Demand Planning and
Forecasting
Sujit Singh
Courtesy@ Operations Management Sustainability and Supply Chain Management by
Heizer, Jay, Render, Barry, Munson, Chuck
-
Introduction
Production Capacity
Inventory
Finance
Cash Flow
Procurement
Suppliers Performance
Production
Plan
Management
Return on Investment
Marketing
Customers’ Demand
Human Resource
Manpower Planning
Engineering
Design Specification
Introduction
• Before making plans, an estimate must be made of what conditions will exist
over some future period.
• Demand Forecasting (DF) is a projection of past information and/or
experience into expectation of demand in the future.
• DF is necessary for developing plans to make deliveries in reasonable time
and satisfy future demand.
• Once a demand forecast is at hand, plans for capacity and other resources
could be established.
Importance of demand planning and
Forecasting
• Optimized Inventory Management
• Cost efficiency
• Enhanced Customer satisfaction
• Resource allocation and capacity planning
• Risk Management
• Better financial planning
• Supply chain efficiency
• Market competitiveness
Forecasting Time Horizons
1.
Short-range forecast
►
Up to 1 year, generally less than 3 months
►
Purchasing, job scheduling, workforce levels, job
assignments, production levels
►
Tend to be more accurate than longer-term forecasts
2.
Medium-range forecast
►
3 months to 3 years
►
Sales and production planning, budgeting
3.
Long-range forecast
►
3+ years
►
New product planning, facility location, research and
development
The Realities!
►
►
►
Forecasts are seldom perfect,
unpredictable outside factors may
impact the forecast
Most techniques assume an
underlying stability in the system
Product family and aggregated
forecasts are more accurate than
individual product forecasts
Forecasting Approaches
Qualitative Forecasting
►
Used when situation is vague and little data such
as new products, new technology
►
Involves intuition, experience
Quantitative Forecasting
►
►
Used when situation is ‘stable’ and historical data
exist
►
Existing products
►
Current technology
Involves mathematical techniques
►
e.g., forecasting sales of color televisions
Forecasting Methods
• Judgement Methods : based on experts’ opinion
• Market research Methods: involve qualitative studies
of consumer behavior
• Time-Series methods : Mathematical methods in
which future performances are extrapolated from past
performance
• Casual Methods: Mathematical methods in which
forecast are generated based on variety of system
variables.
Judgement Methods
• Sales-force composite
• Panel of Experts
• Delphi Method
Market Research Methods
• Market Testing
• Market Survey
Time-Series Methods
• Moving Average
• Exponential Smoothing
• Method for data with trends (regression
analysis)
• Method for seasonal data
• More complex methods
Casual Methods
• Generate forecast based on data other than the data
being predicted.
• Forecast based on
• Inflation
• GNP
• Employment rate
• Weather conditions
• Or anything besides the sales
Re-cap
Which Supply Chain is better, PULL or PUSH? And Why? Support your
answer by an example.
Difference Between Forecasting and Prediction.
Forecasting of Demand is very important for managing the supply
chain. Justify.
What are different forecasting methods?
Overview of Quantitative Approaches
1. Naive approach
2. Moving averages
3. Exponential
smoothing
4. Trend projection
5. Linear regression
Time-series
models
Associative
model
Solve this….
Past Demand data
P-1
P-2
P-3
P-4
P5
P6
P7
P8
P9
P10
P11
P12
P13
Item #1 751
741
728
773
718
752
736
768
729
777
748
756
?
Item #2
250
268
289
314
337
367
391
409
433
459
481
500
?
Item# 3
666
618
483
375
303
242
210
239
342
396
457
587
?
Time-Series Components
Trend
Cyclical
Seasonal
Random
Components of Demand
Demand for product or service
Trend
component
Seasonal peaks
Actual demand
line
Average demand
over 4 years
Random variation
|
1
|
2
|
3
Time (years)
Figure 4.1
|
4
Trend Component
Persistent, overall upward or downward
pattern
► Changes due to population, technology,
age, culture, etc.
► Typically several years duration
►
Seasonal Component
►
►
►
Regular pattern of up and down
fluctuations
Due to weather, customs, etc.
Occurs within a single year
PERIOD LENGTH
“SEASON” LENGTH
NUMBER OF “SEASONS” IN PATTERN
Week
Day
7
Month
Week
4 – 4.5
Month
Day
28 – 31
Year
Quarter
4
Year
Month
12
Year
Week
52
Cyclical Component
►
►
►
►
Repeating up and down movements
Affected by business cycle, political, and
economic factors
Multiple years duration
Often causal or
associative
relationships
0
5
10
15
20
Random Component
►
►
►
Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen events
Short duration
and nonrepeating
M
T
F
W
T
Naive Approach
►
Assumes demand in next
period is the same as
demand in most recent period
►
►
►
e.g., If January sales were 68, then
February sales will be 68
Sometimes cost effective and
efficient
Can be good starting point
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
å
Moving average =
n
Moving Average Example
MONTH
ACTUAL SHED SALES
3-MONTH MOVING AVERAGE
January
10
February
12
March
13
April
16
(10 + 12 + 13)/3 = 11 2/3
May
19
(12 + 13 + 16)/3 = 13 2/3
June
23
(13 + 16 + 19)/3 = 16
July
26
(16 + 19 + 23)/3 = 19 1/3
August
30
(19 + 23 + 26)/3 = 22 2/3
September
28
(23 + 26 + 30)/3 = 26 1/3
October
18
(29 + 30 + 28)/3 = 28
November
16
(30 + 28 + 18)/3 = 25 1/3
December
14
(28 + 18 + 16)/3 = 20 2/3
Weighted Moving Average
►
Used when some trend might be present
►
►
Older data usually less important
Weights based on experience and intuition
((
)(
))
Weighted å Weight for period n Demand in period n
moving =
å Weights
average
Weighted Moving Average
MONTH
ACTUAL SHED SALES
January
10
February
12
March
13
April
16
May
June
3-MONTH WEIGHTED MOVING AVERAGE
[(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6
19
WEIGHTS
APPLIED
23
PERIOD
July
26
3
Last month
August
30
2
Two months ago
September
28
1
Three months ago
October
November
December
18 6
Forecast for
16this month =
Sum of the weights
3 x14
Sales last mo. + 2 x Sales 2 mos. ago + 1 x Sales 3 mos. ago
Sum of the weights
Weighted Moving Average
MONTH
ACTUAL SHED SALES
3-MONTH WEIGHTED MOVING AVERAGE
January
10
February
12
March
13
April
16
[(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6
May
19
[(3 x 16) + (2 x 13) + (12)]/6 = 14 1/3
June
23
[(3 x 19) + (2 x 16) + (13)]/6 = 17
July
26
[(3 x 23) + (2 x 19) + (16)]/6 = 20 1/2
August
30
[(3 x 26) + (2 x 23) + (19)]/6 = 23 5/6
September
28
[(3 x 30) + (2 x 26) + (23)]/6 = 27 1/2
October
18
[(3 x 28) + (2 x 30) + (26)]/6 = 28 1/3
November
16
[(3 x 18) + (2 x 28) + (30)]/6 = 23 1/3
December
14
[(3 x 16) + (2 x 18) + (28)]/6 = 18 2/3
Potential Problems With
Moving Average
Increasing n smooths the forecast but makes
it less sensitive to changes
► Does not forecast trends well
► Requires extensive historical data
►
Graph of Moving Averages
Weighted moving average
30 –
Sales demand
25 –
20 –
15 – Actual sales
10 –
Moving average
5–
Figure 4.2
© 2014 Pearson Education
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F
M
A
M
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J
J
Month
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4 - 29
Exponential Smoothing
►
Form of weighted moving average
►
►
►
Requires smoothing constant ()
►
►
►
Weights decline exponentially
Most recent data weighted most
Ranges from 0 to 1
Subjectively chosen
Involves little record keeping of past data
Exponential Smoothing
New forecast = Last period’s forecast
+  (Last period’s actual demand
– Last period’s forecast)
Ft = Ft – 1 + (At – 1 - Ft – 1)
OR
Ft = (At - 1) + (1 - )(Ft – 1)
where
Ft =
Ft – 1 =
 =
new forecast
previous period’s forecast
smoothing (or weighting) constant (0 ≤  ≤ 1)
At – 1 =
previous period’s actual demand
Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant  = .20
© 2014 Pearson Education
4 - 32
Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant  = .20
New forecast
© 2014 Pearson Education
= 142 + .2(153 – 142)
4 - 33
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
© 2014 Pearson Education
4 - 34
Impact of Different 
Demand
225 –
 = .5
Actual
demand
200 –
175 –
 = .1
150 – |
1
|
2
|
3
|
4
|
5
Quarter
|
6
|
7
|
8
|
9
Impact of Different 
225 –
Demand
►
►
 = .5
Actual
demand
values
high
of 
when underlying average
is likely to change
Chose
200
–
Choose low values of 
when underlying average
is stable|
|
|
|
|
150 – |
175 –
1
2
3
4
5
Quarter
6
 = .1
|
7
|
8
|
9
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
Common Measures of Error
Mean Absolute Deviation (MAD)
Actual - Forecast
å
MAD =
n
Determining the MAD
QUARTER
ACTUAL
TONNAGE
UNLOADED
1
180
175
175
2
168
175.50 = 175.00 + .10(180 – 175)
177.50
3
159
174.75 = 175.50 + .10(168 – 175.50)
172.75
4
175
173.18 = 174.75 + .10(159 – 174.75)
165.88
5
190
173.36 = 173.18 + .10(175 – 173.18)
170.44
6
205
175.02 = 173.36 + .10(190 – 173.36)
180.22
7
180
178.02 = 175.02 + .10(205 – 175.02)
192.61
8
182
178.22 = 178.02 + .10(180 – 178.02)
186.30
9
?
178.59 = 178.22 + .10(182 – 178.22)
184.15
FORECAST WITH  = .10
FORECAST WITH
 = .50
Determining the MAD
QUARTER
ACTUAL
TONNAGE
UNLOADED
FORECAST
WITH
 = .10
1
180
175
5.00
175
5.00
2
168
175.50
7.50
177.50
9.50
3
159
174.75
15.75
172.75
13.75
4
175
173.18
1.82
165.88
9.12
5
190
173.36
16.64
170.44
19.56
6
205
175.02
29.98
180.22
24.78
7
180
178.02
1.98
192.61
12.61
8
182
178.22
3.78
186.30
4.30
Sum of absolute deviations:
MAD =
Σ|Deviations|
n
ABSOLUTE
DEVIATION
FOR a = .10
FORECAST
WITH
 = .50
ABSOLUTE
DEVIATION
FOR a = .50
82.45
98.62
10.31
12.33
Common Measures of Error
Mean Squared Error (MSE)
Forecast errors)
å
(
MSE =
n
2
Determining the MSE
QUARTER
ACTUAL
TONNAGE
UNLOADED
1
180
175
2
168
175.50
(–7.5)2 = 56.25
3
159
174.75
(–15.75)2 = 248.06
4
175
173.18
(1.82)2 = 3.31
5
190
173.36
(16.64)2 = 276.89
6
205
175.02
(29.98)2 = 898.80
7
180
178.02
(1.98)2 = 3.92
8
182
178.22
(3.78)2 = 14.29
FORECAST FOR
 = .10
(ERROR)2
52 = 25
Sum of errors squared = 1,526.52
Forecast errors)
å
(
MSE =
n
2
= 1,526.52 / 8 = 190.8
Comparison of Forecast Error
Quarter
Actual
Tonnage
Unloaded
Rounded
Forecast
with
 = .10
Absolute
Deviation
for
 = .10
1
2
3
4
5
6
7
8
180
168
159
175
190
205
180
182
175
175.5
174.75
173.18
173.36
175.02
178.02
178.22
5.00
7.50
15.75
1.82
16.64
29.98
1.98
3.78
82.45
Rounded
Forecast
with
 = .50
175
177.50
172.75
165.88
170.44
180.22
192.61
186.30
Absolute
Deviation
for
 = .50
5.00
9.50
13.75
9.12
19.56
24.78
12.61
4.30
98.62
Comparison of Forecast Error
Rounded
Absolute
∑ |deviations|
Actual
MAD
=
Tonnage
Quarter
1 For
2
3
4
5 For
6
7
8
Unloaded
Forecast
with
n
a = .10
Deviation
for
a = .10
 180
= .10
175
168
175.5
159 = 82.45/8
174.75
175
173.18
 190
= .50 173.36
205 = 98.62/8
175.02
180
178.02
182
178.22
=
=
5.00
7.50
10.31
15.75
1.82
16.64
29.98
12.33
1.98
3.78
82.45
Rounded
Forecast
with
 = .50
175
177.50
172.75
165.88
170.44
180.22
192.61
186.30
Absolute
Deviation
for
 = .50
5.00
9.50
13.75
9.12
19.56
24.78
12.61
4.30
98.62
Comparison of Forecast Error
2
∑ (forecast
errors)
Rounded
Absolute
Actual
MSE =Tonnage
Quarter
1 For
2
3
4
5 For
6
7
8
Forecast
with
n
a = .10
Deviation
for
a = .10
175
168
175.5
= 1,526.54/8
159
174.75
175
173.18
 190
= .50 173.36
205
175.02
= 1,561.91/8
180
178.02
182
178.22
5.00
7.50
190.82
15.75
1.82
16.64
29.98
195.24
1.98
3.78
82.45
10.31
Unloaded
 180
= .10
MAD
=
=
Rounded
Forecast
with
 = .50
175
177.50
172.75
165.88
170.44
180.22
192.61
186.30
Absolute
Deviation
for
 = .50
5.00
9.50
13.75
9.12
19.56
24.78
12.61
4.30
98.62
12.33
Comparison of Forecast Error
Quarter
Actual
Tonnage
Unloaded
Rounded
Forecast
with
a = .10
1
2
3
4
5
6
7
8
180
168
159
175
190
205
180
182
175
175.5
174.75
173.18
173.36
175.02
178.02
178.22
MAD
MSE
Absolute
Deviation
for
a = .10
5.00
7.50
15.75
1.82
16.64
29.98
1.98
3.78
82.45
10.31
190.82
Rounded
Forecast
with
a = .50
175
177.50
172.75
165.88
170.44
180.22
192.61
186.30
Absolute
Deviation
for
 = .50
5.00
9.50
13.75
9.12
19.56
24.78
12.61
4.30
98.62
12.33
195.24
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
175.5
174.75
173.18
173.36
175.02
178.02
178.22
MAD
MSE
Absolute
Deviation
for
 = .10
5.00
7.50
15.75
1.82
16.64
29.98
1.98
3.78
82.45
10.31
190.82
Rounded
Forecast
with
 = .50
175
177.50
172.75
165.88
170.44
180.22
192.61
186.30
Absolute
Deviation
for
 = .50
5.00
9.50
13.75
9.12
19.56
24.78
12.61
4.30
98.62
12.33
195.24
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
y^ = a + bx
where y^ = computed value of the variable to be predicted
(dependent variable)
a = y-axis intercept
b = slope of the regression line
x = the independent variable
Values of Dependent Variable (y-values)
Least Squares Method
Actual observation
(y-value)
Deviation7
Deviation5
Deviation3
Deviation1
(error)
Deviation6
Least squares method minimizes the
sum of Deviation
the squared
errors (deviations)
4
Deviation2
Trend line, y^ = a + bx
|
|
|
|
|
|
|
1
2
3
4
5
6
7
Time period
© 2014 Pearson Education
Figure 4.4
4 - 49
Least Squares Method
Equations to calculate the regression variables
ŷ = a+ bx
xy - nxy
å
b=
å x - nx
2
2
a = y - bx
© 2014 Pearson Education
4 - 50
Least Squares Example
YEAR
ELECTRICAL
POWER DEMAND
YEAR
ELECTRICAL
POWER DEMAND
1
74
5
105
2
79
6
142
3
80
7
122
4
90
Least Squares Example
YEAR (x)
ELECTRICAL POWER
DEMAND (y)
x2
xy
1
74
1
74
2
79
4
158
3
80
9
240
4
90
16
360
5
105
25
525
6
142
36
852
7
122
49
854
Σx = 28
Σy = 692
x 28
å
x=
=
=4
n
7
Σx2 = 140
y 692
å
y=
=
= 98.86
n
7
Σxy = 3,063
Least Squares Example
YEAR (x)
1
2
xy - nxy 3,063 - ( 7) ( 4) (98.86) 295
å
ELECTRICAL
b=
= POWER
=
= 10.54
DEMAND (y) 140 - ( 7) 4
xy
å x - nx
( ) x 28
2
2
2
2
74
79
()
3
a = y - bx = 98.8680
-10.54 4 = 56.70
4
90
5
1
74
4
158
9
240
16
360
Thus,
105 ŷ = 56.70 +10.54x25
525
6
142
36
852
7
122
49
854
Σx = 28
Σy = 692
Σx2 = 140
Σxy = 3,063
x in
y+ 10.54(8)
Demand
å
å
28year 8 = 56.70
692
x=
=
=4
y=
=
= 98.86
=
141.02,
or
141
megawatts
n
7
n
7
Power demand (megawatts)
Least Squares Example
160
150
140
130
120
110
100
90
80
70
60
50
Trend line,
y^ = 56.70 + 10.54x
–
–
–
–
–
–
–
–
–
–
–
–
|
1
|
2
|
3
|
4
|
5
Year
|
6
|
7
|
8
|
9
Figure 4.5
Seasonal Variations In Data
The multiplicative
seasonal model can
adjust trend data for
seasonal variations
in demand
Seasonal Variations In Data
Steps in the process for monthly seasons:
1. Find average historical demand for each month
2. Compute the average demand over all months
3. Compute a seasonal index for each month
4. Estimate next year’s total demand
5. Divide this estimate of total demand by the
number of months, then multiply it by the
seasonal index for that month
Seasonal Index Example
DEMAND
MONTH
YEAR 1
YEAR 2
YEAR 3
AVERAGE
YEARLY
DEMAND
Jan
80
85
105
90
Feb
70
85
85
80
Mar
80
93
82
85
Apr
90
95
115
100
May
113
125
131
123
June
110
115
120
115
July
100
102
113
105
Aug
88
102
110
100
Sept
85
90
95
90
Oct
77
78
85
80
Nov
75
82
83
80
Dec
82
78
80
80
Total average annual demand =
1,128
AVERAGE
MONTHLY
DEMAND
SEASONAL
INDEX
Seasonal Index Example
DEMAND
MONTH
YEAR 1
YEAR 2
YEAR 3
AVERAGE
YEARLY
DEMAND
AVERAGE
MONTHLY
DEMAND
Jan
80
85
105
90
94
Feb
70
85
85
80
94
80
93
82
85
94
100
94
123
94
115
94
Mar
Apr
May
June
Average
90
95 1,128
115
=
= 94
monthly
113
125
131
12 months
demand
110
115
120
July
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
82
83
80
94
Dec
82
78
80
80
94
Total average annual demand =
1,128
SEASONAL
INDEX
Seasonal Index Example
DEMAND
MONTH
YEAR 1
YEAR 2
YEAR 3
AVERAGE
YEARLY
DEMAND
AVERAGE
MONTHLY
DEMAND
Jan
80
85
105
90
94
Feb
70
85
85
80
94
Mar
80
93
82
85
94
Apr
90
95
115
100
94
May
113
125
131
123
94
Seasonal110
June
July
index
=
100
Average
monthly
demand
years
115
120
115 for past 394
102 Average
113
105 demand 94
monthly
Aug
88
102
110
100
94
Sept
85
90
95
90
94
Oct
77
78
85
80
94
Nov
75
82
83
80
94
Dec
82
78
80
80
94
Total average annual demand =
1,128
SEASONAL
INDEX
.957( = 90/94)
Seasonal Index Example
DEMAND
MONTH
YEAR 1
YEAR 2
YEAR 3
AVERAGE
YEARLY
DEMAND
AVERAGE
MONTHLY
DEMAND
SEASONAL
INDEX
Jan
80
85
105
90
94
.957( = 90/94)
Feb
70
85
85
80
94
.851( = 80/94)
Mar
80
93
82
85
94
.904( = 85/94)
Apr
90
95
115
100
94
1.064( = 100/94)
May
113
125
131
123
94
1.309( = 123/94)
June
110
115
120
115
94
1.223( = 115/94)
July
100
102
113
105
94
1.117( = 105/94)
Aug
88
102
110
100
94
1.064( = 100/94)
Sept
85
90
95
90
94
.957( = 90/94)
Oct
77
78
85
80
94
.851( = 80/94)
Nov
75
82
83
80
94
.851( = 80/94)
Dec
82
78
80
80
94
.851( = 80/94)
Total average annual demand =
1,128
Seasonal Index Example
Seasonal forecast for Year 4
MONTH
Jan
DEMAND
1,200
12
Feb
1,200
12
Mar
1,200
12
Apr
1,200
12
May
1,200
12
June
1,200
12
x .957 = 96
x .851 = 85
x .904 = 90
x 1.064 = 106
x 1.309 = 131
x 1.223 = 122
MONTH
July
DEMAND
1,200
12
Aug
1,200
12
Sept
1,200
12
Oct
1,200
12
Nov
1,200
12
Dec
1,200
12
x 1.117 = 112
x 1.064 = 106
x .957 = 96
x .851 = 85
x .851 = 85
x .851 = 85
Seasonal Index Example
Year 4 Forecast
Year 3 Demand
Year 2 Demand
Year 1 Demand
140 –
130 –
Demand
120 –
110 –
100 –
90 –
80 –
70 –
|
J
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F
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M
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A
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M
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J
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J
Time
|
A
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S
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O
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N
|
D
Thanks!