Forecast bias is caused by errors in statistical predictions. The symptom is regularly over or under-forecasting results. Over-forecasting is predicting sales of more products than actually sell and under-forecasting is when actual sales fall short of the prediction.
There are several things at play in any company that may result in forecast bias. A shortage of data to use while making decisions is a core issue. For instance, focusing only on the most recent data to create a forecast.
A sales team may feel highly optimistic when creating the forecast only to meet with a much lower real-life outcome.
Local and global events tend to sway outlook and optimism. It’s only human to lean toward giving greater importance to what is weighing on our minds. The downfall of this type of reasoning is the issue of overreaction driven solely by current events and losing the big picture.
A well-meaning boss may offer bonuses for exceeding the forecasts. In reaction, employees may delay or conceal some deals to pad their next quarter’s numbers. Which throws off future forecasts and the Mean Absolute Percentage Error (MAPE).
The Cause of Forecast Bias
The overarching cause of forecast bias is the use of incorrect predictions based on flawed logic. Sticking with demonstrable facts and collaborating with colleagues for the common goal of increasing sales and revenue can block bogus bias.
When it comes to Forecast Bias there’s a little information about MAPE that is important to understand. MAPE is a statistical forecasting method that measures how accurate a prediction (forecast) is. The MAPE is used as part of a regression analysis which mathematically sorts how independent variables are forecast to impact a dependent variable.
MAPE is often used to look at an executive’s forecast and predict the likelihood of error.
That information filters down to the forecast bias which is only a problem if the bias is regularly showing up in actual outcomes. The key is to adjust the forecast based on real-world input along the way and keep your main goals of maintaining a robust supply chain and delivering wanted goods to the market for your company’s success.
There are four steps to maximize your forecast value add (FVA) beyond the MAPE tool.
When a bias is identified it’s a fairly simple task to improve the forecast’s accuracy. Business leaders should not dread revealing a bias, it’s something you want to always be on the lookout for because the correction can be made quickly by tweaking the existing forecast numbers. In the case of under-forecast bias, you’d raise the numbers with an eye on what’s happening in your real-world marketplace. Conversely, decrease the forecast numbers if it’s an over-forecast bias that comes to light.
Using these insights to measure and calculate forecast bias means ensuring there’s enough of your product or service on the market at the time it’s in demand. Which spells better serving your customers while earning a higher profit for your company.
You don’t have to go all the way back to the drawing board as long as you ensure you have a measurable objective and consider all relevant data.