how businesses use sensitivity analysis
sensitivity analysis can provide management feedback useful in many different scenarios including because sensitivity analysis answers questions such as what if xyz happens this type of analysis is also called what if analysis examplea sales manager wants to understand the impact of customer traffic on total sales the company determines that sales are a function of price and transaction volume the price of a widget is 1 000 and the company sold 100 last year for a total sales of 100 000 the manager determines that a 10 increase in customer traffic increases transaction volume by 5 this allows the company to build a financial model and sensitivity analysis based on what if statements it can tell the manager what happens to sales if customer traffic increases by 10 50 or 100 based on 100 transactions a 10 50 or 100 increase in customer traffic equates to an increase in transactions by 5 25 or 50 respectively the sensitivity analysis demonstrates that sales are sensitive to changes in customer traffic advantages and disadvantagessensitivity analysis provides several benefits for decision makers it acts as an in depth study of all the variables so the predictions may be more reliable it allows decision makers to identify where they can make improvements in the future however the outcomes are based on assumptions because the variables are based on historical data only complex models may be system intensive and models with too many variables may distort a user s ability to analyze influential variables may help management target specific inputs to achieve more specific resultsmay easily communicate areas to focus on or greatest risks to controlmay identify mistakes in the original benchmarkgenerally reduces the uncertainty and unpredictability of a given undertakingheavily relies on assumptions that may not become true in the futuremay burden computer systems with complex intensive modelsmay become overly complicated which distorts an analysts ability tomay not accurately integrate independent variables as one variable may not accurately the impact of another variable