How To Calculate Forecast Bias and Why It’s Important

1. BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units.
2. If the forecast is greater than actual demand than the bias is positive (indicates over-forecast). …
3. On an aggregate level, per group or category, the +/- are netted out revealing the overall bias.

Forecast Bias

Why is calculating forecast bias important?

Beyond improving the accuracy of predictions, calculating a forecast bias may help identify the inputs causing a bias. Other reasons to motivate you to calculate a forecast bias include:

Meeting the demand of your consumers

Calculating forecasts may help you better serve customers. Accurately predicting demand can help ensure that theres enough of the product or service available for interested consumers. Forecasting can also help determine the regions where theres high demand so those consumers can purchase the product or service from a retailer near them.

Planning for the growth of the company

A business forecast can help dictate the future state of the business, including its customer base, market and financials. The ability to predict revenue accurately can lead to creating efficient budgets for production, marketing and business operations. Its also helpful to calculate and eliminate forecast bias so that the business can make plans to expand. These plans may include hiring initiatives, physical expansion, creating new products or services or marketing to a larger customer base.

Creating and accomplishing business goals

Reducing the risk of a forecast can allow managers to establish realistic goals for their teams. For example, if the forecast shows growth in the companys customer base, the marketing team can set a goal to increase sales and customer engagement. This can improve profits and bring in new customers. With an accurate forecast, teams can also create detailed plans to accomplish their goals.

Improving work environment for employees

Calculating and adjusting a forecast bias can create a more positive work environment. Being prepared for the future because of a forecast can reduce stress and provide more structure for employees to work. When the company can predict consumer demand and business growth, management can ensure that there are enough employees to work towards these goals. This may lead to higher employee satisfaction and productivity.

What is a forecast bias?

A forecast bias is an instance of flawed logic that makes predictions inaccurate. Because of these tendencies, forecasts can be regularly under or over the actual outcomes. This creates risks of being unprepared and unable to meet market demands.

There are several causes for forecast biases, including insufficient data and human error and bias. An example of insufficient data is when a team uses only recent data to make their forecast. Its helpful to perform research and use historical market data to create an accurate prediction. Human error can come from being optimistic or pessimistic and letting these feeling influence their predictions. For example, a marketing team may be too confident in a proposed strategys success and over-estimate the sales the product makes.

How to calculate forecast bias

Here are five steps to follow when creating forecasts and calculating bias:

1. Determine the objective of the forecast

Before forecasting sales, revenue or any growth of a business, its helpful to create an objective. This is a business goal that helps determine the path or direction of the companys operations. By establishing your objectives, you can focus on the datasets you need for your forecast. An example of an objective for forecasting is determining the number of customer acquisitions that the marketing campaign may earn.

2. Collect forecast data

Next, gather all the relevant data for your calculations. This can include customer orders, timeframes, customer profiles, sales channel data and even previous forecasts. Its important to be thorough so that you have enough inputs to make accurate predictions.

3. Analyze data

Study the collected datasets to identify patterns and predict how these patterns may continue. You can automate some of the tasks of forecasting by using forecasting software programs. Technology can reduce error and sometimes create a forecast more quickly than a team of employees.

4. Gather result data

After creating your forecast from the analyzed data, track the results. For example, if you made a forecast for a 10% increase in customers within the next quarter, determine how many customers you actually added by the end of that period. This data is an integral piece of calculating forecast biases.

5. Use formulas to detect potential bias

Once you have your forecast and results data, you can use a formula to calculate any forecast biases. There are different formulas you can use depending on whether you want a numerical value of the bias or a percentage.

You can determine the numerical value of a bias with this formula:

Forecast bias = forecast – actual result

Here, bias is the difference between what you forecast and the actual result. When the bias is a positive number, this means the prediction was over-forecasting, while a negative number suggests under forecasting. If the result is zero, then no bias is present.

The formula for finding a percentage is:

Forecast bias = forecast / actual result

The forecast value divided by the actual result provides a percentage of the forecast bias. The closer to 100%, the less bias is present.

Examples of calculating forecast bias

Here are examples of how to calculate a forecast bias with each formula:

Calculating a numerical value

The marketing team at Stevies Stamps forecasts stamp sales to be 205 for the month. At the end of the month, they gather data of actual sales and find the sales for stamps are 225.

Forecast bias = 205 – 225

Forecast bias = -20

Since the forecast bias is negative, the marketers can determine that they under forecast the sales for that month. To improve future forecasts, its helpful to identify why they under-estimated sales. This can ensure that the company can meet demand in the coming months.

Calculating a percentage value

If the marketing team at Stevies Stamps wants to determine the forecast bias percentage, they input their forecast and sales data into the percentage formula.

Forecast bias = 205 / 225

Forecast bias = 0.91, or 91%

FAQ

How do you calculate bias and MAPE?

1. Add all the absolute errors across all items, call this A.
2. Add all the actual (or forecast) quantities across all items, call this B.
3. Divide A by B.
4. MAPE is the Sum of all Errors divided by the sum of Actual (or forecast)

What is bias in forecast accuracy?

How do you calculate forecast error?