What is the anchor effect?
The anchor effect describes the human tendency to rely heavily on the first information received — the “anchor” — when making decisions. This anchor serves as a reference point that influences subsequent assessments, even if it is irrelevant or arbitrary.
The psychological mechanism
The anchor effect is based on cognitive abbreviations (heuristics), which simplify decision-making processes. However, under time pressure or high cognitive load, these abbreviations can lead to errors. Adjustments from the original anchor are often inadequate, meaning that decisions remain within a fixed framework.
The anchor effect in demand forecasting
In demand forecasting, the anchoring effect is reflected in various ways:
- Excessive reliance on historical data: Forecasters stick to previous sales trends even as market conditions have drastically changed.
- Influence of early estimates: Initial sales forecasts set expectations and influence subsequent adjustments and final decisions.
- Supplier negotiations: Initial price or quantity offers from suppliers can act as anchors and unconsciously influence cost or inventory strategies.
Practical example
A retailer based its inventory planning for the holidays on previous years' sales figures and ignored new consumption trends and competitors. The result: He stored too many outdated products, which led to high discounts and unnecessary storage costs.
Why does the anchor effect persist?
The anchor effect persists because it helps to reduce complexity. In situations with uncertain or incomplete data, the anchor appears as a logical starting point. But this dependency can blind decision makers to new, contradictory information.
Key statistics
- A 2019 study found that the anchor effect increased forecast accuracy in 73% of tested scenarios impaired — even in data-driven environments.
- AI systems with bias-aware algorithms reduced the impact of the anchor effect by up to 7%, which underscores the role of technology in reducing cognitive biases.
Overcoming the anchor effect with AI-supported demand forecasting
The anchor effect is particularly common in traditional forecasting methods, as static data or outdated assumptions distort decisions. Our AI-based solution for demand forecasting starts here and enables strategic, data-based decisions without distortions.
Precision beyond human capabilities
Traditional methods rely heavily on historical patterns and anchor decisions in outdated trends. AI, on the other hand, analyses huge amounts of historical and real-time data, recognizes subtle patterns and complex relationships that human analyses could overlook. As a result, the influence of irrelevant anchors is reduced and forecasts reflect the current market situation.
Dynamic adaptability
Markets are volatile, and static anchors cannot account for sudden changes. AI-powered solutions are constantly adapting by integrating new data in real time and refining forecasts accordingly. This allows companies to remain agile and react to unexpected events — such as global supply chain disruptions.
Holistic data integration
AI seamlessly combines internal and external data sets, including market trends, economic indicators, and seasonal fluctuations. Through this comprehensive market view, AI solutions minimize the risk of the anchor effect by broadening the decision-making context.
Conclusion
Demand forecasting is both an art and a science: forecasters must deal with uncertainties and make well-founded decisions at the same time. The anchor effect shows how strongly first impressions influence the decision-making process — but also why modern tools are needed to question assumptions.
AI-based demand forecasting offers a solution by freeing companies from irrelevant anchors and enabling data-driven, strategic decisions. By using new technologies and consciously managing cognitive biases, companies can develop more accurate and robust forecasting processes — and thus lay the foundation for sustainable growth and success.
Would you like to improve your demand forecasts?
Learn how our AI-powered solution increases your forecast accuracy, reduces bias, and enables better decisions:
https://www.pacemaker.ai/en/demand-forecasting