In this fast-paced business world, you opt for everything possible to stay ahead of your opponents. One of the major things you can do in this regard is demand forecasting. It helps you predict the future sales of your products and customers’ demand. Based. on these predictions, you can plan to deal with upcoming requirements. It will help manage orders and supply chains as well.
However, not everything can proceed the way you want. These predictions are based on assumption. So there are always chances of cognitive bias in demand forecasting. Today we will give you an idea about cognitive biases and then guide you on how to eliminate them. Without further ado, let’s move ahead.
Cognitive biases are the deviations from your judgements or predictions. These are often referred to as systematic errors in demand forecasting. These are deviations from rational judgments that can lead to a distorted interpretation of the information. As a result, things will not go as you have predicted. This can lead to numerous problems for you.
The following are the top types of cognitive biases that can impact demand forecasting.
This bias is mainly due to relying on information that confirms already existing expectations or beliefs. At the same time, it involves denying the downfall data. In simple words, you can say that confirmation bias demand forecasting is based only on the data that support your assumptions and leads to very optimistic predictions.
This bias results when during forecasting, you rely only on previous data present. Anchoring to initial information only can lead to deviations. Anchoring bias can lead to predictions or forecasting based on historical data only. It will be full of deviations if the market has observed some changes over the past few years.
This bias is a result of being overconfident on your predictions. It results when you believe your predictions will be accurate. As a result of this bias, you will have very narrow predictions and fail to deal with a variety of outcomes.
This bias results when you rely only on the recent data. Forecasters don’t pay attention to historical data and make assumptions or predictions on recent data. As a result, demand forecasting will not include some deep trends and will collapse if the trends are followed in the market. You will not have a comprehensive idea of the market as well.
Mitigating Cognitive Bias in Demand Forecasting
Only knowing the cognitive bias and its types is not going to help you a lot. The main thing is that you need to identify and address them. Once you know what biases are impacting your demand forecasting, the next thing you have to do is remove these biases. There are numerous ways you can mitigate these biases. The following are some top-listed and highly effective ways to avoid these biases.
The first thing you can do in this regard is to opt for diverse data sources. Never stick to a single source or two only. Get data from as many resources as possible to ensure you have deeper insights into market trends. Confirmation and Recency biases can be addressed by using this approach.
You can opt for collaborative forecasting to eliminate biases. This method provides you with diverse predictions. Numerous forecasters and other influential people in your firm will collaborate to provide you with collaborative forecasting. It enables you to think in several ways and eliminate biases that may rise due to personal perspectives.
To avoid uncertainties and biases, you can plan scenarios. Consider different scenarios based on the data and make the assumptions accordingly. Then you have to plan how to deal with different scenarios.
Individual biases can hugely impact forecasting. However, by opting for statistical methods, you can undo this influence by opting for statistical models. It helps you objectively analyze data.
Demand forecasting plays an important role in the appropriate functioning of the order management system. Therefore, it needed to be done perfectly without any deviations. To do so, you need to avoid biases. The aforementioned methods can help mitigate cognitive biases.