Forecast Accuracy: Definition and Formula

Forecast accuracy, aptly named, is the accuracy of a company’s demand and sales forecasting. Helping maintain the correct levels of inventory and bettering stock control, accurately forecasting the demand of one’s consumer base is imperative for business operations to continue successfully. This article will, therefore, be exploring all aspects of forecast accuracy – how it’s measured, how it’s calculated, and why it is so important for the smooth functioning of a business.

What is Forecast Accuracy?

Forecast accuracy is the means of measuring how well a demand forecast has predicted actual outcomes or values of sales. Additionally, forecast accuracy ascertains the reliability and effectiveness of one’s forecasting models and techniques currently in-use. Determining one’s forecast accuracy provides more insight into a company’s market, allowing for more effective decision-making with regard to stock numbers and levels. The future benefit of ensuring that the right amount of inventory is available at the right time helps mitigate any issues that could cause major revenue losses such as over or understocking.

Forecast accuracy is also commonly used across various fields, such as sales forecasting, demand planning, and financial forecasting. In sales forecasting, accurate predictions help organisations estimate future sales volumes, optimise inventory levels, and streamline production processes. Like sales forecasting, demand planning places its focus on stock levels, ensuring the availability of products to meet customer demands without running into excess inventory or stock-out issues. Financial forecasting utilises forecast accuracy to make informed monetary decisions: budgeting, resource allocation, and investment strategies.

Read more on forecasting KPIs in our blog article.

Why do we Measure Forecast Accuracy?

By comparing the predicted values with the actual outcomes, forecast accuracy provides insights into the quality of the forecasts and the level of confidence that can be placed in them. By regularly assessing and improving forecast accuracy, businesses can enhance their planning processes, optimise resource allocation, and make more informed and strategic decisions.

1. Reduced Costs: Through avoiding excessive stockpiling or shortage situations, forecast accuracy allows for better monetary management and cost savings. With reliable forecasts and aligning their production and procurement activities with a more accurate expected demand, forecast accuracy helps companies minimise holding costs, obsolescence, and carrying costs associated with over and under-stockings, resulting in reduced costs.
2. Improved customer satisfaction: Accurate forecasts play a crucial role in ensuring product availability and meeting customer demands. Accurate predictions of customer needs and demands allow for better resource management, which helps in avoiding stock-outs, and betters planning of production and supply chain activities. Customers are taught to rely on receiving their products in a timely manner, fostering positive experiences and long-term loyalty.
3. Effective inventory management, resource allocation, and production planning: Forecast accuracy directly impacts inventory management by providing insights into the expected demand for products or services. Accurate forecasts help in efficient resource allocation, by aligning production, labour, and procurement activities with the projected demand. The predicted inventory levels furthermore dictate how each resource will be used and in which quantities. Additionally, precise forecasts aid in effective production planning, allowing businesses to streamline their operations, avoid bottlenecks, and meet customer demands efficiently.

How is Forecasting Accuracy Measured?

Now that we know why we measure forecasting accuracy, let us look at how it is measured in three different ways.

Mean Absolute Error (MAE): A measure of forecast accuracy that calculates the average absolute difference between predicted values and the actual values. This metric assesses how close the forecast has come to the actual outcomes, regardless of the direction of the errors. It is a straightforward interpretation of the average error magnitude. MAE is calculated by summing up the absolute difference of your forecast and the actual value, divided by your sample size.