Let's look at a simple example of how the plan vs. actual analysis works.
For the record, in accounting and financial analysis, they call the difference between plan and actual variance. It's a good word to know. Furthermore, you can have positive or negative variance, as in good variance and bad variance.
It comes out as a positive number.
If you sell more than planned, that's good. If profits are higher than planned, that's good too. So for sales and profits, variance is actual results less planned results (subtract plan from actual).
For costs and expenses, spending less than planned is good, so positive variance is when the actual amount is less than the planned amount. To calculate subtract actual costs (or expenses) from planned costs.
The opposite. When sales or profits are less than plan, that's bad. You calculate variance on sales and profits by subtracting plan from actual.
When costs or expenses are more than planned, that's also bad. Once again, you subtract actual results from the planned results.
I'd like to show you that with a simple example. Let's start with a beginning sales plan, then look at variance, and explore what it means. This is a simple sales forecast table — a portion, showing just three months — from a standard sales forecast.
Beginning Sales Plan
To set the scene, this illustration shows three months of the sales forecast as the business plan is finished.
Actual Results for Sales
The next illustration shows the actual results for the same company during the same three months as in the previous illustration.
The illustration shows the sales variance for the same months. The red numbers represent the negative variances, which are sales amounts less than you forecasted.
What Matters is the Management
Notice in this example that the variance analysis should be just the beginning of a discussion, not an end result. Take the case of the sales of new bicycles.
This business ended up selling fewer units than planned (31 instead of 36), which seems like a disappointment. But on the other hand, the units they sold had a higher price than planned ($615 instead of $500). So sales in dollars were $1,053 higher than planned ($19,053 instead of $18,000).
That's where the management begins. The variance leads to questions about pricing, marketing, promotions, and future projections. Do they worry about selling fewer units, at higher prices? Do they take that as a marketing or seasonal trend? Or a successful change of product variety to favor the higher-priced bikes?
What they do next is the question. Variance analysis helps frame the questions they should be asking. And provides insight to successfully answer or at least test possible solutions.
So the calculations are simple enough. You calculate the variance in sales by subtracting the planned amount from the actual amount, which gives us the table shown below.
I use the classic accountant's red to indicate negative numbers, as in the phrase "in the red." The negatives are also in parentheses. For those cases, the actual sales were lower than planned. Positive numbers here mean actual sales were higher than planned.
You probably see some obvious conclusions. These are just numbers, but they are also indicating areas for more management.
The negative results for unit sales of systems are well below plan. And the per-unit revenue is down too.
Although units of service are disappointing, the price per unit was up, so sales were above plan.
There were pleasant surprises as well for software and training.
Given what's happened with the sales results, the plan-as-you-go planning process indicates in this example that systems sales are going badly, but there are other sales that can make up the problem.
Do you change the plan? That's where the management comes in. Get the people together and talk about it. Why are systems sales so much less than plan? Were the assumptions wrong? Was the plan too optimistic? Has something happened -- new competition, for example, or new technology, or something else -- to change the situation as it was planned.
What about the people? Here's where you have to manage expectations and follow up. Do you have metrics on sales presentations, leads, close rates? Have the people been performing, but just not getting the sales? Was your pipeline assumption wrong?
For this example, let's say we decide to adjust the sales forecast to absorb some changed assumptions. The next illustration shows the new sales forecast, after adjustments.
The illustration shows the revised plan in the April and May columns, even before they happen, to reflect the changes shown in the January-March period. Why would we work with an obsolete plan when the situation has changed.
Does this blow the plan vs. actual comparisons for future months? Not if you make the changes correctly, with everybody on the team being aware of them. You just keep moving your plan forward in time, revising for future months.
In the end, it's not a game. So what if you change the scoring in the middle. The point is managing the company better. Since the company knew systems sales would be down, it has planned on it and made a revised forecast in the actuals area. The same revision affects projected profits, balance sheet, and — most important — cash.
The next illustration shows the actual results recorded in that portion of the profit and loss, after the end of March. The actual results mean little without comparison with the original profit and loss table, shown previously. Unfortunately, many businesses also forget to compare the original plan to the actual results. Especially if business is going well -- the operation shows a profit, and cash flow is satisfactory -- comparisons with the original budget are made poorly or not at all.
The table shows actual results. Note how actual sales, costs, and expenses are different from the planned numbers. This is a portion of the full table.
So here's the significant view, the variance. Sales are below plan, but costs are also below plan, and let's stop there and make a point. You can see in the illustration how sales are negative and costs are positive. If you weren't careful, you could interpret that as sales are down and costs up, which would be a disaster. But variance analysis is fairly specific, defined by accountants and financial analysts, so the positive in the direct costs lines means less costs, not more. That can be tricky.
You can check on that by looking at the gross margin. The gross margin is disappointing, below plan, but not horribly so. It seems like the cost controls helped soften the blow of lower sales.
Then you start looking at the expense rows, and there are several interesting surprises.
This is where we go from the accounting details, the actual calculation, to the human details. What happened here? What should be changed? Does the plan need revision? Have assumptions changed. How have the people performed?
So we've seen some simple examples, in sample financial statements, of how things can go differently than planned. The real management here isn't just the calculations, but rather the management of the differences.
You have to look beyond the numbers, talk to the people, bring these things up in the meetings so the plan stays alive as planning, and becomes management. It isn't always obvious.
Many businesses, especially the small, entrepreneurial kind, ignore or forget the other half of the budgeting. Budgets are too often proposed, discussed, accepted, and forgotten. Variance analysis looks after the fact at what caused a difference between plan and actual numbers. Good management looks at what that difference means to the business.
Variance analysis ranges from simple and straightforward to sophisticated and complex. Some cost-accounting systems separate variances into many types and categories. Sometimes a single result can be broken down into many different variances, both positive and negative.
The most sophisticated systems separate unit and price factors on materials, hours worked, cost-per-hour on direct labor, and fixed and variable overhead variances. Though difficult, this kind of analysis can be invaluable in a complex business.
Look for Specifics. Talk to the People
This presentation of variances shows how important good analysis is. In theory, the positive variances are good news because they mean spending was less than budgeted. The negative variance means spending was more than the budget.
Continuing with our example, the $5,000 positive variance in advertising in January means $5,000 less than planned was spent, and the $7,000 positive variance in literature (meaning collaterals, such as brochures, sales pamphlets and folders) in February means $7,000 less than planned was spent. The negative variance for advertising in February and March and the negative variance for literature in March show that more was spent than was planned for those items.
Evaluating these variances takes thought. Positive variances aren't always good news. For example:
The postive variance of $5,000 in advertising means that money wasn't spent, but it also means that advertising wasn't placed. Systems sales were way below expectations for this same period -- could the advertising missed in January be a possible cause?
For literature, the positive $7,000 in February may be evidence of a missed deadline for literature that wasn't actually completed until March. If so, at least it appears that the costs on completion were $6,401, a bit less than the $7,000 planned.
Among the larger single variances for an expense item in a month shown in the illustration was the positive $7,000 variance for the new literature expenses in February. Is this good news or bad news? Every variance should stimulate questions.
Why did one project cost more or less than planned?
Were objectives met?
Does a positive variance reflect a cost saving or a failure to implement?
Does a negative variance reflect a change in plans, a management failure, or an unrealistic budget?
A variance table can provide management with significant information. Without this data, some of these important questions might go unasked.
More on Variance
Variance analysis on sales can be very complex. There can be significant differences between projected and actual sales because of different unit volumes, or because of different average prices. In the sales variance example in this chapter, the units variance shows that the sales of systems were disappointing. In the expenses variance, however, we can see that advertising and mailing costs were below plan. Could there be a correlation between the saved expenses in mailing, and the lower-than-planned sales? Yes, of course there could.
The mailing cost was much less than planned, but as a result the planned sales never came. The positive expense variance is thus not good for the company. Sales and Marketing expenses were also above plan in March, causing another negative variance.
The sales forecast variance table, shown earlier, which compares units variance and sales variance, yields no surprises. The lower-than-expected unit sales also had lower-than-expected sales values. Compare that with Service, in which lower units yielded higher sales (indicating much higher prices than planned). Is this an indication of a new profit opportunity or a new trend? This clearly depends on the specifics of your business.
It is often hard to tell what caused differences in costs. If spending schedules aren't met, variance might be caused simply by lower unit volume. Management probably wants to know the results per unit, and the actual price, and the detailed feedback on the marketing programs.
The quality of a business plan is measured not by the quality of its ideas, analysis, or presentation, but only by the implementation it causes. It is true, of course, that some business plans are developed only as selling documents to generate financial resources. For these plans, their worth is measured by their effectiveness in selling a business opportunity to a prospective investor. For plans created to help run a business, their worth is measured by how much they help run a business — in other words, their implementation.
Variance analysis is vital to good management. You have to track and follow up on budgets, mainly through variance analysis, or the budgets will be useless.
Although variance analysis can be very complex, the main guide is common sense. In general, going under budget is a positive variance, and over budget is a negative variance. But the real test of management should be whether or not the result was good for business.