Rsh qam11 ch05 ge

Copyright © 2012 Pearson Education 5-1
Chapter 5
To accompany
Quantitative Analysis for Management, Eleventh Edition, Global Edition
by Render, Stair, and Hanna
Power Point slides created by Brian Peterson
Forecasting

5-2
Learning Objectives
1. Understand and know when to use various
families of forecasting models.
2. Compare moving averages, exponential
smoothing, and other time-series models.
3. Seasonally adjust data.
4. Understand Delphi and other qualitative
decision-making approaches.
5. Compute a variety of error measures.
After completing this chapter, students will be able to:After completing this chapter, students will be able to:

5-3
Chapter Outline
5.1 Introduction
5.2 Types of Forecasts
5.3 Scatter Diagrams and Time Series
5.4 Measures of Forecast Accuracy
5.5 Time-Series Forecasting Models
5.6 Monitoring and Controlling Forecasts

5-4
Introduction
 Managers are always trying to reduce
uncertainty and make better estimates of what
will happen in the future.
 This is the main purpose of forecasting.
 Some firms use subjective methods: seat-of-the
pants methods, intuition, experience.
 There are also several quantitative techniques,
including:
 Moving averages
 Exponential smoothing
 Trend projections
 Least squares regression analysis

5-5
Introduction
 Eight steps to forecasting:
1. Determine the use of the forecast—what
objective are we trying to obtain?
2. Select the items or quantities that are to be
forecasted.
3. Determine the time horizon of the forecast.
4. Select the forecasting model or models.
5. Gather the data needed to make the
forecast.
6. Validate the forecasting model.
7. Make the forecast.
8. Implement the results.

5-6
Introduction
 These steps are a systematic way of initiating,
designing, and implementing a forecasting
system.
 When used regularly over time, data is
collected routinely and calculations performed
automatically.
 There is seldom one superior forecasting
system.
 Different organizations may use different techniques.
 Whatever tool works best for a firm is the one that
should be used.

5-7
Regression
Analysis
Multiple
Regression
Moving
Average
Exponential
Smoothing
Trend
Projections
Decomposition
Delphi
Methods
Jury of Executive
Opinion
Sales Force
Composite
Consumer
Market Survey
Time-Series
Methods
Qualitative
Models
Causal
Methods
Forecasting Models
Forecasting
Techniques
Figure 5.1

5-8
Qualitative Models
 Qualitative modelsQualitative models incorporate judgmental
or subjective factors.
 These are useful when subjective factors
are thought to be important or when
accurate quantitative data is difficult to
obtain.
 Common qualitative techniques are:
 Delphi method.
 Jury of executive opinion.
 Sales force composite.
 Consumer market surveys.

5-9
Qualitative Models
 Delphi MethodDelphi Method – This is an iterative group
process where (possibly geographically
dispersed) respondentsrespondents provide input to decisiondecision
makers.makers.
 Jury of Executive OpinionJury of Executive Opinion – This method collects
opinions of a small group of high-level managers,
possibly using statistical models for analysis.
 Sales Force CompositeSales Force Composite – This allows individual
salespersons estimate the sales in their region
and the data is compiled at a district or national
level.
 Consumer Market SurveyConsumer Market Survey – Input is solicited from
customers or potential customers regarding their
purchasing plans.

5-10
Time-Series Models
 Time-series models attempt to predict
the future based on the past.
 Common time-series models are:
 Moving average.
 Exponential smoothing.
 Trend projections.
 Decomposition.
 Regression analysis is used in trend
projections and one type of
decomposition model.

5-11
Causal Models
 Causal modelsCausal models use variables or factors
that might influence the quantity being
forecasted.
 The objective is to build a model with
the best statistical relationship between
the variable being forecast and the
independent variables.
 Regression analysis is the most
common technique used in causal
modeling.

5-12
Scatter Diagrams
Wacker Distributors wants to forecast sales for
three different products (annual sales in the table,
in units):
YEAR
TELEVISION
SETS
RADIOS
COMPACT DISC
PLAYERS
1 250 300 110
2 250 310 100
3 250 320 120
4 250 330 140
5 250 340 170
6 250 350 150
7 250 360 160
8 250 370 190
9 250 380 200
10 250 390 190
Table 5.1

5-13
Scatter Diagram for TVs
Figure 5.2a
        
330 –
250 –
200 –
150 –
100 –
50 –
| | | | | | | | | |
0 1 2 3 4 5 6 7 8 9 10
Time (Years)
AnnualSalesofTelevisions
(a)
 Sales appear to be
constant over time
Sales = 250
 A good estimate of
sales in year 11 is
250 televisions

5-14
Scatter Diagram for Radios










increasing at a
constant rate of 10
radios per year
Sales = 290 + 10(Year)
 A reasonable
estimate of sales in
year 11 is 400
radios.
420 –
400 –
380 –
360 –
340 –
320 –
300 –
280 –
| | | | | | | | | |
0 1 2 3 4 5 6 7 8 9 10
Time (Years)
AnnualSalesofRadios
(b)
Figure 5.2b

5-15
Scatter Diagram for CD
Players







 
 This trend line may
not be perfectly
accurate because
of variation from
year to year
increasing
 A forecast would
probably be a
larger figure each
year
200 –
180 –
160 –
140 –
120 –
100 –
| | | | | | | | | |
0 1 2 3 4 5 6 7 8 9 10
Time (Years)
AnnualSalesofCDPlayers
(c)
Figure 5.2c

5-16
Measures of Forecast Accuracy
 We compare forecasted values with actual values
to see how well one model works or to compare
models.
Forecast error = Actual value – Forecast value
 One measure of accuracy is the mean absolutemean absolute
deviationdeviation (MADMAD):
n
∑=
errorforecast
MAD

5-17
Using a naïvenaïve forecasting model we can compute the
MAD:
YEAR
ACTUAL
SALES OF
CD
PLAYERS
FORECAST
SALES
ABSOLUTE VALUE OF
ERRORS (DEVIATION),
(ACTUAL – FORECAST)
1 110 — —
2 100 110 |100 – 110| = 10
3 120 100 |120 – 110| = 20
4 140 120 |140 – 120| = 20
5 170 140 |170 – 140| = 30
6 150 170 |150 – 170| = 20
7 160 150 |160 – 150| = 10
8 190 160 |190 – 160| = 30
9 200 190 |200 – 190| = 10
10 190 200 |190 – 200| = 10
11 — 190 —
Sum of |errors| = 160
MAD = 160/9 = 17.8
Table 5.2

5-18
YEAR
ACTUAL
SALES OF CD
PLAYERS FORECAST SALES
ABSOLUTE VALUE OF
ERRORS (DEVIATION),
(ACTUAL – FORECAST)
1 110 — —
2 100 110 |100 – 110| = 10
3 120 100 |120 – 110| = 20
4 140 120 |140 – 120| = 20
5 170 140 |170 – 140| = 30
6 150 170 |150 – 170| = 20
7 160 150 |160 – 150| = 10
8 190 160 |190 – 160| = 30
9 200 190 |200 – 190| = 10
10 190 200 |190 – 200| = 10
11 — 190 —
Sum of |errors| = 160
MAD = 160/9 = 17.8
Table 5.2
817
9
160errorforecast
.MAD ===
∑
n
Using a naïvenaïve forecasting model we can compute the
MAD:

5-19
 There are other popular measures of forecast
accuracy.
 The mean squared error:mean squared error:
n
∑=
2
error)(
MSE
 The mean absolute percent error:mean absolute percent error:
%MAPE 100
actual
error
n
∑
=
 And biasbias is the average error.

5-20
Time-Series Forecasting Models
 A time series is a sequence of evenly
spaced events.
 Time-series forecasts predict the future
based solely on the past values of the
variable, and other variables are
ignored.

5-21
Components of a Time-Series
A time series typically has four components:
1.1. TrendTrend (TT) is the gradual upward or downward
movement of the data over time.
2.2. SeasonalitySeasonality (SS) is a pattern of demand
fluctuations above or below the trend line that
repeats at regular intervals.
3.3. CyclesCycles (CC) are patterns in annual data that
occur every several years.
4.4. Random variationsRandom variations (RR) are “blips” in the data
caused by chance or unusual situations, and
follow no discernible pattern.

5-22
Decomposition of a Time-Series
Average Demand
over 4 Years
Trend
Component
Actual
Demand
Line
Time
DemandforProductorService
| | | |
Year Year Year Year
1 2 3 4
Seasonal Peaks
Figure 5.3
Product Demand Charted over 4 Years, with Trend
and Seasonality Indicated

5-23
Decomposition of a Time-Series
 There are two general forms of time-series
models:
 The multiplicative model:
Demand = T x S x C x R
 The additive model:
Demand = T + S + C + R
 Models may be combinations of these two
forms.
 Forecasters often assume errors are
normally distributed with a mean of zero.

5-24
Moving Averages
 Moving averagesMoving averages can be used when
demand is relatively steady over time.
 The next forecast is the average of the
most recent n data values from the time
series.
 This methods tends to smooth out short-
term irregularities in the data series.
n
n periodspreviousindemandsofSum
forecastaverageMoving =

5-25
Moving Averages
 Mathematically:
n
YYY
F nttt
t
11
1
+−−
+
+++
=
...
Where:
= forecast for time period t + 1
= actual value in time period t
n = number of periods to average
tY
1+tF

5-26
Wallace Garden Supply
 Wallace Garden Supply wants to
forecast demand for its Storage Shed.
 They have collected data for the past
year.
 They are using a three-month moving
average to forecast demand (n = 3).

5-27
Table 5.3
MONTH ACTUAL SHED SALES THREE-MONTH MOVING AVERAGE
January 10
February 12
March 13
April 16
May 19
June 23
July 26
August 30
September 28
October 18
November 16
December 14
January —
(12 + 13 + 16)/3 = 13.67
(13 + 16 + 19)/3 = 16.00
(16 + 19 + 23)/3 = 19.33
(19 + 23 + 26)/3 = 22.67
(23 + 26 + 30)/3 = 26.33
(26 + 30 + 28)/3 = 28.00
(30 + 28 + 18)/3 = 25.33
(28 + 18 + 16)/3 = 20.67
(18 + 16 + 14)/3 = 16.00
(10 + 12 + 13)/3 = 11.67

5-28
Weighted Moving Averages
 Weighted moving averagesWeighted moving averages use weights to put
more emphasis on previous periods.
 This is often used when a trend or other pattern is
emerging.
∑
∑=+
)(
))((
Weights
periodinvalueActualperiodinWeight
1
i
Ft
 Mathematically:
n
ntntt
t
www
YwYwYw
F
+++
+++
= +−−
+
...
...
21
1121
1
ere
wi = weight for the ith
observation

5-29
 Wallace Garden Supply decides to try a
weighted moving average model to forecast
demand for its Storage Shed.
 They decide on the following weighting
scheme:
WEIGHTS APPLIED PERIOD
3 Last month
2 Two months ago
1 Three months ago
6
3 x Sales last month + 2 x Sales two months ago + 1 X Sales three months ago
Sum of the weights

5-30
Table 5.4
MONTH ACTUAL SHED SALES
THREE-MONTH WEIGHTED
MOVING AVERAGE
January 10
February 12
March 13
April 16
May 19
June 23
July 26
August 30
September 28
October 18
November 16
December 14
January —
[(3 X 13) + (2 X 12) + (10)]/6 = 12.17
[(3 X 16) + (2 X 13) + (12)]/6 = 14.33
[(3 X 19) + (2 X 16) + (13)]/6 = 17.00
[(3 X 23) + (2 X 19) + (16)]/6 = 20.50
[(3 X 26) + (2 X 23) + (19)]/6 = 23.83
[(3 X 30) + (2 X 26) + (23)]/6 = 27.50
[(3 X 28) + (2 X 30) + (26)]/6 = 28.33
[(3 X 18) + (2 X 28) + (30)]/6 = 23.33
[(3 X 16) + (2 X 18) + (28)]/6 = 18.67
[(3 X 14) + (2 X 16) + (18)]/6 = 15.33

5-31
Program 5.1A
Selecting the Forecasting Module in Excel QM

5-32
ogram 5.1B
Initialization Screen for Weighted Moving Average

5-33
Program 5.1C
Weighted Moving Average in Excel QM for Wallace
Garden Supply

5-34
Exponential Smoothing
 Exponential smoothingExponential smoothing is a type of moving
average that is easy to use and requires little
record keeping of data.
ecast = Last period’s forecast
+ α(Last period’s actual demand
– Last period’s forecast)
Here α is a weight (or smoothing constantsmoothing constant) in
which 0≤α≤1.

5-35
Mathematically:
)( tttt FYFF −+=+ α1
ere:
Ft+1 = new forecast (for time period t + 1)
Ft = pervious forecast (for time period t)
α = smoothing constant (0 ≤ α ≤ 1)
Yt = pervious period’s actual demand
The idea is simple – the new estimate is the old
estimate plus some fraction of the error in the
last period.

5-36
Exponential Smoothing Example
 In January, February’s demand for a certain
car model was predicted to be 142.
 Actual February demand was 153 autos
 Using a smoothing constant of α = 0.20, what
is the forecast for March?
New forecast (for March demand) = 142 + 0.2(153 – 142)
= 144.2 or 144 autos
 If actual demand in March was 136 autos, the
April forecast would be:
New forecast (for April demand) = 144.2 + 0.2(136 – 144.2)
= 142.6 or 143 autos

5-37
Selecting the Smoothing Constant
 Selecting the appropriate value for α is key
to obtaining a good forecast.
 The objective is always to generate an
accurate forecast.
 The general approach is to develop trial
forecasts with different values of α and
select the α that results in the lowest MAD.

5-38
QUARTER
ACTUAL
TONNAGE
UNLOADED
FORECAST
USING α =0.10
FORECAST
USING α =0.50
1 180 175 175
2 168 175.5 = 175.00 + 0.10(180 – 175) 177.5
3 159 174.75 = 175.50 + 0.10(168 – 175.50) 172.75
4 175 173.18 = 174.75 + 0.10(159 – 174.75) 165.88
5 190 173.36 = 173.18 + 0.10(175 – 173.18) 170.44
6 205 175.02 = 173.36 + 0.10(190 – 173.36) 180.22
7 180 178.02 = 175.02 + 0.10(205 – 175.02) 192.61
8 182 178.22 = 178.02 + 0.10(180 – 178.02) 186.30
9 ? 178.60 = 178.22 + 0.10(182 – 178.22) 184.15
Table 5.5
Port of Baltimore Exponential Smoothing Forecast
for α=0.1 and α=0.5.

5-39
QUARTER
ACTUAL
TONNAGE
UNLOADED
FORECAST
WITH α =
0.10
ABSOLUTE
DEVIATIONS
FOR α = 0.10
FORECAST
WITH α = 0.50
ABSOLUTE
DEVIATIONS
FOR α = 0.50
1 180 175
5…..
175
5….
2 168 175.5
7.5..
177.5
9.5..
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
4.3..
Table 5.6
Best choiceBest choice
Absolute Deviations and MADs for the Port of
Baltimore Example

5-40
Port of Baltimore Exponential
Smoothing Example in Excel QM
Program 5.2

5-41
Exponential Smoothing with
Trend Adjustment
 Like all averaging techniques, exponential
smoothing does not respond to trends.
 A more complex model can be used that
adjusts for trends.
 The basic approach is to develop an
exponential smoothing forecast, and then
adjust it for the trend.
1) = Smoothed forecast (Ft+1)
+ Smoothed Trend (Tt+1)

5-42
Exponential Smoothing with
Trend Adjustment
 The equation for the trend correction uses a new
smoothing constant β .
 Tt must be given or estimated. Tt+1 is computed by:
)()1( 11 tttt FITFTT −+−= ++ ββ
where
Tt = smoothed trend for time period t
Ft = smoothed forecast for time period t
FITt = forecast including trend for time
period t
α =smoothing constant for forecasts
β = smoothing constant for trend

5-43
Selecting a Smoothing Constant
 As with exponential smoothing, a high value of β
makes the forecast more responsive to changes
in trend.
 A low value of β gives less weight to the recent
trend and tends to smooth out the trend.
 Values are generally selected using a trial-and-
error approach based on the value of the MAD for
different values of β.

5-44
Midwestern Manufacturing
 Midwest Manufacturing has a demand for
electrical generators from 2004 – 2010 as given in
the table below.
 To forecast demand, Midwest assumes:
 F1 is perfect.
 T1 = 0.
 α = 0.3
 β = 0.4.
YEAR ELECTRICAL GENERATORS SOLD
2004 74
2005 79
2006 80
2007 90
2008 105
2009 142
2010 122
Table 5.7

5-45
 According to the assumptions,
FIT1 = F1 + T1 = 74 + 0 = 74.
 Step 1: Compute Ft+1 by:
FITt+1 = Ft + α(Yt – FITt)
= 74 + 0.3(74-74) = 74
 Step 2: Update the trend using:
Tt+1 = Tt + β(Ft+1 – FITt)
T2 = T1 + .4(F2 – FIT1)
= 0 + .4(74 – 74) = 0

5-46
 Step 3: Calculate the trend-adjusted
exponential smoothing forecast (Ft+1)
using the following:
FIT2 = F2 + T2
= 74 + 0 = 74

5-47
 For 2006 (period 3) we have:
 Step 1: F3 = FIT2 + 0.3(Y2 – FIT2)
= 74 + .3(79 – 74)
= 75.5
 Step 2: T3 = T2 + 0.4(F3 – FIT2)
= 0 + 0.4(75.5 – 74)
= 0.6
 Step 3: FIT3 = F3 + T3
= 75.5 + 0.6
= 76.1

5-48
Midwestern Manufacturing Exponential
Smoothing with Trend Forecasts
Table 5.8
Time
(t)
Demand
(Yt)
FITt+1 = Ft + 0.3(Yt– FITt) Tt+1 = Tt + 0.4(Ft+1 – FITt) FITt+1 = Ft+1 + Tt+1
1 74 74 0 74
2 79 74=74+0.3(74-74) 0 = 0+0.4(74-74) 74 = 74+0
3 80 75.5=74+0.3(79-74) 0.6 = 0+0.4(75.5-74) 76.1 = 75.5+0.6
4 90 77.270=76.1+0.3(80-76.1) 1.068 = 0.6+0.4(77.27-76.1) 78.338 =
77.270+1.068
5 105 81.837=78.338+0.3(90-
78.338)
2.468 = 1.068+0.4(81.837-
78.338)
84.305 =
81.837+2.468
6 142 90.514=84.305+0.3(105-
84.305)
4.952 = 2.468+0.4(90.514-
84.305)
95.466 =
90.514+4.952
7 122 109.426=95.466+0.3(142-
95.466)
10.536 =
4.952+0.4(109.426-95.466)
119.962 =
109.426+10.536
8 120.573=119.962+0.3(122-
119.962)
10.780 =
10.536+0.4(120.573-
119.962)
131.353 =
120.573+10.780

5-49
Program 5.3
Midwestern Manufacturing Trend-Adjusted
Exponential Smoothing in Excel QM

5-50
Trend Projections
 Trend projection fits a trend line to a
series of historical data points.
 The line is projected into the future for
medium- to long-range forecasts.
 Several trend equations can be
developed based on exponential or
quadratic models.
 The simplest is a linear model developed
using regression analysis.

5-51
Trend Projection
The mathematical form is
XbbY 10 +=ˆ
Where
= predicted value
b0 = intercept
b1 = slope of the line
X = time period (i.e., X = 1, 2, 3, …, n)
Yˆ

5-52
Program 5.4A
Excel Input Screen for Midwestern Manufacturing
Trend Line

5-53
Program 5.4B
Excel Output for Midwestern Manufacturing Trend Line

5-54
Company Example
 The forecast equation is
XY 54107156 ..ˆ +=
 To project demand for 2011, we use the coding
system to define X = 8
(sales in 2011) = 56.71 + 10.54(8)
= 141.03, or 141 generators
 Likewise for X = 9
(sales in 2012) = 56.71 + 10.54(9)
= 151.57, or 152 generators

5-55
Figure 5.4
Electrical Generators and the Computed Trend Line

5-56
Program 5.5
Excel QM Trend Projection Model

5-57
Seasonal Variations
 Recurring variations over time may
indicate the need for seasonal
adjustments in the trend line.
 A seasonal index indicates how a
particular season compares with an
average season.
 When no trend is present, the seasonal
index can be found by dividing the
average value for a particular season by
the average of all the data.

5-58
Eichler Supplies
 Eichler Supplies sells telephone
answering machines.
 Sales data for the past two years has
been collected for one particular model.
 The firm wants to create a forecast that
includes seasonality.

5-59
Eichler Supplies Answering Machine
Sales and Seasonal Indices
MONTH
SALES DEMAND
AVERAGE TWO-
YEAR DEMAND
MONTHLY
DEMAND
AVERAGE
SEASONAL
INDEXYEAR 1 YEAR 2
January 80 100
90
94 0.957
February 85 75
80
94 0.851
March 80 90
85
94 0.904
April 110 90
100
94 1.064
May 115 131
123
94 1.309
June 120 110
115
94 1.223
July 100 110
105
94 1.117
August 110 90
100
94 1.064
September 85 95
90
94 0.957
Seasonal index =
Average two-year demand
Average monthly demand
Average monthly demand = = 94
1,128
12 months
Table 5.9

5-60
Seasonal Variations
 The calculations for the seasonal indices are
Jan. July969570
12
2001
=× .
,
1121171
12
2001
=× .
,
Feb. Aug.858510
12
2001
=× .
,
1060641
12
2001
=× .
,
Mar. Sept.909040
12
2001
=× .
,
969570
12
2001
=× .
,
Apr. Oct.1060641
12
2001
=× .
,
858510
12
2001
=× .
,
May Nov.1313091
12
2001
=× .
,
858510
12
2001
=× .
,
June Dec.1222231
12
2001
=× .
,
858510
12
2001
=× .
,

5-61
Seasonal Variations with Trend
 When both trend and seasonal components are
present, the forecasting task is more complex.
 Seasonal indices should be computed using a
centered moving averagecentered moving average (CMACMA) approach.
 There are four steps in computing CMAs:
1. Compute the CMA for each observation
(where possible).
2. Compute the seasonal ratio =
Observation/CMA for that observation.
3. Average seasonal ratios to get seasonal
indices.
4. If seasonal indices do not add to the number
of seasons, multiply each index by (Number
of seasons)/(Sum of indices).

5-62
Turner Industries
 The following table shows Turner Industries’
quarterly sales figures for the past three years, in
millions of dollars:
QUARTER YEAR 1 YEAR 2 YEAR 3 AVERAGE
1 108 116 123 115.67
2 125 134 142 133.67
3 150 159 168 159.00
4 141 152 165 152.67
Average 131.00 140.25 149.50 140.25
Table 5.10 Definite trend
Seasonal
pattern

5-63
Turner Industries
 To calculate the CMA for quarter 3 of year 1 we
compare the actual sales with an average quarter
centered on that time period.
 We will use 1.5 quarters before quarter 3 and 1.5
quarters after quarter 3 – that is we take quarters
2, 3, and 4 and one half of quarters 1, year 1 and
quarter 1, year 2.
CMA(q3, y1) = = 132.00
0.5(108) + 125 + 150 + 141 + 0.5(116)
4

5-64
Turner Industries
Compare the actual sales in quarter 3 to the CMA to
find the seasonal ratio:
1361
132
1503quarterinSales
ratioSeasonal .
CMA
===

5-65
Turner Industries
YEAR QUARTER SALES CMA SEASONAL RATIO
1 1 108
2 125
3 150 132.000 1.136
4 141 134.125 1.051
2 1 116 136.375 0.851
2 134 138.875 0.965
3 159 141.125 1.127
4 152 143.000 1.063
3 1 123 145.125 0.848
2 142 147.875 0.960
3 168
4 165
Table 5.11

5-66
Turner Industries
There are two seasonal ratios for each quarter so
these are averaged to get the seasonal index:
Index for quarter 1 = I1 = (0.851 + 0.848)/2 = 0.85

5-67
Turner Industries
Scatterplot of Turner Industries Sales Data and
Centered Moving Average






 


 
CMA
Original Sales Figures
200 –
150 –
100 –
50 –
0 –
Sales
| | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12
Time Period
Figure 5.5

5-68
The Decomposition Method of Forecasting
with Trend and Seasonal Components
 DecompositionDecomposition is the process of isolating linear
trend and seasonal factors to develop more
accurate forecasts.
 There are five steps to decomposition:
1. Compute seasonal indices using CMAs.
2. Deseasonalize the data by dividing each
number by its seasonal index.
3. Find the equation of a trend line using the
deseasonalized data.
4. Forecast for future periods using the trend
line.
5. Multiply the trend line forecast by the
appropriate seasonal index.

5-69
Deseasonalized Data for Turner
Industries
 Find a trend line using the deseasonalized data:
b1 = 2.34 b0 = 124.78
 Develop a forecast using this trend and multiply
the forecast by the appropriate seasonal index.
Yˆ = 124.78 + 2.34X
= 124.78 + 2.34(13)
= 155.2 (forecast before adjustment for
seasonality)
Yˆ x I1 = 155.2 x 0.85 = 131.92

5-70
Deseasonalized Data for Turner
Industries
SALES
($1,000,000s)
SEASONAL
INDEX
DESEASONALIZED
SALES ($1,000,000s)
108 0.85 127.059
125 0.96 130.208
150 1.13 132.743
141 1.06 133.019
116 0.85 136.471
134 0.96 139.583
159 1.13 140.708
152 1.06 143.396
123 0.85 144.706
142 0.96 147.917
168 1.13 148.673
165 1.06 155.660
Table 5.12

5-71
San Diego Hospital
A San Diego hospital used 66 months of adult
inpatient days to develop the following seasonal
indices.
MONTH SEASONALITY INDEX MONTH SEASONALITY INDEX
January 1.0436 July 1.0302
February 0.9669 August 1.0405
March 1.0203 September 0.9653
April 1.0087 October 1.0048
May 0.9935 November 0.9598
June 0.9906 December 0.9805
Table 5.13

5-72
San Diego Hospital
Using this data they developed the following
equation:
Yˆ = 8,091 + 21.5X
where
Yˆ= forecast patient days
X = time in months
Based on this model, the forecast for patient days
for the next period (67) is:
Patient days = 8,091 + (21.5)(67) = 9,532 (trend only)
days = (9,532)(1.0436)
= 9,948 (trend and seasonal)

5-73
San Diego Hospital
Program 5.6A
Initialization Screen for the Decomposition method
in Excel QM

5-74
San Diego Hospital
Program 5.6B
Turner Industries Forecast Using the Decomposition
Method in Excel QM

5-75
Using Regression with Trend
and Seasonal Components
 Multiple regressionMultiple regression can be used to forecast both
trend and seasonal components in a time series.
 One independent variable is time.
 Dummy independent variables are used to represent the
seasons.
 The model is an additive decomposition model:
where
X1 = time period
X2 = 1 if quarter 2, 0 otherwise
44332211 XbXbXbXbaY ++++=ˆ

5-76
Regression with Trend and
Seasonal Components
Program 5.7A
Excel Input for the Turner Industries Example Using
Multiple Regression

5-77
Program 5.7B
Excel Output for
the Turner
Industries
Example Using
Multiple
Regression

5-78
 The resulting regression equation is:
4321 130738715321104 XXXXY .....ˆ ++++=
 Using the model to forecast sales for the first two
quarters of next year:
 These are different from the results obtained
using the multiplicative decomposition method.
 Use MAD or MSE to determine the best model.
13401300738071513321104 =++++= )(.)(.)(.)(..ˆY
15201300738171514321104 =++++= )(.)(.)(.)(..ˆY

5-79
Monitoring and Controlling Forecasts
 Tracking signalsTracking signals can be used to monitor
the performance of a forecast.
 A tracking signal is computed as the
running sum of the forecast errors (RSFE),
and is computed using the following
equation:
MAD
RSFE
=signalTracking
n
∑=
errorforecast
MAD
where

5-80
Acceptable
Range
Signal Tripped
Upper Control Limit
Lower Control Limit
0 MADs
+
–
Time
Figure 5.6
Tracking Signal
Plot of Tracking Signals

5-81
 Positive tracking signals indicate demand is
greater than forecast.
 Negative tracking signals indicate demand is less
than forecast.
 Some variation is expected, but a good forecast
will have about as much positive error as
negative error.
 Problems are indicated when the signal trips
either the upper or lower predetermined limits.
 This indicates there has been an unacceptable
amount of variation.
 Limits should be reasonable and may vary from
item to item.

5-82
Kimball’s Bakery
Quarterly sales of croissants (in thousands):
TIME
PERIOD
FORECAST
DEMAND
ACTUAL
DEMAND ERROR RSFE
|FORECAST |
| ERROR |
CUMULATIVE
ERROR MAD
TRACKING
SIGNAL
1 100 90 –10 –10 10 10 10.0 –1
2 100 95 –5 –15 5 15 7.5 –2
3 100 115 +15 0 15 30 10.0 0
4 110 100 –10 –10 10 40 10.0 –1
5 110 125 +15 +5 15 55 11.0 +0.5
6 110 140 +30 +35 35 85 14.2 +2.5
214
6
85errorforecast
.MAD ===
∑
n
MADs5.2
2.14
35
signalTracking ===
MAD
RSFE

5-83
Adaptive Smoothing
 Adaptive smoothingAdaptive smoothing is the computer
monitoring of tracking signals and self-
adjustment if a limit is tripped.
 In exponential smoothing, the values of α
and β are adjusted when the computer
detects an excessive amount of variation.

5-84
Copyright
All rights reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted, in
any form or by any means, electronic, mechanical,
photocopying, recording, or otherwise, without the prior
written permission of the publisher. Printed in the United
States of America.

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