Download Forecast Accuracy

FORECAST ACCURACY
By: Agung Utama
INTRODUCTION
A forecast is never completely accurate, there will
be always deviation from the actual demand.
 This difference between the foracast and the
actual is the forecast error.
 Although forecast eror is inevitable, the objective
of forecasting is that it be as slight as possible.
 There are different measures of forecast error,
including: Mean Absolute Deviation (MAD),
Mean Absolute Percent Deviation (MAPD),
Cummulative Error (CE), and Error Bias (E).

MEAN ABSOLUTE DEVIATION (MAD)

MAD is an average of the difference between the forecast
and actual demand, as computed by the following formula:
MAD= Σ І Dt-Ft I
n
Where:
t =The period number
Dt= Demand in period t
Ft = The forecast for period t
n = The total number of periods
I I = Absolute value

The smaller the value of MAD, the more accurate the
forecast, although viewed alone, MAD is difficult to assess.
COMPUTATIONAL VALUES FOR MAD
period
Demand
(Dt)
Forecast
Ft (α = 0.30)
Error (et)
(Dt-Ft)
1
37
37.00
-
2
40
37.00
3.00
3.00
3
41
37.90
3.10
3.10
4
37
38.83
-1.83
1.83
5
45
38.28
6.72
6.72
6
50
40.29
9.69
9.69
7
43
43.20
-0.20
0.20
8
47
43.14
3.86
3.86
9
56
44.30
11.70
11.70
10
52
47.81
4.19
4.19
11
55
49.06
5.94
5.94
12
54
50.84
3.15
3.15
49.32
53.38
557
I dt-Ft I

Using the data in the table, MAD is computed:

MAD=ΣI Dt-Ft I
n
= 53l39
11
= 4.85
THE MEAN ABSOLUTE PERCENT
DEVIATION




Measures the absolute error as a percentage of
demand rather than per period.
As a result, it eliminates the problem of interpreting
the measure of accuracy relative to the magnitude of
the demand and forecast values, as MAD does.
A lower percent deviation implies a more accurate
forecast.
MAPD = ΣI Dt-Ft I
Σ Dt
= 53.39
520
= 0.10 or 10%
CUMULATIVE ERROR

Cumulative error is computed simply by summing the
forecast errors, as shown in the formula:
E=Σ et


A large positive value indicates that the forecast is
probably consistently lower than the actual demand,
or is biased low.
The comulative eror based on the previuos data is
simply computed as: E=Σ et
= 49.31
AVERAGE ERROR (BIAS)
It is computed by averaging the comulative error
over the number of time periods
 The comulative error is interpreted similarly to
the comulative error. A positive value indicates
low bias, and a negative value indicates high
bias. A value close to zero implies a lack of bias.
 The formula is: Ḗ=Σ et
n
= 49.32
11
= 4.48

DISCUSSION QUESTIONS

a)
b)
Registration number for a marketing seminar
over the past 10 weeks are shown below:
Week
1
2
3
4
5
6
7
8
9
10
Registrations
22
21
25
27
35
29
33
37
41
37
Starting with week 2 and ending with week 11,
forecast registrations using the naïve
forecasting method.
Starting with week 3 and ending with week 11,
forecast registrations using a two-week moving
average.

Demand for heart transplant surgery at RSCM has
increased steadily in the past few years:
Year
1
2
3
4
5
6
Heart
Transplant
45
50
52
56
58
?
The director of medical services predicted 6 years ago
that demand in year 1 would be 41 surgeries.
a) Use exponential smoothing, first with a smoothing
constant of 0.6 and then with 1.9, to develop
forecasts for years 2 through 6.
b) Use MAD criterion, which of the two forecasting
methods is best?

Given the following data, use least squares
regression to derive a trend equation. What is your
estimate of the demand in period 7? In period 12?
Period
1
2
3
4
5
6
Number
7
9
5
11
10
15