How To Best Remove Forecast Bias From A Forecasting Process

Executive Summary

  • Removing forecast bias is a politically complicated endeavor.
  • We cover how to do it.


Forecast bias is distinct from forecast error and is one of the most important keys to improving forecast accuracy. To understand what it is, see the article How to Understand Forecast Bias.

In this article, we will address how to address forecast bias once it has been identified.

Our References for This Article

If you want to see our references for this article and other Brightwork related articles, see this link.

The Political Implications of Pointing Out Forecast Bias

Confronting forecast bias means risking yourself politically because many people in the organization want to continue to work their financial bias into the forecast. And they do not like being told they can’t. In fact, they will bristle at the idea that they have any financial bias and will typically point fingers back at the person who points out they have a financial bias. And if you prove that their forecast was biased with all the numbers, they will often still say it wasn’t by coming up with an excuse for why “something changed” and that this was why their forecast was off. This extends beyond forecasting as people generally think they are far more objective than they are. It is difficult for even salespeople that they may have some bias in presenting their products versus a competitor’s products. Typically a person who is 100% biased will make a statement like the following.

Ok, I admit I might be a little bit biased.

A big part of being biased is to try to diminish the concept of bias altogether. And I have witnessed numerous occasions where the person will try to disarm me by stating that the bias that is negative forecast is “no big deal.” And one powerful way of doing this is to make fun of the very concept of bias. And say..

Look, everyone has a bias.

In this case, a person with a financial bias will try to conflate a preference, with a financial bias. That is if a person likes a certain type of movie, they can be said to be “biased.” When this is described as a preference.

This is a deliberate act of deception, and this muddies the water as the most powerful biases that impact forecasting are financial biases (a sales quota, a desire to make marketing look good by proposing a new product will be wildly successful), not personal preferences. I am not proposing that one can’t have preferences. I am explaining that removing biases from forecasts improves their accuracy. We can both remove forecast bias from forecasts, and continue to have movie preferences, and root for our favorite sports team. These two things don’t have much to do with each other.

Those that are finally cornered on a financial bias will often say something like.

That guy was rude (the person pointing out the financial bias), what an as*****!

Politeness often seems to end up being not pointing out financial bias and allowing the financially biased individual to continue to misinform others that they are as objective or nearly as objective as anyone else.

Keeping the Presence of Objectivity Alive

Part of submitting biased forecasts is pretending that they are not biased. Companies are not environments where “truths” are brought forward and the person with the truth on their side wins. People are considering their careers, and try to bring up issues only when they think they can win those debates. Sales and marketing, where most of the forecasting bias resides, are powerful entities, and they will push back politically when challenged. And these are also to departments where the employees are specifically selected for the willingness and effectiveness in departing from reality. Each wants to submit biased forecasts, and then let the implications be someone else’s problem. This is covered in more detail in the article Managing the Politics of Forecast Bias.

How to Remove Bias?

The easiest way to remove bias is to remove the institutional incentives for bias.

Yet, few companies actually are interested in confronting the incentives they create for forecast bias. As we cover in the article How to Keep Forecast Bias Secret, many entities (companies, government bodies, universities) want to continue their forecast bias. For some, having a forecast bias is an essential part of their business model.

For those interested in removing forecast bias, software designed to mitigate forecast bias can help highlight bias and provide mechanisms to adjust it within the application. Within the application, there should be the ability to identify bias and adjust bias quickly and easily.

How We Manage Forecast Bias Removal

We measure bias on all of our forecasting projects. Measuring bias can bring significant benefits because it allows the company to adjust the forecast bias and improve forecast accuracy.

  • The most significant bias by far tends to come from judgment methods. Within this category, sales forecasting tends to have the highest bias, as we cover in the article A Frank Analysis of Deliberate Sales Forecast Bias.
  • A primary reason for this is that sales want to ensure product availability, and sales are not measured by inventory turns on inventory investment.
  • Some companies are unwilling to address their sales forecast bias. Still, another primary reason for their trepidation is they have never actually measured their forecast bias from all the forecast inputs.

Some companies are unwilling to address their sales forecast bias for political reasons. But a significant reason for their trepidation is they have never actually measured their forecast bias from all the forecast inputs. Therefore without the actual data, they are less willing to confront entities within their company, damaging forecast accuracy.

How Common are Requests for Bias Removal from Forecasts by Companies?

We have never received a request to reduce forecast bias. Companies, by and large, do not ask for or discuss bias removal. They want forecast accuracy improvement but are generally blind to the topic of bias. Within any company or any entity, large numbers of people contribute information to various planning processes that have an easily measurable bias, and they do not appreciate having it pointed out.

And outside of judgment forecasting software, software companies do not develop bias identification in their software (and do not build bias identification as a central component of the user interface). Bias identification is essential enough to have its dashboard, or view, within all demand planning applications. Not only for general ease-of-use but because adjusting for bias is about more than identification and adjustment. It is also about making the case.

Many people benefit from providing forecast bias. And if there is no cost to them, they will continue to provide a forecast with bias. For example, marketing is going to overstate their new product forecast because it makes them look like they are adding more value than they are to the company.

The Importance of Exposing Forecast Bias

  • The case for bias can best be made in a presentation format to demonstrate to others that the bias exists, and the action should be taken to minimize its effect on the final forecast.
  • When a bias is demonstrated in this way, it’s more difficult to dispute. However, the challenges in attempting to remove bias cannot be underestimated, even after the bias is pointed out.

If conversations in bias are kept at a high level and not demonstrated with a visual aid, which shows the bias clearly, all types of excuses will be offered by the groups that produced the biased forecast as to why there was, in fact, no bias. The application’s bias dashboard should support that presentation by showing bias from many products and different vantage points in real-time. Many criteria, including can identify bias

  1. Bias by individuals
  2. Bias by an overall department
  3. Bias by-products and geography, etc.

Bias information must be detailed because those with a biased forecast will most often push back by saying there was a good reason for the forecast at the time. However, the reasons provided don’t change a bad or biased forecast. Anyone can come up with an excuse as to why something they predicted did not occur.

Comparative Forecast Error Measurement

To determine the bias of a forecast, one must have the ability to measure comparative forecast accuracy efficiently. As we cover in the article Forecast Error Myth #5: Non-Comparative Forecast Error Measurement is Helpful, there is a strong myth that one does not need to perform comparative forecast error. And related to this myth is a second myth that forecast error is effectively measured in forecasting applications, as we cover in the article Forecast Error Myth #4: Most Forecast Error Measurement Supports Identifying How to Improve Forecast Accuracy.

As companies tend to lack an automated way of performing comparative forecast error measurement, there is often little understanding of how much other forecast methods (or manual overrides, marketing adjustments, etc.) improve or degrade the forecast error.

Reducing Forecasting Inputs From Biased Forecasters

This provides a quantitative and less political way of lowering input from lower-quality sources. It also promotes less participation from weak forecasters, as they can see that their input has less impact on the forecast. These performance dashboards exist in a few vendors, but forecasting accuracy could be significantly improved if they were universal.

In all forms of forecasting, an easy way to compare the performance of forecasters is a necessity. Forecast inputs must be tracked and reviewed, and adjustments must eventually be made because there are vast quality differences between forecasters. These types of dashboards should be considered a best practice in forecasting software design.

The consensus-based vendors, Inkling Markets, Consensus Point, and Right90, have the most significant focus on bias removal that I have seen. Why the statistical vendors lag in this area is an interesting question. In my view, it can be rationally explained by the fact that judgment methods are known to have more bias than statistical methods.

Using Bias Removal as a Forecast Improvement Strategy

It’s tough to find a company that is satisfied with its forecast. I have yet to consult with a company with a forecast accuracy anywhere close to the level that it really could be.

Everything from the use of promotions to the incentives they have set up internally to poorly selected or configured forecasting applications stand in the way of accurate forecasts. I often arrive at companies and deliver the bad news about how their forecast systems are mismanaged. I am sometimes asked by a director, who is worn out by funding continuous improvement initiatives for forecasting.

“But why have our results not improved.”

My answer is often that they are merely violating the rules established in scholarly sources for forecast management, and therefore they have poor outcomes. However, it is also rare to find a company that has a well-thought-out plan for improving its forecast accuracy.

Focusing on the Wrong Areas for Bias Removal

When I listen to executives’ plans to improve their forecast, they almost always focus on the wrong areas and miss out on some of the most straightforward ways to obtain forecast improvement. The new software is usually seen as a magic bullet but can only be part of the solution.

One of the simplest (although not the easiest) ways of improving the forecast—removing the bias—is right under almost every company’s nose. Still, they often have little interest in exploring this option.


Removing forecast bias is tricky. In the vast majority of cases, institutions cannot address forecast bias without bringing in outside help so that the foreign entity can “bear the responsibility” of implementing a new forecasting process that addresses bias. In effect, neither companies nor individuals producing biased forecasts ever appear willing to admit they have biased forecasts.