Demand forecasts guide some of the most important decisions in the supply chain, such as how much raw material to buy or how many products to make.
When those predictions are off, businesses risk overproducing and tying up cash in excess inventory, or underproducing and losing out on sales because of insufficient stock.
Of course, it’s impossible to predict the future with 100% precision. But your forecasts still need to be as accurate as possible to steer the business in the right direction. That’s why it’s important to understand how to measure and improve your forecast accuracy.
What Is Forecast Accuracy in Supply Chain Planning?
Forecast accuracy in the supply chain measures how close your demand predictions are to the actual demand. Measuring and improving your forecast accuracy can help you hold just the right amount of inventory to meet your demand — you don’t incur unnecessary costs for carrying excess stock.
But the importance of forecasting in supply chain management goes beyond inventory optimization.
Accurate predictions promote data-driven decision-making. Instead of relying on guesswork or a gut feeling, you use accurate, data-backed demand predictions to plan your supply chain operations.
Key Metrics to Measure Forecast Accuracy
Wondering how precise your demand predictions are? Use one of the following forecast accuracy metrics to find out.
Mean Absolute Percentage Error (MAPE)
MAPE tells you, on average, how far off your forecasts are as a percentage, regardless of whether you overestimated or underestimated your actual demand.
MAPE forecast accuracy formula = 1/n x ∑[( |actual – forecast| ) / |actual| ] x 100%
n = number of forecast periods
∑ = sum of individual percentage errors
∣…∣ = absolute value (“absolute” means you ignore a number’s negative sign)
[( |actual – forecast| ) / |actual| ] x 100% = absolute percentage error
Example: Sam wants to measure how accurate his company’s demand forecasts were over the past four months. So he organizes his data as follows:
| Month | Actual demand | Forecasted demand | Absolute percentage error for each month [( |actual – forecast| ) / |actual| ] x 100% |
| March | 1,000 | 950 | 5% |
| April | 1,200 | 1,300 | 8.33% |
| May | 1,100 | 1,050 | 4.55% |
| June | 900 | 990 | 10% |
He then adds up all the absolute percentage errors:
∑[( |actual – forecast| ) / |actual| ] x 100% = 5 + 8.33 + 4.55 + 10 = 27.88%
Next, he divides the results by four (n = the number of forecast periods) to calculate his MAPE:
MAPE = 27.88% / 4 = 6.97%
This means his forecasts were off by an average of 6.97% over the past four months.
Use MAPE when:
- You need a percentage-based measurement for your forecast’s accuracy.
- Your actual values don’t include zero — if your actual values include zero, you can’t calculate the absolute percentage error with MAPE (division by zero is mathematically undefined).
Mean Absolute Error (MAE)
MAE measures the average difference between your forecasted and actual values, expressed in the same units as your data rather than as a percentage.
MAE formula = 1/n x ∑ ( |actual – forecast| )
Where:
n = number of forecast periods
∑ ( |actual – forecast| ) = sum of absolute errors in each forecast period
Example: A company’s forecasted and actual unit sales in three months were as follows:
| Month | Actual units sold | Forecasted units | Absolute error for each month (|actual – forecast|) |
| January | 110 | 100 | 10 |
| February | 180 | 160 | 20 |
| March | 150 | 180 | 30 |
To calculate the MAE, you first find the sum of the absolute errors:
∑ ( |actual – forecast| ) = 10 + 20 + 30 = 60 units
Next, you determine the number of forecast periods:
n = 3 (three months)
Finally, divide the sum of the absolute errors by the number of forecast periods:
MAE = 60 / 3 = 20 units
That means the company’s forecasts were off by an average of 20 units in those three months.
Use MAE when you want a direct measurement of the error size in your supply chain forecasts.
Root Mean Square Error (RMSE)
RMSE involves finding the difference between predicted and actual values, squaring those errors to eliminate negative signs, and calculating the average of the squared errors. You then determine the square root of that average to get the RMSE.
RMSE = √[1/n∑(actual – forecast)2]
Where:
n = number of forecast periods
∑ (actual – forecast)2 = sum of squared errors
Example: A company’s actual and forecasted values for unit sales in January and February were as follows:
| Month | Actual units sold | Forecasted units | Error (actual – forecasted) | Squared error |
| January | 200 | 150 | 50 | 2,500 |
| February | 160 | 180 | -20 | 400 |
Here’s how to calculate the RMSE:
n = 2 (two months)
∑ (actual – forecast)2 = 2,500 + 400 = 2,900
Average sum of squared errors = 2,900 / 2 = 1,450
RMSE = √1,450 = 38.08 units
Since RMSE squares errors, big forecasting mistakes have a great impact on the final score. This makes the metric suitable when large deviations between the actual and predicted values would be particularly costly or risky in your supply chain.
Factors That Influence Forecast Accuracy
Do your strategies for supply chain planning depend on forecasts? The following elements will influence the accuracy of your predictions.
Data Quality and Availability
Demand forecasting involves multiple data points, including historical sales, market trends, and customer behaviors. For your predictions to be reliable, this data must be accurate, complete, and up to date. It also needs to be sufficient to reveal meaningful patterns and correlations.
Demand Variability and Seasonality
Fluctuating demand, caused by seasonality, changes in customer preferences, or other factors, can make it difficult to identify reliable trends and patterns.
Supply Chain Complexity
The more suppliers involved in a supply chain, the harder it is to forecast accurately — additional players introduce new variables to consider.
External Disruptions
Natural disasters, political unrest, pandemics, and other unexpected events may suddenly alter demand patterns, which can significantly skew your forecasts.
How to Improve Forecast Accuracy in the Supply Chain
What can you do to make your forecasting more accurate?
- Use reliable data in your predictions: Make sure your supply chain data is accurate, complete, consistent, and up to date. Data preparation tools can help you achieve that. The accuracy of your predictions largely depends on the quality of the data you use.
- Review your forecasts regularly: Demand variability and sudden disruptions can shift demand patterns. Continuously review your predictions and processes to keep up with these changes.
- Use real-time data: Historical data isn’t enough to get demand planning right. Use real-time updates from your supply chain software to adjust your forecasts on the go and stay ahead of disruptions.
Use AI and Machine Learning for Smarter Forecasts
Predicting your demand using spreadsheets and other old-school tools is slow and prone to errors. Forecasting your demand with AI can help you overcome these issues. McKinsey reported that using AI in supply chain forecasting can reduce errors by up to 50%.
Traditional forecasting methods mostly rely on historical data. Platforms powered by AI and machine learning algorithms not only look at past sales trends but also factor in real-time variables like the weather, holidays, economic shifts, and even social media sentiment. That way, you can adjust your demand predictions in real time.
However, not all AI-based forecasting tools are designed for supply chain operations.
Surgere’s AI-powered platform provides real-time supply-chain visibility and advanced analytics, enabling you to predict your demand, optimize your inventory, and adapt to changing customer needs. Contact us today to see how our AI-driven platform can help improve your forecasts’ accuracy.
