Sensitivity analysis tests how your result changes when one assumption changes. You take a number you are unsure about, such as price, sales volume, or material cost, move it up and down by a set amount, and recalculate. The output shows which assumptions your plan really depends on.
Banks and investors ask for this because every forecast is wrong. They want to see that you know where yours breaks.
An example. Your base case is 1,000 units at €50, a variable cost of €30, and fixed costs of €15,000 per month. Contribution is €20,000 and profit is €5,000. Now change one input at a time by 10 percent.
Price down to €45: contribution per unit falls to €15, total contribution €15,000, profit €0. Volume down to 900 units: contribution €18,000, profit €3,000. Variable cost up to €33: contribution €17,000, profit €2,000.
Price is by far the most sensitive input here. A 10 percent discount removes your entire profit, while a 10 percent sales miss costs you 40 percent of it. That result should change how you run the business: defend your price harder than your volume.
Two mistakes. First, founders change several inputs at once and then cannot tell which one caused the swing. Move one at a time, and build a combined worst case separately.
Second, they use symmetric ranges out of habit. If a supplier contract fixes your material cost for two years while your price faces new competitors, test price at minus 25 percent and material cost at plus 5 percent. Match the range to the real uncertainty.
