Leer Evaluating Forecast Accuracy | Inventory Management and Demand Forecasting

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Understanding how well your demand forecasts match reality is critical in supply chain management. Two of the most widely used forecast error metrics are Mean Absolute Error (MAE) and Mean Squared Error (MSE). These metrics help you quantify the difference between your predicted demand and what actually occurred, providing a clear way to assess and compare forecasting methods. MAE measures the average magnitude of errors in a set of forecasts, without considering their direction, while MSE gives more weight to larger errors by squaring the differences.


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def mean_absolute_error(actual, forecast):
    """
    Calculate Mean Absolute Error (MAE) between two lists.
    """
    n = len(actual)
    absolute_errors = [abs(a - f) for a, f in zip(actual, forecast)]
    return sum(absolute_errors) / n

def mean_squared_error(actual, forecast):
    """
    Calculate Mean Squared Error (MSE) between two lists.
    """
    n = len(actual)
    squared_errors = [(a - f) ** 2 for a, f in zip(actual, forecast)]
    return sum(squared_errors) / n

When you use these error metrics, lower values generally indicate that your forecast is closer to the actual demand. MAE is straightforward to interpret: it tells you, on average, how many units your forecast is off by. MSE, because it squares each error, penalizes larger mistakes more heavily. This means that if your forecast sometimes makes large errors, the MSE will be noticeably higher than the MAE. Both metrics are useful for comparing different forecasting models or methods, but you should be aware that MSE is more sensitive to outliers due to the squaring of errors. By applying the functions above, you can directly compute these error metrics for any pair of actual and forecasted demand lists.


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actual_demand = [100, 120, 130, 115, 140]
forecasted_demand = [110, 125, 128, 120, 135]

mae = mean_absolute_error(actual_demand, forecasted_demand)
mse = mean_squared_error(actual_demand, forecasted_demand)

print("Mean Absolute Error (MAE):", mae)
print("Mean Squared Error (MSE):", mse)

1. Why is it important to evaluate forecast accuracy in supply chain management?

2. What does a high MAE indicate about a forecast?

3. Fill in the blank: The formula for MAE is the mean of the absolute ____ between actual and forecasted values.

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