Forecasting Methods
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Transcript of Forecasting Methods
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©The McGraw-Hill Companies, Inc., 2004
Operations Management
Forecasting
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©The McGraw-Hill Companies, Inc., 2004
• Demand Management
• Qualitative Forecasting Methods
• Simple & Weighted Moving Average
Forecasts
• Exponential Smoothing
• Simple Linear Regression
• Web-Based Forecasting
OBJECTIVES
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©The McGraw-Hill Companies, Inc., 2004
Demand Management
A
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand:
Raw Materials,Component parts,
Sub-assemblies, etc.
Independent Demand:
Finished Goods
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©The McGraw-Hill Companies, Inc., 2004
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
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©The McGraw-Hill Companies, Inc., 2004
Types of Forecasts• Qualitative (Judgmental)
• Quantitative
– Time Series Analysis
– Causal Relationships – Simulation
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©The McGraw-Hill Companies, Inc., 2004
Components of Demand
• Average demand for a period of time
• Trend• Seasonal element
• Cyclical elements
• Random variation
• Autocorrelation
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©The McGraw-Hill Companies, Inc., 2004
Finding Components of Demand
1 2 3 4
x
x xx
xx
x xx
xx
x x x
xxxxxx x x
xx
x x xx
xx
xx
x
xx
xx
xx
x
xx
xx
x
x
x
Year
S a l e s
Seasonal variation
Linear
Trend
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©The McGraw-Hill Companies, Inc., 2004
Qualitative Methods
Grass Roots
Market Research
Panel Consensus
Executive Judgment
Historical analogy
Delphi Method
Qualitative
Methods
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©The McGraw-Hill Companies, Inc., 2004
Delphi Methodl. Choose the experts to participate representing a
variety of knowledgeable people in different areas2. 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 theparticipants 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
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©The McGraw-Hill Companies, Inc., 2004
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 required4. Size of forecasting budget
5. Availability of qualified personnel
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©The McGraw-Hill Companies, Inc., 2004
Simple Moving Average Formula
F =A + A + A +...+A
nt t-1 t -2 t-3 t-n
• The simple moving average model assumes an
average is a good estimator of future behavior
• The formula for the simple moving average is:
Ft = Forecast for the coming period
N = Number of periods to be averagedA t-1 = Actual occurrence in the past period for
up to “n” periods
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©The McGraw-Hill Companies, Inc., 2004
Simple Moving Average Problem (1)
Week Demand
1 650
2 678
3 7204 785
5 859
6 920
7 850
8 7589 892
10 920
11 789
12 844
F = A + A + A +...+An
t t -1 t -2 t -3 t-n
Question: What are the 3-week and 6-week movingaverage forecasts fordemand?
Assume you only have 3weeks and 6 weeks ofactual demand data for therespective forecasts
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Week Demand 3-Week 6-Week
1 650
2 678
3 720
4 785 682.67
5 859 727.676 920 788.00
7 850 854.67 768.67
8 758 876.33 802.00
9 892 842.67 815.33
10 920 833.33 844.00
11 789 856.67 866.50
12 844 867.00 854.83
F4=(650+678+720)/3
=682.67
F7=(650+678+720
+785+859+920)/6
=768.67
Calculating the moving averages gives us:
©The McGraw-Hill Companies, Inc., 2004
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500
600
700
800
900
1000
1 2 3 4 5 6 7 8 9 10 11 12
Week
D e m a n d
Demand
3-Week
6-Week
Plotting the moving averages and comparing
them shows how the lines smooth out to reveal
the overall upward trend in this example
Note how the
3-Week issmoother than
the Demand,
and 6-Week is
even smoother
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©The McGraw-Hill Companies, Inc., 2004
Simple Moving Average Problem (2) Data
Week Demand1 820
2 775
3 680
4 655
5 620
6 600
7 575
Question: What is the 3
week moving average
forecast for this data? Assume you only have 3
weeks and 5 weeks of
actual demand data for
the respectiveforecasts
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©The McGraw-Hill Companies, Inc., 2004
Simple Moving Average Problem (2)
Solution
Week Demand 3-Week 5-Week
1 820
2 7753 680
4 655 758.33
5 620 703.33
6 600 651.67 710.00
7 575 625.00 666.00
F4=(820+775+680)/3
=758.33F6=(820+775+680
+655+620)/5
=710.00
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©The McGraw-Hill Companies, Inc., 2004
Weighted Moving Average Formula
F = w A + w A + w A + ...+ w At 1 t -1 2 t - 2 3 t -3 n t - n
w = 1i
i=1
n
While the moving average formula implies an equalweight being placed on each value that is being averaged,
the weighted moving average permits an unequal
weighting on prior time periods
wt = weight given to time period “t”
occurrence (weights must add to one)
The formula for the moving average is:
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©The McGraw-Hill Companies, Inc., 2004
Weighted Moving Average Problem (1)
Data
Weights:t-1 .5
t-2 .3
t-3 .2
Week Demand1 650
2 678
3 720
4
Question: Given the weekly demand and weights, what is
the forecast for the 4th period or Week 4?
Note that the weights place more emphasis on the
most recent data, that is time period “t-1”
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©The McGraw-Hill Companies, Inc., 2004
Weighted Moving Average Problem (1)
Solution
Week Demand Forecast
1 650
2 6783 720
4 693.4
F4 = 0.5(720)+0.3(678)+0.2(650)=693.4
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©The McGraw-Hill Companies, Inc., 2004
Weighted Moving Average Problem (2)
Data
Weights:
t-1 .7
t-2 .2t-3 .1
Week Demand
1 820
2 7753 680
4 655
Question: Given the weekly demand information and
weights, what is the weighted moving average forecast
of the 5th period or week?
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Weighted Moving Average Problem (2)
Solution
Week Demand Forecast
1 820
2 775
3 680
4 655
5 672
F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672
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©The McGraw-Hill Companies, Inc., 2004
Simple Linear Regression Model
Yt = a + bx
0 1 2 3 4 5 x (Time)
YThe simple linear regression
model seeks to fit a line
through various data over
time
Is the linear regression model
a
Yt is the regressed forecast value or dependentvariable in the model, a is the intercept value of the theregression line, and b is similar to the slope of the
regression line. However, since it is calculated with thevariability of the data in mind, its formulation is not asstraight forward as our usual notion of slope.
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©The McGraw-Hill Companies, Inc., 2004
Simple Linear Regression Formulas
for Calculating “a” and “b”
a = y - bx
b = xy - n(y)(x)
x -n(x2 2
)
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©The McGraw-Hill Companies, Inc., 2004
Simple Linear Regression Problem Data
Week Sales1 150
2 157
3 1624 166
5 177
Question: Given the data below, what is the simple linearregression model that can be used to predict sales in future
weeks?
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Week Week*Week Sales Week*Sales
1 1 150 1502 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)x - n(x
= 2499 - 5(162.4)(3) =
a = y - bx = 162.4 - (6.3)(3) =
2 2
) ( )55 5 96310
6.3
143.5
Answer: First, using the linear regression formulas, we
can compute “a” and “b”
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Yt = 143.5 + 6.3x
180
Perio
135
140
145
150
155
160
165
170
175
1 2 3 4 5
S a l e s
Sales
Forecast
The resulting regression model
is:
Now if we plot the regression generated forecasts against the
actual sales we obtain the following chart:
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©The McGraw-Hill Companies, Inc., 2004
Web-Based Forecasting: CPFR
Defined
• 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 futureviews of demand in the supply chain.
• CPFR uses a cyclic and iterative approach to derive
consensus forecasts.
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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