Sold for parts and materials consume 36% of every sales dollar
• Technician wages are 24% of sales
• Variable overhead and other operating expenses are 16% of revenue
• Fixed overhead (monthly) is $13,268

June – 

• Sales revenue is predicted to be – $166,000 x 85% = $141,000 which is 15% lower than their average revenue based on history
• Cost of Goods Sold = $50,796
• Technician wages = $33,840
• Variable overhead + other operating expenses = $22,560
• Fixed overhead = $13,268

Based on this, John’s BEP:  $50,796 + 33,840 + 22,560 + 13,268 = $120,464. This is the amount of revenue needed to cover his June expenses. 

July – 

• Cost of Goods Sold = 36% x 88% x 166,000 = $52,589
• Technician wages = 24% x 88% x 166,000 = $35,059
• Variable overhead + other operating expenses = 16% x 88% x 166,000 = $23,373
• Fixed overhead = $13,268

Based on this, John’s BEP:  $52,589 + 35,059 + 23,373 + 13,268 = $124,289. This is amount of revenue needed to cover his July expenses. 

August – 

• Cost of Goods Sold = 36% x 92% x 166,000 = $54,979
• Technician wages = 24% x 92% x 166,000 = $36,653
• Variable overhead + other operating expenses = 16% x 92% x 166,000 = $24,435
• Fixed overhead = $13,268

Based on this, John’s BEP:  $54,979 + 36,653 + 24,435 + 13,268 = $129,335. This is the amount of revenue needed to cover his August expenses. 

Conclusion:

Some small business owners may look at this and consider it a stretch – asking,  “why should I plan my business around estimates?” The simple answer is that all planning, including documents that you submit to the IRS for your tax filing or those that you use to obtain a loan from a lending institution involve estimates. Here are just a few examples:

• Budgets involve estimates – Businesses small and large, use budgets
• Inventory costs – like Average Cost, FIFO and LIFO are based on estimates and are used in tax returns
• Tax plans involve estimates
• Business plans that you submit to a lending institution to get a business loan are based on estimates

They’re estimates but they need to be credible and hold up under scrutiny if they’re ever challenged. This is nothing new.

The value in knowing what your Break-even Points are is that it gives you visibility into what the future may hold and that gives you time to prepare.

Further to this, any credible business owner will always tell you that having additional time to manage critical aspects of a business plan is worth the effort.

Again, giving you time to plan is the value in creating a BEP tool.