3. 3
Demand Management
Independent Demand:
Finished Goods
A Dependent Demand:
Raw Materials,
Component parts,
B(4) C(2) Sub-assemblies, etc.
D(2) E(1) D(3) F(2)
1-3
4. 4
Independent Demand:
What a firm can do to manage it?
• Can take an active role to
influence demand
• Can take a passive role and
simply respond to demand
1-4
5. 5
Types of Forecasts
• Qualitative (Judgmental)
• Quantitative
– Time Series Analysis
– Causal Relationships
– Simulation
1-5
6. 6
Components of Demand
• Average demand for a period
of time
• Trend
• Seasonal element
• Cyclical elements
• Random variation
• Autocorrelation
1-6
7. 7
Finding Components of Demand
Seasonal variation
Seasonal variation
x
x x Linear
Linear
x x
x x Trend
x x Trend
Sales
x x x
x
x
xx
x xx x x
x
x
x x x x x x
x x xxx x
x x x
x xxxxx
x
x x
1 2 3 4
Year
1-7
9. 9
Delphi Method
l. Choose the experts to participate
representing a variety of knowledgeable
people in different areas
2. Through a questionnaire (or E-mail), obtain
forecasts (and any premises or
qualifications for the forecasts) from all
participants
3. Summarize the results and redistribute them
to the participants along with appropriate
new questions
4. Summarize again, refining forecasts and
conditions, and again develop new
questions
5. Repeat Step 4 as necessary and distribute
the final results to all participants
1-9
10. 10
Time Series Analysis
• Time series forecasting models
try to predict the future based on
past data
• You can pick models based on:
1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified
personnel
1-10
11. 11
Simple Moving Average Formula
• The simple moving average model assumes an
average is a good estimator of future behavior
• The formula for the simple moving average is:
A t-1 + A t-2 + A t-3 +...+A t- n
Ft =
n
Ft = Forecast for the coming period
N = Number of periods to be averaged
A t-1 = Actual occurrence in the past period for up to “n”
periods
1-11
12. 12
Simple Moving Average Problem (1)
A t-1 + A t-2 + A t-3 +...+A t- n
Ft =
Week Demand n
1 650 Question: What are the 3-
Question: What are the 3-
2 678 week and 6-week moving
week and 6-week moving
3 720 average forecasts for
average forecasts for
4 785
demand?
demand?
5 859
6 920 Assume you only have 3
Assume you only have 3
7 850 weeks and 6 weeks of
weeks and 6 weeks of
8 758 actual demand data for the
actual demand data for the
9 892 respective forecasts
10 920
respective forecasts
11 789
12 844
1-12
14. 14
Plotting the moving averages and comparing
Plotting the moving averages and comparing
them shows how the lines smooth out to reveal
them shows how the lines smooth out to reveal
the overall upward trend in this example
the overall upward trend in this example
1000
900
Demand
800
Demand
3-Week
700
6-Week
600
500 Note how the
Note how the
1 2 3 4 5 6 7 8 9 10 11 12 3-Week is
3-Week is
Week smoother than
smoother than
the Demand,
the Demand,
and 6-Week is
and 6-Week is
even smoother
even smoother
1-14
15. 15
Simple Moving Average Problem (2) Data
Question: What is the
Question: What is the
3 week moving
3 week moving
Week Demand average forecast
average forecast
1 820 for this data?
2 775
for this data?
3 680 Assume you only
Assume you only
4 655 have 3 weeks and
have 3 weeks and
5 620 5 weeks of actual
5 weeks of actual
6 600 demand data for
demand data for
7 575 the respective
the respective
forecasts
forecasts
1-15
17. 17
Weighted Moving Average Formula
While the moving average formula implies an equal
While the moving average formula implies an equal
weight being placed on each value that is being averaged,
weight being placed on each value that is being averaged,
the weighted moving average permits an unequal
the weighted moving average permits an unequal
weighting on prior time periods
weighting on prior time periods
The formula for the moving average is:
The formula for the moving average is:
Ft = w1A t-1 + w 2 A t-2 + w 3A t-3 +...+w n A t-n
n
wt = weight given to time period “t”
wt = weight given to time period “t”
occurrence (weights must add to one)
∑w i =1
occurrence (weights must add to one) i=1
1-17
18. 18
Weighted Moving Average Problem (1) Data
Question: Given the weekly demand and weights, what is
Question: Given the weekly demand and weights, what is
the forecast for the 4th period or Week 4?
the forecast for the 4th period or Week 4?
Week Demand Weights:
1 650
t-1 .5
2 678
3 720 t-2 .3
4 t-3 .2
Note that the weights place more emphasis on the
Note that the weights place more emphasis on the
most recent data, that is time period “t-1”
most recent data, that is time period “t-1”
1-18
20. 20
Weighted Moving Average Problem (2) Data
Question: Given the weekly demand information and
Question: Given the weekly demand information and
weights, what is the weighted moving average forecast
weights, what is the weighted moving average forecast
of the 5th period or week?
of the 5th period or week?
Week Demand Weights:
1 820 t-1 .7
2 775 t-2 .2
3 680
t-3 .1
4 655
1-20
22. 22
Exponential Smoothing Model
Ftt = Ft-1 + α(At-1 - Ft-1)
F = Ft-1 + α(At-1 - Ft-1)
Where :
Ft = Forcast value for the coming t time period
Ft - 1 = Forecast value in 1 past time period
At - 1 = Actual occurance in the past t time period
α = Alpha smoothing constant
• Premise: The most recent observations might
have the highest predictive value
• Therefore, we should give more weight to the
more recent time periods when forecasting 1-22
23. 23
Exponential Smoothing Problem (1) Data
Week Demand
1 820 Question: Given the
Question: Given the
2 775 weekly demand
weekly demand
3 680 data, what are the
data, what are the
4 655 exponential
exponential
5 750 smoothing
smoothing
6 802 forecasts for
forecasts for
7 798 periods 2-10 using
periods 2-10 using
8 689
α=0.10 and α=0.60?
α=0.10 and α=0.60?
9 775
Assume F1=D11
Assume F1=D
10
1-23
24. 24
Answer: The respective alphas columns denote the forecast values. Note
Answer: The respective alphas columns denote the forecast values. Note
that you can only forecast one time period into the future.
that you can only forecast one time period into the future.
Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
1-24
25. 25
Exponential Smoothing Problem (1) Plotting
Note how that the smaller alpha results in a smoother line
Note how that the smaller alpha results in a smoother line
in this example
in this example
900
800 Demand
Demand
700 0.1
600 0.6
500
1 2 3 4 5 6 7 8 9 10
Week
1-25
26. 26
Exponential Smoothing Problem (2) Data
Question: What are the
Question: What are the
Week Demand exponential smoothing
exponential smoothing
1 820 forecasts for periods 2-5
forecasts for periods 2-5
2 775 using a =0.5?
using a =0.5?
3 680
4 655
Assume F11=D11
Assume F =D
5
1-26
28. 28
The MAD Statistic to Determine Forecasting Error
n
1 MAD ≈ 0.8 standard deviation
∑ A t - Ft 1 standard deviation ≈ 1.25 MAD
t=1
MAD =
n
• The ideal MAD is zero which would mean
there is no forecasting error
• The larger the MAD, the less the
accurate the resulting model
1-28
29. 29
MAD Problem Data
Question: What is the MAD value given
Question: What is the MAD value given
the forecast values in the table below?
the forecast values in the table below?
Month Sales Forecast
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
1-29
30. 30
MAD Problem Solution
Month Sales Forecast Abs Error
1 220 n/a
2 250 255 5
3 210 205 5
4 300 320 20
5 325 315 10
40
n
Note that by itself, the MAD
∑A
t=1
t - Ft
40
Note that by itself, the MAD
only lets us know the mean
only lets us know the mean
MAD = = = 10 error in a set of forecasts
error in a set of forecasts
n 4
1-30
31. 31
Tracking Signal Formula
• The Tracking Signal or TS is a
measure that indicates whether the
forecast average is keeping pace with
any genuine upward or downward
changes in demand.
• Depending on the number of MAD’s
selected, the TS can be used like a
quality control chart indicating when
the model is generating too much
error in its forecasts.
• The TS formula is:
RSFE Running sum of forecast errors
TS = =
MAD Mean absolute deviation
1-31
32. 32
Simple Linear Regression Model
The simple linear regression
The simple linear regression Y
model seeks to fit a line
model seeks to fit a line
through various data over
through various data over
time
a
time
0 1 2 3 4 5 x (Time)
Yt = a + bx Is the linear regression model
Is the linear regression model
Yt is the regressed forecast value or dependent
variable in the model, a is the intercept value of the
the regression line, and b is similar to the slope of the
regression line. However, since it is calculated with
the variability of the data in mind, its formulation is
not as straight forward as our usual notion of slope.
1-32
33. 33
Simple Linear Regression Formulas for Calculating “a” and “b”
a = y - bx
∑ xy - n(y)(x)
b= 2 2
∑ x - n(x )
1-33
34. 34
Simple Linear Regression Problem Data
Question: Given the data below, what is the simple linear
Question: Given the data below, what is the simple linear
regression model that can be used to predict sales in future
regression model that can be used to predict sales in future
weeks?
weeks?
Week Sales
1 150
2 157
3 162
4 166
5 177
1-34
35. 35
Answer: First, using the linear regression formulas, we
Answer: First, using the linear regression formulas, we
can compute “a” and “b”
can compute “a” and “b”
Week Week*Week Sales Week*Sales
1 1 150 150
2 4 157 314
3 9 162 486
4 16 166 664
5 25 177 885
3 55 162.4 2499
Average Sum Average Sum
b=
∑ xy - n(y)(x) = 2499 - 5(162.4)(3) = 63 = 6.3
∑ x 2 - n(x )2 55 − 5(9) 10
a = y - bx = 162.4 - (6.3)(3) = 143.5
36. 36
The resulting regression model
is: Yt = 143.5 + 6.3x
Now if we plot the regression generated forecasts against the
actual sales we obtain the following chart:
180
175
170
165
160 Sales
Sales
155 Forecast
150
145
140
135
1 2 3 4 5
Period
37. 37
Web-Based Forecasting: CPFR
• Collaborative Planning, Forecasting,
and Replenishment (CPFR) a Web-
based tool used to coordinate demand
forecasting, production and purchase
planning, and inventory replenishment
between supply chain trading partners.
• Used to integrate the multi-tier or n-
Tier supply chain, including
manufacturers, distributors and
retailers.
• CPFR’s objective is to exchange
selected internal information to
provide for a reliable, longer term
future views of demand in the supply
chain.
• CPFR uses a cyclic and iterative
approach to derive consensus
forecasts. 1-37
38. 38
Web-Based Forecasting:
Steps in CPFR
• 1. Creation of a front-end partnership
agreement
• 2. Joint business planning
• 3. Development of demand forecasts
• 4. Sharing forecasts
• 5. Inventory replenishment
1-38
