Demand planning
26
Demand planning
=
Demand forecast
+
Demand management
http://www.slsc.nu.ac.th/bsoln Email: bsoln@nu.ac.th
27
® Vatcharapol Sukhotu
Demand planning
Demand Planning
is the combined process of forecasting and managing the customer
demands to create a planned pattern of demand that meets the firms
operational and financial goals.
Managing Operations Across the Supply Chain, Swink et. al.
28
Demand planning
® Vatcharapol Sukhotu
Demand Forecasting
is the decision process in which
managers predict demand and make
operational plans accordingly.
Managing Operations Across the Supply Chain, Swink et. al.
29
® Vatcharapol Sukhotu
Demand planning
Demand Management
is the other part of demand planning that is
a proactive approach in which managers
attempts to influence the demand. Usually,
demand management involves the use of
pricing and promotional activities.
Managing Operations Across the Supply Chain, Swink et. al.
30
® Vatcharapol Sukhotu
Importance of demand planning
Demand plan is underlying basis of all business decisions
•
•
•
•
Production
Inventory
Personnel
Facilities
• Affects firm’s decision and strategy
• Bad demand plan will have a severe impact on the firm
• Marketing and Production functions rely a lot on demand plan
Adapted from: Production and operations analysis, Nahmias and Operations Management, Hiezer and Render
31
Demand
32
® Vatcharapol Sukhotu
Demand in business
Independent Demand
Finished Goods
Dependent Demand
Raw Materials,
Component parts,
Sub-assemblies, etc.
33
® Vatcharapol Sukhotu
Credit: Motor Trend
What happens if we forecast the demand for 100,000 cars
and
forecast the demand for 500,000 wheels ?
(with a 4-wheel car model)
34
® Vatcharapol Sukhotu
Independent Demand
Finished Goods
Dependent Demand
Raw Materials,
Component parts,
Sub-assemblies, etc.
We forecast only independent demand
We calculate dependent demand
35
® Vatcharapol Sukhotu
Demand dimensions
PRODUCT
CHANNEL
TIME BUCKET
36
® Vatcharapol Sukhotu
Products
All
products
Spark
A
Product family
Still
B
C
D
Product items
37
® Vatcharapol Sukhotu
Channel of distribution
All
channels
Tradition
al trade
North
Agent
1
Agent
2
Modern
trade
Channel
South
Agent
3
Agent
4
Region
8-12
Walco
Customer
38
® Vatcharapol Sukhotu
Time bucket vs. time horizon
Bucket = months
Horizon = 2 years
1
2
3
4
5
6
7
8
9
10 11 12
Year 1
1
2
3
4
5
6
7
8
9
10 11 12
Month
4
Quarter
Year 2
Bucket = Quarters
Horizon = 2 years
1
2
3
4
Year 1
1
2
3
Year 2
Bucket = Months
Horizon = 1 year
1
2
3
4
5
6
7
8
9
10 11 12
Month
Year 1
39
Demand forecasting
40
® Vatcharapol Sukhotu
Demand forecasting
Demand Forecasting
is the decision process in which managers predict demand and make operational
plans accordingly.
Managing Operations Across the Supply Chain, Swink et. al.
Difference between forecasting
and guessing
41
Forecasting characteristics
® Vatcharapol Sukhotu
Almost always wrong!
43
Challenge
Forecast the daily demand of Coca
Cola can in a store for the next 7
days.
Credit: Matichon
44
Why do we still need to do a good
forecasting?
45
® Vatcharapol Sukhotu
Can we rely on that ‘gut
feelings’ to make the
decision?
140
80
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
It is wrong – but how much wrong?
Historical demand plan
® Vatcharapol Sukhotu
Demand
Historical demand actual
Actual
60
Historical
Week
Demand actual
200
180
160
Plan
120
100
Can anybody find a ‘perfect’ equation
to fit the demand?
40
Future
0
47
® Vatcharapol Sukhotu
Let’s look at 2 forecasts
A and B
48
A
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
It is wrong – but how much wrong?
® Vatcharapol Sukhotu
Demand
Historical demand actual
200
180
160
140
120
100
80
60
40
Historical
Future
0
Week
49
A
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
It is wrong – but how much wrong?
® Vatcharapol Sukhotu
Demand
Historical demand actual
Historical
Week
Demand forecast
200
180
160
140
Forecast
120
100
80
60
40
Future
0
50
A
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
It is wrong – but how much wrong?
Historical demand actual
® Vatcharapol Sukhotu
Demand
Demand forecast
Historical
Week
Demand actual
200
180
160
140
Forecast
120
Wrong a lot
100
Wrong a lot
Wrong a lot
80
60
40
Future
0
51
B
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
It is wrong – but how much wrong?
® Vatcharapol Sukhotu
Demand
Historical demand actual
200
180
160
140
120
100
80
60
40
Historical
Future
0
Week
52
B
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
It is wrong – but how much wrong?
® Vatcharapol Sukhotu
Demand
Historical demand actual
200
180
160
140
120
Forecast
100
80
60
40
Historical
Future
0
Week
53
B
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
It is wrong – but how much wrong?
® Vatcharapol Sukhotu
Demand
Historical demand actual
140
Historical
Week
Demand actual
200
180
160
Wrong a lot
Wrong a lot
Wrong a lot
100
Wrong a lot
120
Wrong a lot
Forecast
Wrong a lot
Wrong a lot
Wrong a lot
80
60
40
Future
0
54
® Vatcharapol Sukhotu
Why do we still need to do a good
forecasting?
55
® Vatcharapol Sukhotu
It is wrong – but how much wrong?
Good forecast will reduce the chance of being wrong a lot
Error
Error
Forecast Actual
Good forecast will
reduce the chance
of this happening
Forecast Actual
56
Something cannot be forecast
Credit: Alamy
57
® Vatcharapol Sukhotu
Who would
predict this?
58
® Vatcharapol Sukhotu
Who would even
predict this?
59
Forecasting characteristics
® Vatcharapol Sukhotu
A good forecast is more than a single number
– Mean
– Variance/Error
Adapted from: Production and operations analysis, Nahmias and Operations Management, Hiezer and Render
60
Forecasting characteristics
® Vatcharapol Sukhotu
• Aggregate forecasts are usually more accurate
Adapted from: Production and operations analysis, Nahmias and Operations Management, Hiezer and Render
61
® Vatcharapol Sukhotu
Aggregation
3 Tesla models
Credit: Fox News
Standard
Long range
A
B
C
Total
(Aggregated)
Forecast
10
10
10
30
Actual
5
10
15
30
5
0
5
Error
10
Performance
0
62
Forecasting characteristics
® Vatcharapol Sukhotu
• The longer the horizon, the less accurate the forecast
• Most techniques assume an underlying stability in the system
Adapted from: Production and operations analysis, Nahmias and Operations Management, Hiezer and Render
63
Forecasting characteristics
® Vatcharapol Sukhotu
• Forecasts should not be used to the exclusion of known information
– It is usually good to dynamically update the forecast as more
information/knowledge becomes known
Adapted from: Production and operations analysis, Nahmias and Operations Management, Hiezer and Render
64
® Vatcharapol Sukhotu
Forecast ≠ Plan
Forecast ≠ Budget
Forecast ≠ Sales target
Plan ≠ Sales target
65
6
6
Plan vs. Sales target
http://www.slsc.nu.ac.th/bsoln Email: bsoln@nu.ac.th
® Vatcharapol Sukhotu
Forecast horizon
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
Short-term forecasting usually
employs different methodologies
than longer-term forecasting
Short-term forecasts tend to be
more accurate than longer-term
forecasts
• Sales and production planning,
budgeting
Long-range forecast
•
3+
years
• New product planning, facility location,
research and development
Adapted from Heizer
Medium/long range forecasts deal
with more comprehensive issues
and support management
decisions regarding planning and
products, plants and processes
67
Forecast and product life cycle
Source: Heizer
® Vatcharapol Sukhotu
68
® Vatcharapol Sukhotu
Forecasting steps
Determine the use of the forecast
Select the items to be forecasted
Determine the time horizon of the forecast
Select the forecasting model(s)
Gather the data
Make the forecast
Validate and implement results
Evaluate
Adapted from Heizer
69
® Vatcharapol Sukhotu
Forecasting approach
1. Qualitative
2. Quantitative
70
® Vatcharapol Sukhotu
Qualitative techniques
Experience
Qualitative process
Demand forecast
Supporting analysis
71
Forecasting approach
® Vatcharapol Sukhotu
Qualitative Methods
• Used when situation is vague and little data exist
• New products
• New technology
• Involves intuition, experience
• e.g., forecasting sales on Internet
Source: Operations Management, Hiezer and Render
72
Forecasting approach
® Vatcharapol Sukhotu
Qualitative methods
Jury of executive opinion
• Pool opinions of high-level executives, sometimes augment by
statistical models
• Involves small group of high-level managers
• Group estimates demand by working together
• Combines managerial experience with statistical models
• Relatively quick
• ‘Group-think’ disadvantage
Source: Operations Management, Hiezer and Render
73
Forecasting approach
® Vatcharapol Sukhotu
Qualitative methods
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
Source: Operations Management, Hiezer and Render
74
Forecasting approach
® Vatcharapol Sukhotu
Qualitative methods
Delphi method
• Panel of experts, queried iteratively
• Iterative group process, continues until consensus is reached
• 3 types of participants
• Decision makers
• Staff
• Respondents
Source: Operations Management, Hiezer and Render
75
Forecasting approach
® Vatcharapol Sukhotu
Qualitative methods
Consumer Market Survey
• Ask the customer
• Ask customers about purchasing plans
• What consumers say, and what they actually do are
often different
• Sometimes difficult to answer
Source: Operations Management, Hiezer and Render
Credit: Green Rope
76
® Vatcharapol Sukhotu
Quantitative techniques
Formula
Numbers
Demand forecast
77
® Vatcharapol Sukhotu
Quantitative techniques
1. Causal method
2. Time series method
78
® Vatcharapol Sukhotu
Causal method
Use factors that are not the demand to forecast demand.
Non-demand factors
Demand forecast
79
® Vatcharapol Sukhotu
Forecasting approach
Quantitative methods
Causal method
Let Y be the quantity to be forecasted and (X1, X2, . . . , Xn) be n variables that have predictive power for Y.
A causal model is Y = f (X1, X2, . . . , Xn)
A typical relationship is a linear one (econometric). That is,
Y = a0 + a1X1 + . . . + an Xn.
Adapted from: Production and operations analysis, Nahmias and Operations Management, Hiezer and Render
80
® Vatcharapol Sukhotu
Time series method
Use historical demand to forecast demand.
Historical demand
Demand forecast
81
Forecasting approach
® Vatcharapol Sukhotu
Quantitative methods
Time series method
• 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
Adapted from: Production and operations analysis, Nahmias and Operations Management, Hiezer and Render
82
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
Time series
® Vatcharapol Sukhotu
Demand
Historical demand actual
200
180
160
140
120
100
80
60
40
Historical
Now
Week
Future
0
83
20
2018-01
2018-03
2018-05
2018-07
2018-09
2018-11
2018-13
2018-15
2018-17
2018-19
2018-21
2018-23
2018-25
2018-27
2018-29
2018-31
2018-33
2018-35
2018-37
2018-39
2018-41
2018-43
2018-45
2018-47
2018-49
2018-51
2019-01
2019-03
2019-05
2019-07
2019-09
2019-11
2019-13
2019-15
2019-17
2019-19
2019-21
2019-23
2019-25
2019-27
2019-29
2019-31
2019-33
2019-35
2019-37
2019-39
2019-41
2019-43
2019-45
2019-47
2019-49
2019-51
Time series
® Vatcharapol Sukhotu
Demand
Historical demand actual
180
Historical
Now
Week
Demand plan
200
Use demand in the series of times in the
past to forecast future demand
160
140
Forecast
120
100
80
60
40
Future
0
84
® Vatcharapol Sukhotu
Simple forecasting technique
30
𝑨_𝟐
25
𝑨𝟏
20
Demand
𝑨𝟓
𝑭𝟔 =?
𝑨𝟒
𝑭𝟕 =?
𝑨_𝟑
15
10
5
Month
0
0
1
2
3
4
5
Now
6
7
Actual
85
Moving average
Example
•
•
•
•
® Vatcharapol Sukhotu
MA is a series of arithmetic means
Used if little or no trend
Used often for smoothing
Provides overall impression of data over time
Source: Operations Management, Hiezer and Render
86
® Vatcharapol Sukhotu
Shed Sales
Moving average
30
28
26
24
22
20
18
16
14
12
10
–
–
–
–
–
–
–
–
–
–
–
Moving
Average
Forecast
Actual
Sales
|
J
|
F
|
M
|
A
|
M
|
J
|
J
|
A
|
S
|
O
|
N
|
D
Source: Heizer
87
Exponential smoothing
•
•
•
® Vatcharapol Sukhotu
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
Source: Operations Management, Hiezer and Render
88
® Vatcharapol Sukhotu
Exponential smoothing
is a smart and efficient technique for capturing the historicle
pattern
89
® Vatcharapol Sukhotu
Exponential smoothing
Suppose we had forecasted the demand for month 5, F5 = 18
and
we have observed the actual demand for month 5 , A5 = 22
30
𝑨𝟐
25
𝑨𝟓 = 𝟐𝟐
𝑨𝟏
Demand
20
We were under-forecasted =
𝐴 −𝐹
𝑨𝟒
𝑭𝟔 =?
𝑭𝟓 = 𝟏𝟖
15
𝐹 =𝐹 +α 𝐴 −𝐹
10
𝐹
5
=𝐹 +α 𝐴 −𝐹
Month
0
0
1
2
3
4
Historical
5
Now
6
7
Future
Actual
Source: Heizer
90
® Vatcharapol Sukhotu
Exponential smoothing
adjusts the over- or under-forecasted demand to the actual demand of
the last period.
Starting from the last
period forecast
We can give the weight to how much we
want to adjust.
Adjust according to the
amount of under- or
over-forecasted value
 = 1 we adjust to the latest actual
 = 0 we do not adjust at all
91
® Vatcharapol Sukhotu
Exponential smoothing
Suppose we had forecasted the demand for month 5, F5 = 18
and
we have observed the actual demand for month 5 , A5 = 22
30
𝑨𝟐
25
𝑨𝟓 = 𝟐𝟐
𝑨𝟏
Demand
20
We were under-forecasted =
𝐴 −𝐹
𝑨𝟒
𝑨𝟑
15
𝑭𝟔 =?
𝑭𝟓 = 𝟏𝟖
𝐹 =𝐹 +α 𝐴 −𝐹
10
5
Month
0
0
1
2
3
4
5
Historical
Actual
Source: Heizer
Now
6
7
Future
If  = 0.5,
92
® Vatcharapol Sukhotu
Using just one-period data, is it any good?
.
.
.
93
® Vatcharapol Sukhotu
Exponential smoothing
30
𝑨𝟐
25
𝑨𝟏
Demand
20
𝑨𝟒
α 𝛼−1
The weight given will be
reduced for the actual
demand further in the
past.
15
10
𝑨𝟓 = 𝟐𝟐
α 𝛼−1
𝑨𝟑
Highest weight given to
the latest actual
demand.
α
𝑭𝟔 =?
α 𝛼−1
α 𝛼−1
5
Month
0
0
1
2
3
4
Historical
𝐹 =𝐹 +α 𝐴 −𝐹
𝐹 = α 𝐴 + α 𝛼−1
Source: Heizer
5
Now
6
7
Future
Actual
𝐴 + α 𝛼−1
𝐴 + α 𝛼−1
𝐴 + α 𝛼−1
𝐴 +⋯
94
® Vatcharapol Sukhotu
Exponential smoothing
225 –
Actual
demand
Demand
200 –
 = .5
175 –
 = .1
150 –
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
Quarter
Source: Heizer
95
® Vatcharapol Sukhotu
Exponential smoothing
Standard formula for coding
If 𝑘 = 1,
𝐿 = α𝐴 + 1 − 𝛼 𝐿
or
𝐹
=𝐿
𝑘 = 1, 2, 3, …
𝐹
=𝐹 +α 𝐴 −𝐹
Source: Provost and Fawcett
Smoothing parameters (between 0 and 1)
Level
=α
96
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Exponential smoothing
Example
𝐹
𝐴
𝐹 = ?
Source: Heizer
𝐿 = α𝐴 + 1 − 𝛼 𝐿
𝐿 = α𝐴 + 1 − 𝛼 𝐿
Note, for a one-step forecast, 𝑘 = 1: 𝐹 = 𝐿 , then
𝐹
=𝐿
𝑘 = 1, 2, 3, …
𝐿 = α𝐴 + 1 − 𝛼 𝐹
= 0.2 × 153 + 1 − 0.2 × 142
= 144
𝐹 = 𝐿 = 144
97
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Demand with trend
70
𝑭𝟕 =?
60
𝑭𝟔 =?
50
Demand
𝑨𝟓
40
𝑨𝟒
𝑨𝟑
30
𝑨𝟏
20
𝑨𝟐
10
Month
0
0
1
2
3
4
Historical
5
Now
6
7
Future
Actual
99
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Holt’s exponential smoothing model
Trend
Standard formula for coding
𝐿 = α𝐴 + 1 − 𝛼 𝐿
𝑇 =β 𝐿 −𝐿
𝐹
= 𝐿 + 𝑘𝑇
+𝑇
+ 1−𝛽 𝑇
𝑘 = 1, 2, 3, …
Level
Trend (growth or decline per period)
Forecast = Level + Trend for each Period in the future
Source: Provost and Fawcett
Smoothing parameters (between 0 and 1)
Level
=α
Trend
=β
100
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Demand with trend
70
𝑭𝟕 = 𝑳𝟓 + 𝟐 𝑻𝟓
60
𝑭𝟔 = 𝑳𝟓 + 𝑻𝟓
50
Demand
𝑨𝟓
40
𝑨𝟏
20
𝐿
𝑨𝟒
𝑨𝟑
30
𝑇
2×𝑇
𝑨𝟐
10
Month
0
0
1
2
3
4
Historical
5
Now
6
7
Future
Actual
101
® Vatcharapol Sukhotu
Exercise
Demand forecast
Holt's exponential smoothing

b
0.5
Year
2021
2022
2023
2024
2025
𝐴
𝐹
6,846
7,512
𝐿
6,780
7,523
0.3
𝑇
753
750
Starting parameters
Parameters that need to be calculated
8,272
9,022
9,772
Forecast of each year over the next 3 years =?
102
Exercise
® Vatcharapol Sukhotu
103
® Vatcharapol Sukhotu
Seasonal forecast technique
Demand
4,500,000
4,000,000
Cycle (M)
3,500,000
3,000,000
2,500,000
2,000,000
1,500,000
Season 2
Season 1
Season 12
1,000,000
500,000
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
0
Month
Source: Heizer
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Winter’s exponential smoothing model
Trend and seasonality
Standard formula for coding
𝐿 =α
𝐴
𝑆
+ 1−𝛼 𝐿
𝑇 =β 𝐿 −𝐿
𝑆 =γ
𝐹
+𝑇
+ 1−𝛽 𝑇
𝐴
+ 1−𝛾 𝑆
𝐿
= 𝐿 + 𝑘𝑇 𝑆
Level
Trend (growth or decline per period)
Seasonality (factor for higher or lower
demand in each season)
𝑘 = 1, 2, 3, …
Forecast = [Level + Trend for each Period in the future]
x Seasonalilty factor
Source: Provost and Fawcett
Smoothing parameters (between 0 and 1)
Level
=α
Trend
=β
Seasonal = γ
106
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Exercise
Demand forecast
Winter's exponential smoothing

b
0.5
Year
2021
2022
2023
Quarter
𝐴
1
2
3
4
1
2
3
4
1
2
3
4
𝐹
1,277
2,067
1,952
1,550
1,396
2,213
2,153
1,750
𝐿
1,967
1,893
1,860
1,886
1,926
g
0.3
𝑇
0.3
𝑆
30
-1
-11
0
12
0.78
1.21
1.12
0.89
0.77
1.20
1.13
0.90
Starting parameters
Parameters that need to be calculated
1,487
2,348
2,211
1,769
Forecast of each month in 2023 = ?
107
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108
® Vatcharapol Sukhotu
Case: KT Beverage
Forecasting
Example of demand forecast using Exponential Smoothing with Trend and Seasonality
Demand
forecast
Historical
Future
Now
111
Integrated business planning tool
Credit: SAP
® Vatcharapol Sukhotu
112
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Credit: SAP
113
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Forecasting performance
Mean Absolute Deviation (MAD)
MAD =
∑ | Ai - F i |
n
Ai= actual demand in period i
Fi= demand forecast in period I
n = number of periods
Mean Squared Error (MSE)
MSE =
∑ (Ai - Fi )2
n
Mean Absolute Percent Error (MAPE)
MAPE =
Source: Heizer
100 ∑ | Ai - Fi |/ Ai
n
114
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Forecast bias
•
•
A bias occurs when the Expected Value of a forecast error is not zero
An Unbiased Forecast is generally preferred
Source: Nahmias
115
Practical Considerations
•
•
Source: Heizer
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Overly sophisticated forecasting methods can be problematic,
especially for long term forecasting.
Always update forecast once new information becomes available
121
Example: Demand planning system
122
Demand planning system
® Vatcharapol Sukhotu
Software support
123
Demand planning system
Source: Infor
® Vatcharapol Sukhotu
124
Forecast Generation
Source: Infor
® Vatcharapol Sukhotu
125
Forecast Graph
Source: Infor
® Vatcharapol Sukhotu
126
Forecast Table
Source: Infor
® Vatcharapol Sukhotu
127
Production Selection to View Forecast
Source: Infor
® Vatcharapol Sukhotu
128
Channel Selection to View Forecast
® Vatcharapol Sukhotu
129
Mark-out History
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Source: Infor
130
Mark-out History
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Source: Infor
131
Noted for Mark out History
Source: Infor
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132
Re-forecasting
Source: Infor
® Vatcharapol Sukhotu
133
Refitting Forecast
Source: Infor
® Vatcharapol Sukhotu
134
Promotional Profiles
Source: Infor
® Vatcharapol Sukhotu
135
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Promotional Profiles
•
•
Promotion profile = promotional pattern
Shows the proportion of additional sales over the promotional period
In a 3 week promotion,
50% of extra sales will occur
in Week 1,
25% will occur in Week 2
and
25% will occur in Week 3.
Source: Infor
136
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Channel matrices
Country
Depot 1
Source: Infor
Depot 2
Region 1
Region 2
137
Case: KT Beverage
® Vatcharapol Sukhotu
Forecasting
139
Case: KT Beverage
® Vatcharapol Sukhotu
Forecasting
140
Paper: Understanding demand
141
Discussion
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Read paper: Understanding demand
Discussion
1. 3 types of demand, independent, derived, and dependent. Which is the type of the demand, we must
not forecast? Explain the reason briefly.
2. Why do we need to do a forecast (instead of going to a demand plan or sales target)? Explain the briefly.
3. Which should happen before a) forecast or b) sales target? Explain the reason briefly.
4. Why should we differentiate between demand (sales) forecast and the sales target?
Group exercise
142
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Big data
“
refers to data-sets whose size is so large that
the quantity can no longer fit into the memory that computers
use for processing.”
Manyika et al, Mckinsy and Co.
143
Big data
Customer Forecasting: The first prediction
is that big data will be used to switch
forecasting focus away from the product
and more towards the customer.
® Vatcharapol Sukhotu
The fact is, items don’t really “do”
anything. They don’t sell themselves. They
don’t make decisions.
Causal Forecasting: The second is that
big data will lead to far more causal
forecasting.
The customer’s behavior is what you
should be tracking. Big data and the ability
to analyze customer transactions have
revolutionized the understanding of
customer demand.
Blue Ridge Software (2016) cited by Snapp (2017) in Foresight
144
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Big data
Forecasting by item or customer
Or
145
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Big data
The company is actually stocking
products, and it therefore must generate a
product forecast in order for inventory
management to work properly
Multiple ship-to locations for a single
customer. Therefore, forecasting at the
customership-to location is another
option.
By moving to a customer forecast we
reduce the volume that is forecasted
Causal models are often used where the number of
forecasted items is small, and the financial benefit is very
large. A good example of this is forecasting in the financialservices industry, where investment banks have few
forecasted items and very big budgets.
Snapp (2017) in Foresight
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Challenges of big data in demand planning
How much and which data to include in the planning
process. Often these large data-sets tend to be
“sparse” and “transient.” As more data-sets are
included, the complexity of data management and
system support also increases
One has to “trust” machine-learning
algorithms to make those judgments
Big data-sets are typically used to detect
patterns and associations that have
predictive value. It is common to use
machine-learning techniques. Planners
are unfamiliar with these methods.
Contradiction between the increased
personalization afforded by big data and
the aggregate nature of the S&OP
process.
Boone et al (2018) in Foresight
147
Artificial Intelligence
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148
Credit: GAP
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150
Case: Big data at Gap Inc.
Source: Predicting Consumer Tastes with Big Data at Gap; Harvard Business School
® Vatcharapol Sukhotu
151
Case: Big data at Gap Inc.
® Vatcharapol Sukhotu
Digital data streams allowed companies to observe their consumers’ purchase journeys and collect a detailed trail of
data about their online behavior. The mining of big data could yield many actionable insights to inform managerial
decision making, such as identifying consumers who were more loyal to brands, matching consumers to products they
might prefer, or predicting the behaviors or characteristics that could cause consumers to churn. By uncovering
patterns in past customer behavior, companies could develop heuristics or algorithm-driven protocols to customize
how they treated future customers to maximize satisfaction and/or profitability. It allowed “remarketing” or
“retargeting”: as companies observed that a particular visitor viewed an item online but failed to purchase it, they
could immediately serve up customized digital advertising that appeared as customers surfed other websites to entice
them to return and complete the purchase.
As digital data streams became more accessible and robust, companies were exploring how to use data-mining and
machine-learning to induct consumer preferences and predict future behaviors.
Source: Predicting Consumer Tastes with Big Data at Gap; Harvard Business School
152
Case: Big data at Gap Inc.
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With the firing of his creative directors, Peck was betting on a new role for big data—as the initial creative spark for a
new line—predicting what the new fashion would be in the upcoming season.
Product 3.0 relied heavily on the analysis of customer purchase data. According to Peck, “We’ve also substantially
increased our testing of product whether that’s crowd source testing, which we now have validation results in better
commercial outcomes, or testing physically in our stores, oftentimes in stores that are seasonally ahead of where we
are so that we can that to inform our buys. Google Analytics data was also a source of inspiration. A recent fashion
trend, men’s jogging pants, was identified early, as Gap’s managers noticed that customers were using the search
term on its websites, and its progressive adoption across North America was predicted based on the geolocations of
various people using the search term.
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Big data: higher valume and more variety of data types, and various sources.
The use of big data analyses for
• Patterns
• Segmentation of one - personalization
• Gaining insights into behavior
• Prediction
Resulting in business
Source: Sahay in Harvard Business Publishing
actions
154