1. Measuring Bias in forecast

2. Quotes
In Dictionary: Bias is prejudice in favour of or against one thing, person,
or group compared with another, usually in a way considered to be unfair.
In Statistics: Bias is a systematic as opposed to a random distortion of a statistic population.
Forecast BIAS is described as a tendency to either
over-forecast (meaning, more often than not, the forecast is more than the actual), or
under-forecast (meaning, more often than not, the forecast is less than the actual).

3. From Arkieva
Not smart!

4. Tracking signal
Tracking signal is a measure used to value if the actual demand does not reflect the assumptions in the forecast about the level and perhaps trend in the demand profile. In Statistical Process Control, people study when a process is going out of control and needs intervention.
Similarly Tracking signal tries to flag if there is a persistent tendency for actuals to be higher or lower systematically.
If Forecast is consistently lower than the actual demand quantity, then there is persistent under forecasting and Tracking Signal will be positive.
Tracking Signal is calculated as the ratio of Cumulative Error divided by the mean absolute deviation (MAD). The cumulative error can be positive or negative, so the TS can be positive or negative as well.
TS should pass a threshold test to be significant. If Tracking Signal > 3.75 then there is persistent under forecasting. On the other hand, if this is less than then, there is persistent over-forecasting.

5. Calculating tracking signal
Product
Actual Sales (At)
Forecast (Ft)
abs (At-Ft)
At-Fo
MAD M1 23 22 1 M2 24 2
1,5 M3
1,0 M4 21 -1 M5 25 3
1,4 M6
1,3 M7
159
154 9 5
SUM
Tracking signal =
3,89
Accuracy
94%
Tracking signal is:
Sum of errors (column (At-Fo) divided by
MAD (1,3)

6. Tracking signal, Bonnie Harrison
The “Tracking Signal” quantifies “Bias” in a forecast. No product can be planned from a badly biased forecast. Tracking Signal is the gateway test for evaluating forecast accuracy.
Tracking signal is computed as the running sum of forecast error (RSFE) divided by MAE. We compute RSFE by summing up the forecast errors over time.
The tracking signal in each period is calculated as follows:

7. Running Sum Forecast Error calculation RSFE

9. Tracking signal

10. Since tracking signal (2,22) is between +/- 4,5 there is no need for forecast review (excel bonnie)

11. Normalized metrics (excel bonnie)
Arkieva has the Normalized Forecast Metric to measure the bias. The formula is very simple.
This metric will stay between -1 and 1, with 0 indicating the absence of bias. Consistent negative values indicate a tendency to under-forecast whereas consistent positive values indicate a tendency to over-forecast.