Calculate Mean Absolute Deviation to measure forecast accuracy in units. Determine the average absolute difference between actual and forecast demand.
Mean Absolute Deviation (MAD) measures forecast accuracy in the same units as demand — pieces, cases, or dollars. Unlike MAPE, which expresses error as a percentage, MAD gives you the average absolute number of units by which the forecast misses actual demand.
MAD is especially useful when comparing forecasts for items with similar demand volumes or when you need to translate forecast error into inventory buffer requirements. Many safety stock formulas use MAD as an input to calculate the amount of buffer stock needed.
This calculator takes pairs of actual and forecast values and computes MAD, along with the total absolute deviation and number of periods evaluated.
Supply-chain managers, warehouse operators, and shipping coordinators rely on precise mad calculator (mean absolute deviation) data to maintain efficiency and control costs across complex distribution networks. Revisit this calculator whenever conditions change to keep your logistics plans aligned with real-world performance.
From regional delivery fleets to global freight operations, knowing your precise mad calculator (mean absolute deviation) figures empowers you to negotiate better carrier rates, optimize routes, and allocate resources more effectively. Adjust the inputs above to model your specific supply-chain variables and uncover hidden savings opportunities.
From regional delivery fleets to global freight operations, knowing your precise mad calculator (mean absolute deviation) figures empowers you to negotiate better carrier rates, optimize routes, and allocate resources more effectively. Adjust the inputs above to model your specific supply-chain variables and uncover hidden savings opportunities.
MAD is the most practical forecast error metric for operations because it speaks the language of the warehouse — units. It directly feeds into safety stock calculations and helps planners understand how much buffer inventory is needed to compensate for forecast inaccuracy. Real-time recalculation lets you model different scenarios quickly, ensuring your logistics decisions are backed by accurate, up-to-date numbers.
MAD = (1/n) × Σ|Actual_i − Forecast_i| Where n is the number of periods.
Result: MAD = 5.5 units
Absolute deviations: |100-105|=5, |120-115|=5, |110-108|=2, |130-140|=10. MAD = (5+5+2+10)/4 = 22/4 = 5.5 units. On average, the forecast misses by 5.5 units per period.
Many supply chain textbooks derive safety stock as z × σ × √L, where σ is the standard deviation of demand. Since MAD ≈ 0.8 × σ for normally distributed data, you can substitute σ ≈ 1.25 × MAD. This makes MAD a practical, easy-to-compute input for safety stock calculations.
Plot MAD monthly alongside changes in forecasting methods or data sources. A declining MAD trend confirms that your improvements are working. Be alert to sudden MAD increases that may signal data quality issues, demand pattern shifts, or supply disruptions.
Group SKUs by demand pattern (stable, trending, seasonal, intermittent) and track MAD for each group separately. Stable-demand items should have low MAD; intermittent items will naturally have higher MAD. Setting group-specific MAD targets is more meaningful than a single company-wide target.
MAD is the average of the absolute differences between actual demand and forecast demand across all periods. It measures forecast error magnitude in the same units as demand.
MAD expresses error in absolute units (e.g., 50 pieces). MAPE expresses error as a percentage of actual demand. MAD is better for safety stock calculations; MAPE is better for cross-item comparison.
For normally distributed errors, the standard deviation is approximately 1.25 × MAD. This conversion is used in safety stock formulas that require standard deviation as input.
A "good" MAD depends on the item's demand level. MAD of 5 is excellent for an item with demand of 100 (5% error) but poor for an item with demand of 10 (50% error). Always evaluate MAD relative to demand volume.
Yes, MAD equals zero when the forecast exactly matches actual demand in every period. This is practically impossible but serves as the theoretical best case.
MAD treats all errors equally. MSE squares errors, penalizing large deviations more heavily. If large errors are particularly costly, MSE is more appropriate. For general use and safety stock, MAD is standard.