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• What is Forecasting?
• The Forecasting Process
• Types of Forecasting Methods
• Time Series Forecasting Models
• Causal Models
• Measuring Forecast Accuracy
• Collaborative Forecasting and
Demand Planning
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• Forecasting drives all other business decisions
• Planning requires organizing resources in anticipation of the forecast
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Planning involves the following decisions:
1. Scheduling existing resource
2. Determining future resource needs
3. Acquiring new resources
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Demand management is the process of influencing demand
– promotional campaigns, advertisements, etc.
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Every organizational function relies on forecasting for numerous things
• Marketing
– estimates of demand, future trends
• Finance
– set budgets, predict stock prices
• Operations
– capacity planning, scheduling, inventory levels
• Sourcing
– make purchasing decisions, select suppliers
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Demand forecast affects the plans made by each member of the supply chain
• Independent forecasting among supply chain members
– causes a mismatch between supply and demand
– gives rise to the bullwhip effect
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1. Forecasts are rarely perfect
2. Forecasts are more accurate for groups than for individual items
3. Forecasts are more accurate for shorter than longer time horizons
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1. Decide what to forecast
2. Analyze appropriate data
• common patterns include:
– Level or horizontal
– Trend
– Seasonality
– Cycles
• in addition to patterns, data contain random variation
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3. Select the forecasting model
– select the model best suited for the identified data pattern
4. Generate the forecast
5. Monitor forecast accuracy
– measure forecast error
– use to improve the forecast process
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The following factors should be considered when selecting a forecasting method:
• Amount and type of available data
• Degree of accuracy required
• Length of forecast horizon
• Patterns in the data
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There are two groups of forecasting methods:
• Qualitative
– based on subjective opinions
– often called judgmental methods
• Quantitative
– based on mathematical modeling
– objective and consistent
– can handle large amounts of data and uncover complex relationships
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Qualitative methods are useful when identifying customer buying patterns, expectations, and estimating sales of new products
• Executive Opinion
– a group decision-making process, subject to bias
• Market Research
– surveys and interviews used to collect preferences
• The Delphi Method
– a consensus is developed from anonymously contributed expert information
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Quantitative methods are based on mathematical concepts
Two categories:
• Time Series Models
– generate the forecast from an analysis of a
“time series” of the data
• Causal Models
– assume that the variable being forecast is related to other variables in the environment
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A time series is a listing of data points of the variable being forecast over time
Models include:
• Mean
• Moving Averages
• Exponential Smoothing
• Trend Adjusted Exponential Smoothing
A Seasonality Adjustment can also be applied
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Forecast is made by taking an average:
F t+1
=

D t n where: F t+1
= forecast of demand for next period
D t
= demand for current period n = # of data points
– appropriate for a level data pattern
– forecasts become more stable over time
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Given the following sales for a drill over the past 5 weeks:
Week
1
2
5
6
3
4
Sales
8
10
9
12
10
What is the forecast for week 6?
F t+1
=

D t n
F
6
= [8+10+9+12+10 ] /5 = 9.8 ≈ 10
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Forecast is made by averaging a specified number, n, of the most recent data:
F t+1
=

D t where: F t+1 n
= forecast of demand for next period
D t
= demand for current period n = # of data points in the moving average
– appropriate for a level data pattern
– forecast becomes more responsive as n decreases
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Given the following sales for over 4 months:
Month Sales
Jan 38
Feb 27
March 42
April 42
May
What is the forecast for
May using a three-period moving average?

F t+1
=
D t n
F
May
= [27+42+42 ] /3 = 37
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All data are weighted equally with a simple moving average (weight = 1/n)
• Weighted Moving Average
– computation is the same as a simple moving average except that managers have the option of specifying the weights assigned to data points
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A weighted average procedure is used to obtain a forecast:
F t+1
=

D t

( 1
 
) F t where: F t+1
= forecast of demand for next period
D t
= actual value for current period
F t
= forecast for current period

= smoothing coefficient (between 0 and 1) demand changes
– must set forecast for initial period
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Café Nervosa forecast a monthly usage of cream to be 24 gallons in May. The actual usage in May was 28 gallons. What is the forecast for June given

= 0.7 ?
F t+1
=

D t

( 1
 
) F t
F
June
= (0.70)(28) + (0.30)(24)
= 26.8 gallons
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Forecast is modified to account for a trend in the data
FIT t+1
= F t+1
+ T t+1
T t+1
=

( F t

1

F t
)

( 1
 
) T t where : FIT t+1
= forecast including trend for next period
F t+1
= unadjusted forecast for next period
T t+1
= trend factor for next period
T t
= trend factor for current period
F t
= forecast for current period

= smoothing coefficient (between 0 and 1)
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Given a demand for December of 18 and a demand for January of 20, what is the trend
 
Unadjusted: F t+1
=

D t

( 1
 
) F t
= 0.3(20) + (1 – 0.3)(18) = 18.6
Trend: T t+1
=

( F t

1

F t
)

( 1
 
) T t
= 0.4(18.6 – 18) + (1 – 0.4)(0) = 0.24
Adjusted: FIT t+1
= F t+1
+ T t+1
= 18.6 + 0.24 = 18.84
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The forecast can be adjusted to reflect the amount by which a season is above or below average
Steps:
1. Compute average demand for each season
– total annual demand divided by the
# of seasons
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2. Compute a seasonal index for each season
– divide the demand for each season by the average demand for each year
– average across years available
3. Adjust the average forecast for next year by the seasonal index
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Given the following table of customer traffic for an ice cream shop experiencing seasonal fluctuations.
# Customers (thousands)
Quarter Year 1 Year 2
Fall 14 15
Winter 25 26
Spring 20 20
Summer 33 35
Total 92 96
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A forecast of 98,000 customers has been generated for next year
What is the seasonally adjusted forecast per quarter?
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Step 1
– Compute the average demand for each season
Year 1:
92
4

23
Year 2:
96
4

24
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Step 2
– Compute a seasonal index for each season
Quarter
Fall
Winter
Spring
Summer
Seasonal Indexes
Year 1 Year 2
14
23

0 .
61
25
23

1 .
09
20
23

0 .
87
33
23

1 .
43
15
24

0 .
63
26
24

1 .
08
20
24

0 .
83
35
24

1 .
42
Average
Index
0.620
1.085
0.850
1.425
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Step 3
– Seasonally adjust the average forecast for next year
Next year forecast = 98,000  Average = 24,500
Number of Customers
Quarter Seasonally Adjusted Forecast
Fall 24,500 (0.620) = 15,190
Winter 24,500 (1.085) = 26, 583
Spring 24,500 (0.850) = 20,825
Summer 24,500 (1.425) = 34,913
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Assume that the variable being forecast is related to other variables in the environment
• Linear Regression
– a forecasting model that assumes a straight line relationship between an independent variable and a single dependent variable
• Multiple Regression
– extends linear regression by looking at a relationship between an independent variable and multiple dependent variables
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The straight line equation for the model is:
Y = a + bX where: Y = dependent variable
X = independent variable a = Y intercept of the straight line b = slope of the straight line
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1. Compute parameter b: b =
XY - nXY
∑X 2 – nX 2
where Y = average of the Y values
X = average of the X values n = # of data points
2. Compute parameter a: a = Y – b X
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3. Substitute values for a and b in the equation:
Y = a + b X
4. Generate a forecast for the dependent variable (Y)
– substitute the appropriate value for X
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Given the following four months of pizza sales and advertising dollars:
Pizza Sales Advertising $
58 135
43
62
90
145
68 145
Use linear regression to estimate pizza sales if $150 is spent on advertising next month
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Dependent Variable Y = Pizza Sales
Independent Variable X = Advertising $
Total
Y
58
43
62
68
231
X
135
90
145
145
515
XY
7,830
3,870
8,990
9,860
30,550
X 2
18,225
8,100
21,025
21,025
68,375
Y 2
3,364
1,849
3,844
4,624
13,681 compute X = 515/4 = 128.75 and Y = 231/4 = 57.75
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1. Compute parameter b: b =
[ XY – nXY ]
[ ∑X 2 – nX 2 ]
=
30,550 – 4(128.75)(57.75)
= 0.391
68,375 – 4(128.75) 2
2. Compute parameter a: a = Y – bX = 57.75 – (0.391)(128.75) = 7.48
3. Substitute a and b: Y = 7.48 + 0.391X
4. Forecast: Y = 7.48 +0.391(150) = 66.13 pizzas
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Multiple regression looks at the relationship between the independent variable and multiple dependent variables:
Y = β
0
+ β
1
X
1
+ β
2
X
2
+…+ β k
X k where: Y = dependent variable
X
1
…X k
β
0
β
1
… β k
= independent variables
= Y intercept
= coefficients that represent the influence of the independent variables on the dependent variable
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Two measures to help determine how our forecasting methods are performing:
• Mean Absolute Deviation (MAD)
• Mean Square Error (MSE)
First measure forecast error: e t
= D t
– F t where: e t
D t
F t
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= forecast error for period t
= actual demand for period t
= forecast for period t
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• MAD is the average of the sum of the absolute errors:
MAD =

Actual

Forecast n
• MSE is the average of the squared errors:
MSE =
 
Actual

Forecast

2 n
– for both measures, select the forecasting method that provides the lowest value
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Given the following two sets of forecasts:
Method A
Month Sales Forecast e |e|
Jan
Feb
Mar
Apr
May
Total
40
28
41
41
39
42
29
39
38
41
-2
1
2
3
-2
2
2
1
2
3
2 e
4
1
4
9
4
2
10 22
Forecast
44
31
38
42
40
Method B e
-4
-3
3
-1
-1
-6
|e| e 2
4
3
3
1
1
12
16
9
9
1
1
36
Calculate the MAD and MSE for both methods
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• MAD =

Actual

Forecast n
MAD
A
=
10
4

2 .
5 MAD
B
=
12

3
4
• MSE =
 
Actual

Forecast

2 n
MSE
A
=
22

5 .
5
4
MSE
B
=
36
4

9
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Two common processes:
• Collaborative Planning, Forecasting and Replenishment (CPFR)
• Sales and Operations Planning (S&OP)
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CPFR is a collaborative process of developing joint forecasts and plans with supply chain partners
Five-Step Process:
1. Create joint objectives
2. Develop a business plan
3. Create a joint forecast
4. Agree on replenishment strategies
5. Agree on a technology partner to bring
CPFR to fruition
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S&OP is a collaborative process for generating forecasts that all functional areas agree upon
Five-Step Process:
1. Generate quantitative sales forecast
2. Marketing adjusts the forecast
3. Operations checks forecast against existing capability
4. Marketing, operations, and finance jointly review forecast and resource issues
5. Executives finalize forecast and capacity decisions
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1. Forecasting is the process of attempting to predict future events. Planning is the process of selecting actions in anticipation of the forecast.
2. There are three principles of forecasting: (a) forecasts are rarely perfect; (b) forecasts are more accurate for aggregated items than for individual items; and (c) forecasts are more accurate for shorter than longer time horizons.
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3. Data are composed of patterns and randomness.
Four of the most common patterns are level, trend, seasonality, and cycle.
4. Forecasting methods can be divided into qualitative and quantitative. Qualitative methods are subjective and based on objectives. Quantitative methods are mathematically based, are objective and consistent.
5. Quantitative forecasting methods can be time series models and causal models.
6. A. Time series models generate the forecast by identifying and analyzing patterns in a “time series” of the data.
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6. b. Causal models assume that the variable being forecast is related to other variables.
7. CPFR is a collaborative process of developing joint forecasts and plans with supply chain partners, rather than doing them independently.
8. Sales and Operations Planning (S&OP) is intended to match supply and demand through financial collaboration between marketing, operations, and finance, in order to ensure that supply can meet demand requirements.
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