APMGNZ632 Operations Management
Maths & Excel skills
FORECAST ERROR CALCULATION
INTRODUCTION
There are many methods available for forecasting and a business may use many and then track the
resulting forecasts and actual data to find which methods was most accurate (and could be used
again).
This is done by finding the ‘error’ in the forecast which is a measure of how different the forecast
and actual data are.
Several error methods are used:MAD (mean absolute deviation)
MSE (mean squared error)
MAPE (mean absolute percentage error)
The methods all do the same thing in slightly different ways.
We will look at two - MAD and MSE.
EXAMPLE : Big Mac demand at local McDonalds restaurant.
DAY
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
ACTUAL
98
85
90
105
120
160
110
FORECAST 1
95
90
75
100
125
175
100
FORECAST 2
85
85
95
100
110
150
100
To find the most accurate forecast we find the error of each forecast (compared to actual sales) and
the one with the smaller error is the more accurate, (and this is the method that we would
recommend using again).
METHOD:
1. Open an Excel SS and save it as something.
2. Copy and paste the data from the table into SS. (Tidy up spacing, remove borders etc if you like).
3. MAD calculation.
This requires use of the ‘absolute value’ which is the non-zero value of any integer, expressed
without any sign (+ /-). In excel we use the absolute formula for this (= abs).
We need to find the sum of the differences of the forecast and the actual for each day, and using
absolute value makes it easier to account for over and under estimation.
Choose two columns to store the abs values for each forecast and actual. Head them ABSVAL 1 and
ABSVAL2 (or similar).
APMGNZ632 Operations Management
Maths & Excel skills
4. In the top cell of ABSVAL1 enter =abs (actual – forecast 1). Press enter. Copy down column (use
handle). Repeat for ABSVAL 2. (If you try and copy across from ABSVAL 1 to ABSVAL 2 you will have a
cell referencing problem).
5. At the bottom of the ABSVAL data, enter the formula =sum( . Select your ABSVAL data, closer the
brackets. Press enter. Repeat for ABSVAL 2.
6. Underneath the ABSVAL 1 sum cell enter = sum/7. This is dividing the sum by the seven data
points to find the average (or mean) absolute error. Repeat for ABSVAL 2 and you have finished.
7. Label the cells so you remember what is in each one.
8. Which forecast is more accurate?
9. MSE Calculation.
This process uses the step of squaring each error to remove any negatives (as we used abs for
above), so that errors do not cancel each other out.
10. Choose two columns for the error values. Head them ERROR1 and ERROR2
11. Enter a formula to find the difference of the actual and forecast data in the top cell of each
column, copy down.
12. Choose the columns next to the error data, and head them SQE1 and SQE2
13. Into the top cell of the columns, enter a formula to multiply the error by itself (squaring it).
Repeat for the other data set.
14. At the bottom of the SQE 1 data, enter the formula =sum( . Select your SQE 1data, closer the
brackets. Press enter. Repeat for SQE 2.
15. Underneath the SQE 1 sum cell enter = sum/7. This is dividing the sum by the seven data points
to find the average (or mean) absolute error. Repeat for SQE 2 and you have finished.
16. Label the cells so you remember what is in each one.
17. Which forecast is more accurate?