Transcript Forecasting
Chapter 4 – Forecasting
PowerPoint presentation to accompany
Heizer/Render
Operations Management, 8e
© 2006 Prentice Hall, Inc.
PSM10
4–1
What is Forecasting?
Process of
predicting a future
event
Underlying basis of
all business
decisions
??
Production
Inventory
Personnel
Facilities
PSM10
4–2
Forecasting Time Horizons
Short-range forecast
Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforce
levels, job assignments, production levels
Medium-range forecast
3 months to 3 years
Sales and production planning, budgeting
Long-range forecast
3+ years
New product planning, facility location,
research and development
PSM10
4–3
Distinguishing Differences
Medium/long range forecasts deal with
more comprehensive issues and support
management decisions regarding
planning and products, plants and
processes
Short-term forecasting usually employs
different methodologies than longer-term
forecasting
Short-term forecasts tend to be more
accurate than longer-term forecasts
PSM10
4–4
Influence of Product Life Cycle
Introduction – Growth – Maturity – Decline
Introduction and growth require longer
forecasts than maturity and decline
As product passes through life cycle,
forecasts are useful in projecting
Staffing levels
Inventory levels
Factory capacity
PSM10
4–5
Types of Forecasts
Economic forecasts
Address business cycle – inflation rate,
money supply, housing starts, etc.
Technological forecasts
Predict rate of technological progress
Impacts development of new products
Demand forecasts
Predict sales of existing product
PSM10
4–6
Strategic Importance of Forecasting
Human Resources – Hiring, training,
laying off workers
Capacity – Capacity shortages can
result in undependable delivery, loss
of customers, loss of market share
Supply-Chain Management – Good
supplier relations and price advance
PSM10
4–7
Seven Steps in Forecasting
1. Determine the use of the forecast
2. Select the items to be forecasted
3. Determine the time horizon of the
forecast
4. Select the forecasting model(s)
5. Gather the data
6. Make the forecast
7. Validate and implement results
PSM10
4–8
The Realities!
Forecasts are seldom perfect
Most techniques assume an
underlying stability in the system
Product family and aggregated
forecasts are more accurate than
individual product forecasts
PSM10
4–9
Forecasting Approaches
Qualitative Methods
Used when situation is vague
and little data exist
New products
New technology
Involves intuition, experience
e.g., forecasting sales on Internet
PSM10
4 – 10
Forecasting Approaches
Quantitative Methods
Used when situation is ‘stable’ and
historical data exist
Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color
televisions
PSM10
4 – 11
Overview of Qualitative Methods
Jury of executive opinion
Pool opinions of high-level
executives, sometimes augment by
statistical models
Delphi method
Panel of experts, queried iteratively
PSM10
4 – 12
Overview of Qualitative Methods
Sales force composite
Estimates from individual
salespersons are reviewed for
reasonableness, then aggregated
Consumer Market Survey
Ask the customer
PSM10
4 – 13
Jury of Executive Opinion
Involves small group of high-level
managers
Group estimates demand by working
together
Combines managerial experience with
statistical models
Relatively quick
‘Group-think’
disadvantage
PSM10
4 – 14
Sales Force Composite
Each salesperson projects his or
her sales
Combined at district and national
levels
Sales reps know customers’ wants
Tends to be overly optimistic
PSM10
4 – 15
Delphi Method
Iterative group
process,
continues until
consensus is
reached
Staff
(Administering
3 types of
survey)
participants
Decision makers
Staff
Respondents
PSM10
Decision Makers
(Evaluate
responses and
make decisions)
Respondents
(People who can
make valuable
judgments)
4 – 16
Consumer Market Survey
Ask customers about purchasing
plans
What consumers say, and what
they actually do are often different
Sometimes difficult to answer
PSM10
4 – 17
Overview of Quantitative Approaches
1. Naive approach
2. Moving averages
Time-Series
Models
3. Exponential
smoothing
4. Trend projection
5. Linear regression
PSM10
Associative
Model
4 – 18
Time Series Forecasting
Set of evenly spaced numerical
data
Obtained by observing response
variable at regular time periods
Forecast based only on past
values
Assumes that factors influencing
past and present will continue
influence in future
PSM10
4 – 19
Time Series Components
Trend
Cyclical
Seasonal
Random
PSM10
4 – 20
Demand for product or service
Components of Demand
Trend
component
Seasonal peaks
Actual
demand
Average
demand over
four years
Random
variation
|
1
|
2
|
3
|
4
Year
PSM10
4 – 21
Trend Component
Persistent, overall upward or
downward pattern
Changes due to population,
technology, age, culture, etc.
Typically several years
duration
PSM10
4 – 22
Seasonal Component
Regular pattern of up and
down fluctuations
Due to weather, customs, etc.
Occurs within a single year
Period
Length
Number of
Seasons
Week
Month
Month
Year
Year
Year
Day
Week
Day
Quarter
Month
Week
7
4-4.5
28-31
4
12
52
PSM10
4 – 23
Cyclical Component
Repeating up and down movements
Affected by business cycle, political,
and economic factors
Multiple years duration
Often causal or
associative
relationships
0
PSM10
5
10
15
20
4 – 24
Random Component
Erratic, unsystematic, ‘residual’
fluctuations
Due to random variation or
unforeseen events
Short duration and
nonrepeating
M
PSM10
T
W
T
F
4 – 25
Naive Approach
Assumes demand in next period is
the same as demand in most
recent period
e.g., If May sales were 48, then June
sales will be 48
Sometimes cost effective and
efficient
PSM10
4 – 26
Moving Average Method
MA is a series of arithmetic means
Used if little or no trend
Used often for smoothing
Provides overall impression of data
over time
∑ demand in previous n periods
Moving average =
n
PSM10
4 – 27
Moving Average Example
Month
Actual
Shed Sales
3-Month
Moving Average
January
February
March
April
May
June
July
10
12
13
16
19
23
26
(10 + 12 + 13)/3 = 11 2/3
(12 + 13 + 16)/3 = 13 2/3
(13 + 16 + 19)/3 = 16
(16 + 19 + 23)/3 = 19 1/3
PSM10
4 – 28
Shed Sales
Graph of Moving Average
30
28
26
24
22
20
18
16
14
12
10
Moving
Average
Forecast
–
–
–
–
–
–
–
–
–
–
–
Actual
Sales
|
J
|
F
|
M
|
A
|
M
|
J
PSM10
|
J
|
A
|
S
|
O
|
N
|
D
4 – 29
Weighted Moving Average
Used when trend is present
Older data usually less important
Weights based on experience and
intuition
Weighted
moving average =
∑ (weight for period n)
x (demand in period n)
PSM10
∑ weights
4 – 30
Weighted
Moving Average
Weights Applied
Period
3
2
1
6
Month
Actual
Shed Sales
January
February
March
April
May
June
July
10
12
13
16
19
23
26
Last month
Two months ago
Three months ago
Sum of weights
3-Month Weighted
Moving Average
[(3 x 13) + (2 x 12) + (10)]/6 = 121/6
[(3 x 16) + (2 x 13) + (12)]/6 = 141/3
[(3 x 19) + (2 x 16) + (13)]/6 = 17
[(3 x 23) + (2 x 19) + (16)]/6 = 201/2
PSM10
4 – 31
Potential Problems With Moving Average
Increasing n smooths the forecast
but makes it less sensitive to
changes
Do not forecast trends well
Require extensive historical data
PSM10
4 – 32
Moving Average And Weighted Moving
Average
Weighted
moving
average
Sales demand
30 –
25 –
20 –
Actual
sales
15 –
Moving
average
10 –
5 –
|
J
|
F
|
M
|
A
|
M
|
J
PSM10
|
J
|
A
|
S
|
O
|
N
|
D
4 – 33
Exponential Smoothing
Form of weighted moving average
Weights decline exponentially
Most recent data weighted most
Requires smoothing constant ()
Ranges from 0 to 1
Subjectively chosen
Involves little record keeping of past
data
PSM10
4 – 34
Exponential Smoothing
New forecast = last period’s forecast
+ (last period’s actual demand
– last period’s forecast)
Ft = Ft – 1 + (At – 1 - Ft – 1)
where
Ft = new forecast
Ft – 1 = previous forecast
= smoothing (or weighting)
constant (0 1)
PSM10
4 – 35
Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant = .20
PSM10
4 – 36
Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant = .20
New forecast = 142 + .2(153 – 142)
PSM10
4 – 37
Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant = .20
New forecast = 142 + .2(153 – 142)
= 142 + 2.2
= 144.2 ≈ 144 cars
PSM10
4 – 38
Effect of
Smoothing Constants
Weight Assigned to
Smoothing
Constant
Most
Recent
Period
()
2nd Most 3rd Most 4th Most 5th Most
Recent
Recent
Recent
Recent
Period
Period
Period
Period
2
3
(1 - ) (1 - )
(1 - )
(1 - )4
= .1
.1
.09
.081
.073
.066
= .5
.5
.25
.125
.063
.031
PSM10
4 – 39
Impact of Different
Demand
225 –
= .5
Actual
demand
200 –
175 –
= .1
150 – |
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
Quarter
PSM10
4 – 40
Choosing
The objective is to obtain the most
accurate forecast no matter the
technique
We generally do this by selecting the
model that gives us the lowest forecast
error
Forecast error = Actual demand - Forecast value
= At - Ft
PSM10
4 – 41
Other interpretation
Ft = Ft – 1 + (At - Ft – 1) – forecast for the t+1st period, At – demand
of the t-th period
Ft At ( 1 )Ft 1
At ( 1 ) At 1 ( 1 )Ft 2
At ( 1 ) At 1 ( 1 )2 Ft 2
At ( 1 ) At 1 ( 1 )2 Ar t 2 ( 1 )Ft 3
At ( 1 ) At 1 ( 1 )2 At 2 ( 1 )3 Ft 3 ...
α 1 α i At i
i 0
Exponential smoothing is the weighted average of the
complete historic demand. The weights are decreasing
exponentially from period to period.
PSM10
4 – 42
Common Measures of Error
Mean Absolute Deviation (MAD)
MAD =
∑ |actual - forecast|
n
Mean Squared Error (MSE)
MSE =
∑ (forecast errors)2
n
PSM10
4 – 43
Common Measures of Error
Mean Absolute Percent Error (MAPE)
n
100 ∑ |actuali - forecasti|/actuali
MAPE =
i=1
n
PSM10
4 – 44
Comparison of Forecast Error
Quarter
Actual
Tonnage
Unloaded
Rounded
Forecast
with
= .10
Absolute
Deviation
for
= .10
Rounded
Forecast
with
= .50
1
2
3
4
5
6
7
8
180
168
159
175
190
205
180
182
175
176
175
173
173
175
178
178
5
8
16
2
17
30
2
4
84
175
178
173
166
170
180
193
186
PSM10
Absolute
Deviation
for
= .50
5
10
14
9
20
25
13
4
100
4 – 45
Comparison of Forecast Error
∑ |deviations|
Rounded
Absolute
MADActual
=
Quarter
Tonage
Unloaded
Forecast
n
with
= .10
Deviation
for
= .10
For 180
= .10 175
168 = 84/8
176
= 10.50
1
2
3
4 For
5
6
7
8
159
175
175
= .50 173
190
173
205 = 100/8
175 =
180
178
182
178
5
8
16
2
17
12.5030
2
4
84
PSM10
Rounded
Forecast
with
= .50
175
178
173
166
170
180
193
186
Absolute
Deviation
for
= .50
5
10
14
9
20
25
13
4
100
4 – 46
Comparison of Forecast Error
∑ (forecast errors)2
MSE = Actual
Quarter
Tonage
Unloaded
Rounded
Forecast
n
with
= .10
Absolute
Deviation
for
= .10
For 180
= .10 175
5
168
176
= 1,558/8
= 194.758
1
2
3
4 For
5
6
7
8
159
175
175
= .50 173
190
173
= 1,612/8175=
205
180
178
182
178
16
2
17
201.50
30
2
4
84
MAD
10.50
PSM10
Rounded
Forecast
with
= .50
175
178
173
166
170
180
193
186
Absolute
Deviation
for
= .50
5
10
14
9
20
25
13
4
100
12.50
4 – 47
Comparison of Forecast Error
n
100 ∑ |deviationi|/actuali
i =Rounded
1
Forecast
Tonage
with
Unloaded
= .10
MAPE =
Actual
Quarter
1
2
3
4
5
6
7
8
n
Absolute
Deviation
for
= .10
For 180
= .10 175
5
168
176
8
= 45.62/8
= 5.70%
159
For 175
=
190
205
180
182
175
.50 173
173
= 54.8/8
175
178
178
MAD
MSE
16
2
17
= 6.85%
30
2
4
84
10.50
194.75
PSM10
Rounded
Forecast
with
= .50
175
178
173
166
170
180
193
186
Absolute
Deviation
for
= .50
5
10
14
9
20
25
13
4
100
12.50
201.50
4 – 48
Comparison of Forecast Error
Quarter
Actual
Tonnage
Unloaded
Rounded
Forecast
with
= .10
1
2
3
4
5
6
7
8
180
168
159
175
190
205
180
182
175
176
175
173
173
175
178
178
MAD
MSE
MAPE
Absolute
Deviation
for
= .10
5
8
16
2
17
30
2
4
84
10.50
194.75
5.70%
PSM10
Rounded
Forecast
with
= .50
175
178
173
166
170
180
193
186
Absolute
Deviation
for
= .50
5
10
14
9
20
25
13
4
100
12.50
201.50
6.85%
4 – 49
Exponential Smoothing with Trend
Adjustment
When a trend is present, exponential
smoothing must be modified
Forecast
exponentially
exponentially
including (FITt) = smoothed (Ft) + (Tt) smoothed
trend
forecast
trend
PSM10
4 – 50
Exponential Smoothing with Trend
Adjustment
Ft At 1 1 Ft 1 Tt 1
Tt Ft Ft 1 1 Tt 1
Step 1: Compute Ft
Step 2: Compute Tt
Step 3: Calculate the forecast FITt = Ft + Tt
PSM10
4 – 51
Exponential Smoothing with Trend
Adjustment Example
Month(t)
1
2
3
4
5
6
7
8
9
10
Actual
Demand (At)
12
17
20
19
24
21
31
28
36
Smoothed
Forecast, Ft
11
PSM10
Smoothed
Trend, Tt
2
Forecast
Including
Trend, FITt
13.00
4 – 52
Exponential Smoothing with Trend
Adjustment Example
Month(t)
1
2
3
4
5
6
7
8
9
10
Forecast
Including
Trend, FITt
13.00
Actual
Smoothed
Smoothed
Demand (At) Forecast, Ft
Trend, Tt
12
11
2
17
20
19
Step 1: Forecast for Month 2
24
21
F2 = A1 + (1 - )(F1 + T1)
31
28
F2 = (.2)(12) + (1 - .2)(11 + 2)
36
= 2.4 + 10.4 = 12.8 units
PSM10
4 – 53
Exponential Smoothing with Trend
Adjustment Example
Month(t)
1
2
3
4
5
6
7
8
9
10
Forecast
Including
Trend, FITt
13.00
Actual
Smoothed
Smoothed
Demand (At) Forecast, Ft
Trend, Tt
12
11
2
17
12.80
20
19
Step 2: Trend for Month 2
24
21
T2 = (F2 - F1) + (1 - )T1
31
28
T2 = (.4)(12.8 - 11) + (1 - .4)(2)
36
= .72 + 1.2 = 1.92 units
PSM10
4 – 54
Exponential Smoothing with Trend
Adjustment Example
Month(t)
1
2
3
4
5
6
7
8
9
10
Forecast
Including
Trend, FITt
13.00
Actual
Smoothed
Smoothed
Demand (At) Forecast, Ft
Trend, Tt
12
11
2
17
12.80
1.92
20
19
Step 3: Calculate FIT for Month 2
24
21
FIT2 = F2 + T1
31
28
FIT2 = 12.8 + 1.92
36
= 14.72 units
PSM10
4 – 55
Exponential Smoothing with Trend
Adjustment Example
Month(t)
1
2
3
4
5
6
7
8
9
10
Actual
Demand (At)
12
17
20
19
24
21
31
28
36
Smoothed
Forecast, Ft
11
12.80
15.18
17.82
19.91
22.51
24.11
27.14
29.28
32.48
PSM10
Smoothed
Trend, Tt
2
1.92
2.10
2.32
2.23
2.38
2.07
2.45
2.32
2.68
Forecast
Including
Trend, FITt
13.00
14.72
17.28
20.14
22.14
24.89
26.18
29.59
31.60
35.16
4 – 56
Exponential Smoothing with Trend
Adjustment Example
35 –
Product demand
30 –
Actual demand (At)
25 –
20 –
15 –
Forecast including trend (FITt)
10 –
5 –
0 – |
1
|
2
|
3
|
4
|
5
|
6
Time (month)
PSM10
|
7
|
8
|
9
4 – 57
Trend Projections
Fitting a trend line to historical data points
to project into the medium-to-long-range
Linear trends can be found using the least
squares technique (regression analysis)
y^ = a + bx
^ = computed value of the variable to
where y
be predicted (dependent variable)
a = y-axis intercept
b = slope of the regression line
x = the independent variable
PSM10
4 – 58
Values of Dependent Variable
Least Squares Method
Actual observation
(y value)
Deviation7
Deviation5
Deviation6
Deviation3
Deviation4
Deviation1
Deviation2
Trend line, y^ = a + bx
Time period
PSM10
4 – 59
Values of Dependent Variable
Least Squares Method
Actual observation
(y value)
Deviation7
Deviation5
Deviation6
Least squares method
minimizes the sum of the
Deviation
squared
errors (deviations)
Deviation3
4
Deviation1
Deviation2
Trend line, y^ = a + bx
Time period
PSM10
4 – 60
Least Squares Method
Equations to calculate the regression variables
y^ = a + bx
b=
Sxy - nxy
Sx2 - nx2
a = y - bx
PSM10
4 – 61
Least Squares Example
Year
1999
2000
2001
2002
2003
2004
2005
Time
Period (x)
1
2
3
4
5
6
7
∑x = 28
x=4
Electrical Power
Demand
74
79
80
90
105
142
122
∑y = 692
y = 98.86
x2
xy
1
4
9
16
25
36
49
∑x2 = 140
74
158
240
360
525
852
854
∑xy = 3,063
3,063 - (7)(4)(98.86)
∑xy - nxy
b=
=
= 10.54
140 - (7)(42)
∑x2 - nx2
a = y - bx = 98.86 - 10.54(4) = 56.70
PSM10
4 – 62
Least Squares Example
Time
Period (x)
Electrical Power
Demand
x2
xy
1999
1
74
1
2000
2
79
4
line is 80
2001The trend
3
9
2002
4
90
16
2003
105
25
y^ 5= 56.70 + 10.54x
2004
6
142
36
2005
7
122
49
Sx = 28
Sy = 692
Sx2 = 140
x=4
y = 98.86
74
158
240
360
525
852
854
Sxy = 3,063
Year
3,063 - (7)(4)(98.86)
Sxy - nxy
b=
=
= 10.54
140 - (7)(42)
Sx2 - nx2
a = y - bx = 98.86 - 10.54(4) = 56.70
PSM10
4 – 63
Power demand
Least Squares Example
160
150
140
130
120
110
100
90
80
70
60
50
Trend line,
y^ = 56.70 + 10.54x
–
–
–
–
–
–
–
–
–
–
–
–
141=
56.7+8*10.54
|
1999
|
2000
|
2001
|
2002
PSM10
|
2003
Year
|
2004
|
2005
|
2006
|
2007
4 – 64
Seasonal Variations In Data
The multiplicative seasonal model can
modify trend data to accommodate
seasonal variations in demand
1. Find average historical demand for each season
2. Compute the average demand over all seasons
3. Compute a seasonal index for each season
4. Estimate next year’s total demand
5. Divide this estimate of total demand by the
number of seasons, then multiply it by the
seasonal index for that season
PSM10
4 – 66
Seasonal Index Example
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
Demand
2003 2004 2005
80
70
80
90
113
110
100
88
85
77
75
82
85
85
93
95
125
115
102
102
90
78
72
78
Average
2003-2005
Average
Monthly
90
80
85
100
123
115
105
100
90
80
80
80
94
94
94
94
94
94
94
94
94
94
94
94
105
85
82
115
131
120
113
110
95
85
83
80
PSM10
Seasonal
Index
4 – 67
Seasonal Index Example
Month
Demand
2003 2004 2005
Average
2003-2005
Average
Monthly
Jan
80
85 105
90
94
Feb
70
85
85
80
94
Mar
80
93 average
82
85 monthly demand
94
2003-2005
Seasonal90index95= 115
Apr
100
94
average monthly
demand
May
113 125 131
123
94
= 90/94 = .957
Jun
110 115 120
115
94
Jul
100 102 113
105
94
Aug
88 102 110
100
94
Sept
85
90
95
90
94
Oct
77
78
85
80
94
Nov
75
72
83
80
94
Dec
82
78
80
80
94
PSM10
Seasonal
Index
0.957
4 – 68
Seasonal Index Example
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
Demand
2003 2004 2005
80
70
80
90
113
110
100
88
85
77
75
82
85
85
93
95
125
115
102
102
90
78
72
78
Average
2003-2005
105
85
82
115
131
120
113
110
95
85
83
80
90
80
85
100
123
115
105
100
90
80
80
80
1,128
PSM10
Average
Monthly
Seasonal
Index
94
94
94
94
94
94
94
94
94
94
94
94
0.957
0.851
0.904
1.064
1.309
1.223
1.117
1.064
0.957
0.851
0.851
0.851
4 – 69
Seasonal Index Example
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
Demand
2003 2004 2005
Average
2003-2005
Average
Monthly
80
85 105
90
94
for802006
70
85 Forecast
85
94
80
93
82
85
94
annual demand
= 1,200
90Expected
95 115
100
94
113 125 131
123
94
110 115 120 1,200 115
94
Jan 113
x
.957 = 96 94
100 102
105
12
88 102 110
100
94
1,200
85
90
95
Feb
x90
.851 = 85 94
77
78
85 12
80
94
75
72
83
80
94
82
78
80
80
94
PSM10
Seasonal
Index
0.957
0.851
0.904
1.064
1.309
1.223
1.117
1.064
0.957
0.851
0.851
0.851
4 – 70
Seasonal Index Example
Jan
95,70
Feb
85,10
Mar
90,40
Apr
106,40
140,00
May
130,90
120,00
Jun
122,30
Jul
111,70
Aug
106,40
Sept
95,70
Oct
85,10
Nov
85,10
Dec
85,10
100,00
80,00
60,00
1
PSM10
2
3
4
5
6
7
8
9 10 11 12
4 – 71
Seasonal Index Example
2006 Forecast
2005 Demand
2004 Demand
2003 Demand
140 –
130 –
Demand
120 –
110 –
100 –
90 –
80 –
70 –
|
J
|
F
|
M
|
A
|
M
|
J
|
J
Time
PSM10
|
A
|
S
|
O
|
N
|
D
4 – 72
Monitoring and Controlling Forecasts
Tracking Signal
Measures how well the forecast is
predicting actual values
Ratio of running sum of forecast errors
(RSFE) to mean absolute deviation (MAD)
Good tracking signal has low values
If forecasts are continually high or low, the
forecast has a bias error
PSM10
4 – 73
Monitoring and Controlling Forecasts
RSFE
Tracking
=
signal
MAD
∑(actual demand in
period i forecast demand
in period i)
Tracking
signal = ∑|actual - forecast|/n)
PSM10
4 – 74
Tracking Signal
Signal exceeding limit
Tracking signal
+
Upper control limit
Acceptable
range
0 MADs
–
Lower control limit
Time
PSM10
4 – 75
Tracking Signal Example
Qtr
Actual
Demand
Forecast
Demand
Error
RSFE
Absolute
Forecast
Error
1
2
3
4
5
6
90
95
115
100
125
140
100
100
100
110
110
110
-10
-5
+15
-10
+15
+30
-10
-15
0
-10
+5
+35
10
5
15
10
15
30
PSM10
Cumulative
Absolute
Forecast
Error
MAD
10
15
30
40
55
85
10.0
7.5
10.0
10.0
11.0
14.2
4 – 76
Tracking Signal Example
Qtr
1
2
3
4
5
6
Tracking
Actual Signal
Forecast
(RSFE/MAD)
Demand
Demand
Error
RSFE
Absolute
Forecast
Error
90-10/10
100= -1 -10
95
-15/7.5
100= -2 -5
115 0/10
100
= 0 +15
100-10/10
110= -1 -10
125
+5/11110
= +0.5+15
140
+35/14.2
110= +2.5
+30
-10
-15
0
-10
+5
+35
10
5
15
10
15
30
Cumulative
Absolute
Forecast
Error
MAD
10
15
30
40
55
85
10.0
7.5
10.0
10.0
11.0
14.2
The variation of the tracking signal
between -2.0 and +2.5 is within acceptable
limits
PSM10
4 – 77
Forecasting in the Service Sector
Presents unusual challenges
Special need for short term records
Needs differ greatly as function of
industry and product
Holidays and other calendar events
Unusual events
PSM10
4 – 78
Questions
Answer the following questions
How would you rate the time horizon for long range forecast in the field of
mobile information technologies?
Which of the Seven Steps in Forecasting is the most interesting for you?
Did you ever use the method of “Jury of Executive Opinion” when
discussing family affairs at home?
Which week sides if the Delphi method would you identify?
Under which circumstances do the Weighted Moving Average method and
the method of exponential smoothing coincide?
Can the two methods of exponential smoothing ever coincide?
Look for the various opportunities of using forecast methods by Excel.
Let the demand follow the function d(t) = 10 + 2t and apply the simple
weighted average forecast method with n = 2. What can you say about the
tracking signal?
PSM10
4 – 79
Questions
Homework No. 3:
Create an Excel Spreadsheet to solve the following problem! Sales of music
stands at Johnny Ho’s music store, in Columbus, Ohio, over the past 10 weeks
are shown in the table below.
Week
Demand
Week
Demand
1
20
6
29
2
21
7
36
3
28
8
22
4
37
9
25
5
25
10
2?
Put here the last digit of your
university ID card number.
a) Forecast demand for each week, including week 10, using exponential
smoothing with α = 0.3 (initial forecast F1 = 20)
b) Compute the MAD.
c) Compute the tracking signal.
Submit your solution file via E-Mail to [email protected] (term: 29.04.2010).
Don’t forget to indicate your university ID card number.
PSM10
4 – 80