Variance Analysis

Basically, this is the fancy business way to ask, "Why didn't things go as expected?" or “How on earth are we so far off plan?”

“Plan” is the key word. You made a plan, a.k.a. a budget. Others in the company rely on your planning and budgeting…from the hiring needs of the union factory workers this year...to the marketing spend for TV ads from that department...to the legal team who is fully plumbed up for the 11.3 lawsuits you expected you’d have to defend this year. But then real life happened, and things didn’t quite go as you expected.

What happened? To figure it out, you're going to use variance analysis. And yes, it’s a whole science unto itself.

You own Zesty Kitty, a leading provider of condiments for pet food. You launch a new sriracha-based sauce meant to pair perfectly with the frozen mice people feed to their pet snakes. It's called Spicy Serpent Surprise. And cats love it…as much as a cat can love anything. Based on market research and projections derived from other products you already sell, you expect sales of $300,000 in the first month. And you expect Contributed Profits from this product to be $125,000, giving you a contribution margin of about 42%. A month after launch, you look at the numbers. You brought in revenues of $400,000...better than expected. However, profits and margins fell short of projections. You only brought in $100,000 in contributed profits…a contribution margin of just 25%. Well shy of the budgeted expected 42 percent. So, uh...WTF?

You run some variance analysis. As it turns out, you had unexpected demand from Ireland. You were pretty sure you heard they didn't have snakes there. Oh, well...additional demand is usually a good surprise. But the extra demand meant you had to scramble to make enough Spicy Serpent Surprise to fill all the orders. To do this, you had to pay overtime to your workers in order to crank out the extra sauce.

Now that you know what happened, you can adjust. You know the extra demand is there, so you can hire some additional workers. These new employees will get regular pay, and the additional capacity means you won’t have to pay anybody overtime. Since you're not paying the extra labor costs associated with overtime, margins will return to the expected levels. Meanwhile, you adjust your revenue projections to the new levels. The added demand is still going to be there next month, so you revise your expectations. You make them higher. For the second month, you expect to repeat $400,000 in revenue. However, your labor adjustment should allow margins to get closer to the originally expected 42%. Which would give you profits of $168,000. Once the month is over, you'll look at the numbers and see if anything else doesn't match the new expectations. Then you’ll run the variance analysis process again.

It becomes part of an ongoing cycle...basically checking budgets and projections against real-life results. When the process is done, you’ll know whether your projections were just wrong and need to be adjusted...like with the unexpected Irish demand for your new mouse sauce...or you’ll find points in the production and distribution process where adjustments need to be made. You can implement changes here to get things back to what you predicted...like when you figured out how the overtime hurt your margins, and you brought in new workers to fix the unnecessarily high labor costs.

Now that your sriracha mouse sauce has taken off, you can start your R&D department working on its next big project...soy sauce-flavored insects for pet spiders.