A Comprehensive Guide to Accurate Forecasting [ With Formulas]

Join us as we delve deep into what forecast accuracy is, why it is important, and what benefits it can bring to your business. And, please stay with us as we discuss different forecast accuracy formulas and how implementing them can help you make more accurate projections.  

What Is Forecast Accuracy?

Forecast accuracy is a way of calculating the accuracy of a particular forecast (e.g., sales forecast) or how well it managed to predict real outcomes (e.g., sales values). It can also help companies assess how reliable and effective their current forecasting models, methods, and techniques are. 

Calculating the degree to which a forecast is dependable can provide businesses with valuable insight into their current market (overall market and the most recent trends). This info can help companies (i.e., relevant stakeholders) in their decision-making process, making it more reliable and data-driven.

Forecast accuracy is in use across numerous industries and business fields, with the most common types of forecasting being:

• Sales forecasting — It helps companies predict their future sales volumes, allows them to enhance inventory management, and better organize production processes.  
• Demand planning— Allows businesses to understand their inventory needs. The goal is to ensure product availability to meet customer demand without running out of inventory or having too much of it.  
• Financial forecasting — Businesses use this type of forecasting to make decisions about budgeting, resource management, and allocation, various investment plans, and policies.

Why Is Forecast Accuracy Important for Business?

Comparing and contrasting the estimated values of a particular forecast with real-life outcomes gives insight into the quality of the forecasts, which is what forecast accuracy is all about. It allows businesses to measure how accurate their current forecasting models are and gives them enough information to discern how much confidence they should place in each forecasting model. 

By evaluating and improving forecast accuracy regularly, companies can upgrade their planning processes and make better strategic decisions, allowing them to:

Reduce Costs  

Forecast accuracy allows businesses to better manage their inventories and avoid excessive stockpiling or shortages, leading to better financial management and cost reduction. Once companies know their forecasting methods are reliable (through forecast accuracy), they can match their production capacity and procurement needs with a more dependable demand estimate. 

This can help minimize holding and carrying costs and products becoming out-of-date (i.e., obsolescence), reducing overall expenses.

Improve Customer Relations

Forecast accuracy plays a pivotal role in ensuring product availability and meeting customer demands, which inevitably leads to better resource and supply chain management. When customers are certain they’ll receive their products on time and without hassle or delays, their overall relationships and experience with the company will improve.

Improve Inventory Management and Resource Allocation

Forecast accuracy directly impacts inventory management by allowing companies to determine the accuracy of demand forecasting (for a specific product or service). It’s also extremely helpful in resource allocation, allowing businesses to match production, labor, and procurement actions with the demand estimate. This helps predict in which way and how much each available resource will be needed/used. 

How Do You Calculate Forecast Accuracy?

You can use a forecast formula to calculate the accuracy of a particular forecast. The most commonly used forecasting formulas are:

Mean Absolute Percentage Error

The Mean Absolute Percentage Error (MAPE) is a formula used to determine the accuracy of different types of estimates (e.g., time estimates, project budget estimates, sale estimates, etc). The way to calculate MAPE is by subtracting the estimated value (forecast) from the actual value, dividing the result by the actual value, and then multiplying that result by 100 to turn it into percentages.

Here’s a short example of MAPE calculation, just to make sure you fully understand how to use this formula:

MAPE Calculation Example 

Let’s say you’ve completed a time estimate for a project. The forecast shows that to finish said project your company will need to spend 1250 work hours. You start working on the project, ultimately finishing it in 1500 work hours. Now that you have both the forecast and the actual value (which you got after completing the project), you can determine the forecast accuracy of the particular estimating method you’ve used. Let’s take those numbers and put them into the MAPE formula:

So, in this particular case, the forecast accuracy was off by 17%

Before moving on to the next forecast accuracy formula, we must mention that the MAPE calculation is not always useful. For example, if you’re dealing with small numbers or quantities, you might get results that don’t show the full picture. 

Let’s say your forecast says that you’ll need 2 of something (it could be anything, but let’s say work hours) to finish a project. You start working on it and end up completing it in 1 hour. With MAPE, you’ll get a result that shows:

In this case, the forecast was off by 100% (instead of 2, the project took 1 hour to finish). Although technically correct, an error of 100% does not fully reflect the forecast accuracy. The result of 100% suggests that the original estimate was completely off when, in reality, it was only off by a single hour.  

Mean Absolute Error

The Mean Absolute Error (MAE) formula for calculating forecast accuracy works by taking the absolute value (i.e., the non-negative value of the number) of the Forecast subtracted by the Actual value, and then dividing that result by the sample size: