Chapter 5To accompanyQuantitative Analysis for Manag.docx

Chapter 5
To accompany
Quantitative Analysis for Management, Eleventh Edition, by
Render, Stair, and Hanna
Power Point slides created by Brian Peterson
Forecasting
Copyright ©2012 Pearson Education, Inc. publishing as Prentice
Hall
*
Learning Objectives
Understand and know when to use various families of
forecasting models.
Compare moving averages, exponential smoothing, and other
time-series models.
Seasonally adjust data.
Understand Delphi and other qualitative decision-making
approaches.
Compute a variety of error measures.
After completing this chapter, students will be able to:
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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
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IntroductionManagers 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
averagesExponential smoothingTrend projectionsLeast squares
regression analysis
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IntroductionEight steps to forecasting:
Determine the use of the forecast—what objective are we trying
to obtain?
Select the items or quantities that are to be forecasted.
Determine the time horizon of the forecast.
Select the forecasting model or models.
Gather the data needed to make the forecast.
Validate the forecasting model.
Make the forecast.
Implement the results.

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IntroductionThese 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.
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Forecasting Models
Forecasting Techniques
Figure 5.1
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
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Qualitative Models
Qualitative 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.
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Qualitative ModelsDelphi Method – This is an iterative group
process where (possibly geographically dispersed) respondents
provide input to decision makers.Jury of Executive Opinion –
This method collects opinions of a small group of high-level
managers, possibly using statistical models for analysis.Sales
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 Survey – Input is solicited from
customers or potential customers regarding their purchasing
plans.
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Time-Series ModelsTime-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.
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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.
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Scatter Diagrams
Wacker Distributors wants to forecast sales for three different
products (annual sales in the table, in units):
Table 5.1YEARTELEVISION SETSRADIOSCOMPACT DISC
PLAYERS12503001102250310100325032012042503301405250
34017062503501507250360160825037019092503802001025039
0190
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Scatter Diagram for TVs
Figure 5.2aSales appear to be constant over time
Sales = 250A good estimate of sales in year 11 is 250
televisions
330 –
250 –
200 –
150 –
100 –

50 –
||||||||||
012345678910
Time (Years)
Annual Sales of Televisions
(a)
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Scatter Diagram for RadiosSales appear to be 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.
Figure 5.2b
420 –
400 –
380 –
360 –
340 –
320 –
300 –
280 –
||||||||||

012345678910
Time (Years)
Annual Sales of Radios
(b)
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Scatter Diagram for CD PlayersThis trend line may not be
perfectly accurate because of variation from year to yearSales
appear to be increasingA forecast would probably be a larger
figure each year
Figure 5.2c
200 –
180 –
160 –
140 –
120 –
100 –
||||||||||
012345678910

Time (Years)
Annual Sales of CD Players
(c)
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Measures of Forecast AccuracyWe compare forecasted values
with actual values to see how well one model works or to
compare models.
Forecast error = Actual value – Forecast valueOne measure of
accuracy is the mean absolute deviation (MAD):
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Measures of Forecast Accuracy
Using a naïve forecasting model we can compute the MAD:
Table 5.2YEARACTUAL
SALES OF CD PLAYERSFORECAST SALESABSOLUTE
VALUE OF
ERRORS (DEVIATION), (ACTUAL – FORECAST)1110——
2100110|100 – 110| = 103120100|120 – 110| = 204140120|140 –
120| = 205170140|170 – 140| = 306150170|150 – 170| =
207160150|160 – 150| = 108190160|190 – 160| = 309200190|200
– 190| = 1010190200|190 – 200| = 1011—190—Sum of |errors|
= 160MAD = 160/9 = 17.8

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Measures of Forecast Accuracy
Table 5.2
Using a naïve forecasting model we can compute the
MAD:YEARACTUAL
SALES OF CD PLAYERSFORECAST SALESABSOLUTE
VALUE OF
ERRORS (DEVIATION), (ACTUAL – FORECAST)1110——
2100110|100 – 110| = 103120100|120 – 110| = 204140120|140 –
120| = 205170140|170 – 140| = 306150170|150 – 170| =
207160150|160 – 150| = 108190160|190 – 160| = 309200190|200
– 190| = 1010190200|190 – 200| = 1011—190—Sum of |errors|
= 160MAD = 160/9 = 17.8
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Measures of Forecast AccuracyThere are other popular measures
of forecast accuracy.The mean squared error:The mean absolute
percent error:And bias is the average error.
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Time-Series Forecasting ModelsA 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.
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Components of a Time-Series
A time series typically has four components:
Trend (T) is the gradual upward or downward movement of the
data over time.
Seasonality (S) is a pattern of demand fluctuations above or
below the trend line that repeats at regular intervals.
Cycles (C) are patterns in annual data that occur every several
years.
Random variations (R) are “blips” in the data caused by chance
or unusual situations, and follow no discernible pattern.
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Decomposition of a Time-Series
Average Demand over 4 Years
Trend Component
Actual Demand Line
Figure 5.3
Product Demand Charted over 4 Years, with Trend and
Seasonality Indicated
Time
Demand for Product or Service
||||

YearYearYearYear
1234
Seasonal Peaks
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Decomposition of a Time-SeriesThere are two general forms of
time-series models:The multiplicative model:
Demand = T x S x C x RThe additive model:
Demand = T + S + C + RModels may be combinations of these
two forms.Forecasters often assume errors are normally
distributed with a mean of zero.
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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.
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Moving AveragesMathematically:
Where:
= forecast for time period t + 1

= actual value in time period t
n= number of periods to average
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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).
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Table 5.3
MONTHACTUAL SHED SALESTHREE-MONTH MOVING
AVERAGEJanuary10February12March13April16May19June23J
uly26August30September28October18November16December14J
anuary—
(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
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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.Mathematically:
where
wi= weight for the ith observation
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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:
3 x Sales last month + 2 x Sales two months ago + 1 X Sales
three months agoWEIGHTS APPLIEDPERIOD3Last
month2Two months ago1Three months ago6

Sum of the weights
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Table 5.4
MONTHACTUAL SHED SALESTHREE-MONTH WEIGHTED
MOVING
AVERAGEJanuary10February12March13April16May19June23J
uly26August30September28October18November16December14J
anuary—
[(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

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Program 5.1A
Selecting the Forecasting Module in Excel QM
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Program 5.1B
Initialization Screen for Weighted Moving Average
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Program 5.1C
Weighted Moving Average in Excel QM for Wallace Garden
Supply
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Exponential SmoothingExponential smoothing is a type of

moving average that is easy to use and requires little record
keeping of data.
New forecast =Last period’s forecast
– Last period’s forecast)
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Mathematically:
Where:
Ft+1= new forecast (for time period t + 1)
Ft= pervious forecast (for time period t)
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.
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Exponential Smoothing ExampleIn January, February’s demand
for a certain car model was predicted to be 142.Actual February
what is the forecast for March?
New forecast (for March demand)= 142 + 0.2(153 – 142)
= 144.2 or 144 autosIf 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

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Selecting the Smoothing ConstantSelecting the appropriate
always to generate an accurate forecast.The general approach is
MAD.
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Table 5.5
UNLOADEDFORECAST
801751752168175.5 = 175.00 + 0.10(180 –
175)177.53159174.75 = 175.50 + 0.10(168 –
175.50)172.754175173.18 = 174.75 + 0.10(159 –
174.75)165.885190173.36 = 173.18 + 0.10(175 –
173.18)170.446205175.02 = 173.36 + 0.10(190 –
173.36)180.227180178.02 = 175.02 + 0.10(205 –
175.02)192.618182178.22 = 178.02 + 0.10(180 –
178.02)186.309?178.60 = 178.22 + 0.10(182 – 178.22)184.15
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Table 5.6
Absolute Deviations and MADs for the Port of Baltimore
ExampleQUARTERACTUAL TONNAGE
0.50ABSOLUTE
0.5011801755…..1755….2168175.57.5..177.59.5..3159174.7515
.75172.7513.754175173.181.82165.889.125190173.3616.64170.
4419.566205175.0229.98180.2224.787180178.021.98192.6112.6
18182178.223.78186.304.3..Sum of absolute
deviations82.4598.63MAD =Σ|deviations|=10.31MAD =12.33n
Best choice
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Port of Baltimore Exponential Smoothing Example in Excel QM
Program 5.2
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Annie Puciloski (AP) - upper callout, 2nd line: insert this initial
forecast, "delete" cells E10:H10
Exponential Smoothing with Trend AdjustmentLike 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.
Forecast including trend (FITt+1) = Smoothed forecast (Ft+1)
+ Smoothed Trend (Tt+1)
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Exponential Smoothing with Trend AdjustmentThe equation for
the trend correct
be given or estimated. Tt+1 is computed by:
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
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Selecting a Smoothing ConstantAs with exponential smoothing,
changes in
recent trend and tends to smooth out the trend.Values are
generally selected using a trial-and-error approach based on the
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Midwestern ManufacturingMidwest 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 = 0.3β = 0.4.
Table 5.7YEARELECTRICAL GENERATORS
SOLD200474200579200680200790200810520091422010122
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Midwestern ManufacturingAccording 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) = 74Step 2: Update the trend using:
Tt+1 = Tt + β(Ft+1 – FITt)
T2 = T1 + .4(F2 – FIT1)
= 0 + .4(74 – 74) = 0
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Midwestern ManufacturingStep 3: Calculate the trend-adjusted
exponential smoothing forecast (Ft+1) using the following:
FIT2 = F2 + T2
= 74 + 0 = 74
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Annie Puciloski (AP) - 2nd line: F s/b FIT sub t+1

Midwestern ManufacturingFor 2006 (period 3) we have:Step
1:F3 = FIT2 + 0.3(Y2 – FIT2)
= 74 + .3(79 – 74)
= 75.5Step 2: T3 = T2 + 0.4(F3 – FIT2)
= 0 + 0.4(75.5 – 74)
= 0.6Step 3: FIT3 = F3 + T3
= 75.5 + 0.6
= 76.1
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Midwestern Manufacturing Exponential Smoothing with Trend
Forecasts
Table 5.8Time (t)Demand (Yt)FITt+1 = Ft + 0.3(Yt– FITt)
Tt+1 = Tt + 0.4(Ft+1 – FITt)
FITt+1 = Ft+1 + Tt+1 1747407427974=74+0.3(74-74)0 =
0+0.4(74-74)74 = 74+038075.5=74+0.3(79-74)0.6 = 0+0.4(75.5-
74)76.1 = 75.5+0.649077.270=76.1+0.3(80-76.1)1.068 =
0.6+0.4(77.27-76.1)78.338 =
77.270+1.068510581.837=78.338+0.3(90-78.338)2.468 =
1.068+0.4(81.837-78.338)84.305 =
81.837+2.468614290.514=84.305+0.3(105-84.305)4.952 =
2.468+0.4(90.514-84.305)95.466 =
90.514+4.9527122109.426=95.466+0.3(142-95.466)10.536 =
4.952+0.4(109.426-95.466)119.962 =
109.426+10.5368120.573=119.962+0.3(122-119.962)10.780 =
10.536+0.4(120.573-119.962)131.353 = 120.573+10.780

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Midwestern Manufacturing
Program 5.3
Midwestern Manufacturing Trend-Adjusted Exponential
Smoothing in Excel QM
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Trend ProjectionsTrend 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.
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Trend Projection

The mathematical form is
Where
= predicted value
b0= intercept
b1= slope of the line
X= time period (i.e., X = 1, 2, 3, …, n)
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Program 5.4A
Excel Input Screen for Midwestern Manufacturing Trend Line
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Program 5.4B
Excel Output for Midwestern Manufacturing Trend Line
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Midwestern Manufacturing Company ExampleThe forecast
equation isTo 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 generatorsLikewise for X = 9
(sales in 2012)= 56.71 + 10.54(9)
= 151.57, or 152 generators

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Figure 5.4
Electrical Generators and the Computed Trend Line
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Program 5.5
Excel QM Trend Projection Model
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Seasonal VariationsRecurring 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.
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Eichler SuppliesEichler 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.

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Eichler Supplies Answering Machine Sales and Seasonal Indices
Table 5.9MONTHSALES DEMANDAVERAGE TWO- YEAR
DEMANDMONTHLY DEMANDAVERAGE SEASONAL
INDEXYEAR 1YEAR
2January8010090940.957February857580940.851March8090859
40.904April11090100941.064May115131123941.309June12011
0115941.223July100110105941.117August11090100941.064Sep
tember859590940.957October758580940.851November8575809
40.851December808080940.851Total average demand = 1,128
Average two-year demand
Average monthly demand
Seasonal index =
1,128
12 months
Average monthly demand = = 94
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Seasonal VariationsThe calculations for the seasonal indices are
Jan.
July
Feb.
Aug.

Mar.
Sept.
Apr.
Oct.
May
Nov.
June
Dec.
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Seasonal Variations with TrendWhen both trend and seasonal
components are present, the forecasting task is more
complex.Seasonal indices should be computed using a centered
moving average (CMA) approach.There are four steps in
computing CMAs:
Compute the CMA for each observation (where possible).
Compute the seasonal ratio = Observation/CMA for that
observation.
Average seasonal ratios to get seasonal indices.
If seasonal indices do not add to the number of seasons,
multiply each index by (Number of seasons)/(Sum of indices).
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Turner IndustriesThe following table shows Turner Industries’
quarterly sales figures for the past three years, in millions of
dollars:
Table 5.10QUARTERYEAR 1YEAR 2YEAR
3AVERAGE1108116123115.672125134142133.6731501591681
59.004141152165152.67Average131.00140.25149.50140.25

Definite trend
Seasonal pattern
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Turner IndustriesTo 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.
0.5(108) + 125 + 150 + 141 + 0.5(116)
4
CMA(q3, y1) = =
132.00
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Turner Industries
Compare the actual sales in quarter 3 to the CMA to find the
seasonal ratio:
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Turner Industries
Table 5.11YEARQUARTERSALESCMASEASONAL
RATIO1110821253150132.0001.1364141134.1251.0512111613
6.3750.8512134138.8750.9653159141.1251.1274152143.0001.0
6331123145.1250.8482142147.8750.96031684165
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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
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Turner Industries
Scatterplot of Turner Industries Sales Data and Centered
Moving Average
Figure 5.5

CMA
Original Sales Figures
200 –
150 –
100 –
50 –
0 –
Sales
||||||||||||
123456789101112
Time Period
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The Decomposition Method of Forecasting with Trend and

Seasonal ComponentsDecomposition is the process of isolating
linear trend and seasonal factors to develop more accurate
forecasts.There are five steps to decomposition:
Compute seasonal indices using CMAs.
Deseasonalize the data by dividing each number by its seasonal
index.
Find the equation of a trend line using the deseasonalized data.
Forecast for future periods using the trend line.
Multiply the trend line forecast by the appropriate seasonal
index.
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Deseasonalized Data for Turner IndustriesDevelop a forecast
using this trend and multiply the forecast by the appropriate
seasonal index.Find a trend line using the deseasonalized data:
b1 = 2.34b0 = 124.78
= 124.78 + 2.34X
= 124.78 + 2.34(13)
= 155.2(forecast before adjustment for seasonality)
x I1 = 155.2 x 0.85 = 131.92
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Deseasonalized Data for Turner Industries
Table 5.12SALES ($1,000,000s)SEASONAL
INDEXDESEASONALIZED SALES
($1,000,000s)1080.85127.0591250.96130.2081501.13132.74314
11.06133.0191160.85136.4711340.96139.5831591.13140.70815
21.06143.3961230.85144.7061420.96147.9171681.13148.67316
51.06155.660

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San Diego Hospital
A San Diego hospital used 66 months of adult inpatient days to
develop the following seasonal indices.
Table 5.13MONTHSEASONALITY
INDEXMONTHSEASONALITY
INDEXJanuary1.0436July1.0302February0.9669August1.0405M
arch1.0203September0.9653April1.0087October1.0048May0.99
35November0.9598June0.9906December0.9805
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San Diego Hospital
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)
Patient days= (9,532)(1.0436)
= 9,948 (trend and seasonal)
= 8,091 + 21.5X
= forecast patient days
X= time in months
where
Using this data they developed the following equation:
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San Diego Hospital
Program 5.6A
Initialization Screen for the Decomposition method in Excel
QM
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San Diego Hospital
Program 5.6B
Turner Industries Forecast Using the Decomposition Method in
Excel QM
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Using Regression with Trend and Seasonal ComponentsMultiple
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
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Regression with Trend and Seasonal Components

Program 5.7A
Excel Input for the Turner Industries Example Using Multiple
Regression
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Using Regression with Trend and Seasonal Components
Program 5.7B
Excel Output for the Turner Industries Example Using Multiple
Regression
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Using Regression with Trend and Seasonal ComponentsThe
resulting regression equation is: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.
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Monitoring and Controlling Forecasts
Tracking 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:
where

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Figure 5.6
Plot of Tracking Signals
Acceptable Range
Signal Tripped
Upper Control Limit
Lower Control Limit
0 MADs
+
–
Time
Tracking Signal
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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.
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Kimball’s Bakery
Quarterly sales of croissants (in thousands):TIME
PERIODFORECAST DEMANDACTUAL
DEMANDERRORRSFE|FORECAST|
| ERROR|CUMULATIVE ERRORMADTRACKING
SIGNAL110090–10–10101010.0–1210095–5–155157.5–
23100115+150153010.004110100–10–10104010.0–
15110125+15+5155511.0+0.56110140+30+35358514.2+2.5
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Adaptive SmoothingAdaptive smoothing is the computer
monitoring of tracking signals and self-adjustment if a limit is
adjusted when the computer detects an excessive amount of
variation.
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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.
Hall
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Chapter 3
To accompany
Decision Analysis
Hall
*
Learning Objectives
List the steps of the decision-making process.
Describe the types of decision-making environments.
Make decisions under uncertainty.
Use probability values to make decisions under risk.
Hall
Learning Objectives

Develop accurate and useful decision trees.
Revise probabilities using Bayesian analysis.
Use computers to solve basic decision-making problems.
Understand the importance and use of utility theory in decision
making.
Hall
Chapter Outline
3.1 Introduction
3.2 The Six Steps in Decision Making
3.3 Types of Decision-Making Environments
3.4 Decision Making under Uncertainty
3.5 Decision Making under Risk
3.6 Decision Trees
3.7 How Probability Values Are Estimated by Bayesian
Analysis
3.8 Utility Theory
Hall
IntroductionWhat is involved in making a good
decision?Decision theory is an analytic and systematic approach
to the study of decision making.A good decision is one that is
based on logic, considers all available data and possible
alternatives, and the quantitative approach described here.
Hall

The Six Steps in Decision Making
Clearly define the problem at hand.
List the possible alternatives.
Identify the possible outcomes or states of nature.
List the payoff (typically profit) of each combination of
alternatives and outcomes.
Select one of the mathematical decision theory models.
Apply the model and make your decision.
Hall
Thompson Lumber Company
Step 1 – Define the problem.The company is considering
expanding by manufacturing and marketing a new product –
backyard storage sheds.
Step 2 – List alternatives.Construct a large new plant.Construct
a small new plant.Do not develop the new product line at all.
Step 3 – Identify possible outcomes.The market could be
favorable or unfavorable.
Hall
Step 4 – List the payoffs.Identify conditional values for the
profits for large plant, small plant, and no development for the
two possible market conditions.
Step 5 – Select the decision model.This depends on the
environment and amount of risk and uncertainty.
Step 6 – Apply the model to the data.

Solution
and analysis are then used to aid in decision-making.
Hall
Table 3.1
Decision Table with Conditional Values for Thompson
LumberSTATE OF NATUREALTERNATIVEFAVORABLE
MARKET ($)UNFAVORABLE MARKET ($)Construct a large
plant200,000–180,000Construct a small plant100,000–20,000Do
nothing00
Hall

Types of Decision-Making Environments
Type 1:Decision making under certaintyThe decision maker
knows with certainty the consequences of every alternative or
decision choice.
Type 2:Decision making under uncertaintyThe decision maker
does not know the probabilities of the various outcomes.
Type 3:Decision making under riskThe decision maker knows
the probabilities of the various outcomes.
Hall
Decision Making Under Uncertainty
Maximax (optimistic)
Maximin (pessimistic)
Criterion of realism (Hurwicz)
Equally likely (Laplace)
Minimax regret
There are several criteria for making decisions under
uncertainty:
Hall

Maximax
Used to find the alternative that maximizes the maximum
payoff.Locate the maximum payoff for each alternative.Select
the alternative with the maximum number.
Table 3.2STATE OF NATUREALTERNATIVEFAVORABLE
MARKET ($)UNFAVORABLE MARKET ($)MAXIMUM IN A
ROW ($)Construct a large plant200,000–
180,000200,000Construct a small plant100,000–
20,000100,000Do nothing000
Maximax
Hall
Maximin
Used to find the alternative that maximizes the minimum
payoff.Locate the minimum payoff for each alternative.Select
the alternative with the maximum number.

MARKET ($)UNFAVORABLE MARKET ($)MINIMUM IN A
ROW ($)Construct a large plant200,000–180,000–
180,000Construct a small plant100,000–20,000–20,000Do
nothing000
Maximin
Hall
Criterion of Realism (Hurwicz)
This is a weighted average compromise between optimism and
value of 1 is perfectly optimistic, while a value of 0 is perfectly
pessimistic.Compute the weighted averages for each
alternative.Select the alternative with the highest value.
+ (1 –

Hall
Criterion of Realism (Hurwicz)For the large plant alternative
(0.8)(200,000) + (1 – 0.8)(–180,000) = 124,000For the small
(0.8)(100,000) + (1 – 0.8)(–20,000) = 76,000
MARKET ($)UNFAVORABLE MARKET ($)CRITERION OF
–
180,000124,000Construct a small plant100,000–
20,00076,000Do nothing000
Realism
Hall

Equally Likely (Laplace)
Considers all the payoffs for each alternative Find the average
payoff for each alternative.Select the alternative with the
highest average.
MARKET ($)UNFAVORABLE MARKET ($)ROW AVERAGE
($)Construct a large plant200,000–180,00010,000Construct a
small plant100,000–20,00040,000Do nothing000
Equally likely
Hall
Minimax Regret
Based on opportunity loss or regret, this is the difference
between the optimal profit and actual payoff for a
decision.Create an opportunity loss table by determining the

opportunity loss from not choosing the best
alternative.Opportunity loss is calculated by subtracting each
payoff in the column from the best payoff in the column.Find
the maximum opportunity loss for each alternative and pick the
alternative with the minimum number.
Hall
Minimax Regret
Table 3.6
Determining Opportunity Losses for Thompson LumberSTATE
OF NATUREFAVORABLE MARKET ($)UNFAVORABLE
MARKET ($)200,000 – 200,0000 – (–180,000)200,000 –
100,0000 – (–20,000)200,000 – 00 – 0
Hall
Minimax Regret

Table 3.7
Opportunity Loss Table for Thompson LumberSTATE OF
NATUREALTERNATIVEFAVORABLE MARKET
($)UNFAVORABLE MARKET ($)Construct a large
plant0180,000Construct a small plant100,00020,000Do
nothing200,0000
Hall
Minimax Regret
Table 3.8
Thompson’s Minimax Decision Using Opportunity LossSTATE
OF NATUREALTERNATIVEFAVORABLE MARKET
($)UNFAVORABLE MARKET ($)MAXIMUM IN A ROW
($)Construct a large plant0180,000180,000Construct a small
plant100,00020,000100,000Do nothing200,0000200,000
Minimax

Hall
Decision Making Under RiskThis is decision making when there
are several possible states of nature, and the probabilities
associated with each possible state are known.The most popular
method is to choose the alternative with the highest expected
monetary value (EMV).This is very similar to the expected
value calculated in the last chapter.
EMV (alternative i)= (payoff of first state of nature)
x (probability of first state of nature)
+ (payoff of second state of nature)
x (probability of second state of nature)
+ … + (payoff of last state of nature)
x (probability of last state of nature)
Hall
EMV for Thompson LumberSuppose each market outcome has a

probability of occurrence of 0.50.Which alternative would give
the highest EMV?The calculations are:
EMV (large plant)= ($200,000)(0.5) + (–$180,000)(0.5)
= $10,000
EMV (small plant)= ($100,000)(0.5) + (–$20,000)(0.5)
= $40,000
EMV (do nothing)= ($0)(0.5) + ($0)(0.5)
= $0
Hall
EMV for Thompson Lumber
MARKET ($)UNFAVORABLE MARKET ($)EMV ($)Construct
a large plant200,000–180,00010,000Construct a small
plant100,000–20,00040,000Do nothing000Probabilities0.500.50
Largest EMV

Hall
Expected Value of Perfect Information (EVPI)EVPI places an
upper bound on what you should pay for additional information.
EVPI = EVwPI – Maximum EMV
EVwPI is the long run average return if we have perfect
information before a decision is made.
EVwPI= (best payoff for first state of nature)
x (probability of first state of nature)
+ (best payoff for second state of nature)
x (probability of second state of nature)
+ … + (best payoff for last state of nature)
x (probability of last state of nature)
Hall
Expected Value of Perfect Information (EVPI)Suppose
Scientific Marketing, Inc. offers analysis that will provide
certainty about market conditions (favorable).Additional
information will cost $65,000.Should Thompson Lumber
purchase the information?

Hall
Expected Value of Perfect Information (EVPI)
Table 3.10
Decision Table with Perfect InformationSTATE OF
NATUREALTERNATIVEFAVORABLE MARKET
($)UNFAVORABLE MARKET ($)EMV ($)Construct a large
plant200,000-180,00010,000Construct a small plant100,000-
20,00040,000Do nothing000With perfect
information200,0000100,000Probabilities0.50.5
EVwPI
Hall

The maximum EMV without additional information is $40,000.
= $100,000 - $40,000
= $60,000
So the maximum Thompson should pay for the additional
information is $60,000.
Hall
The maximum EMV without additional information is $40,000.
= $100,000 - $40,000
= $60,000
Therefore, Thompson should not pay $65,000 for this
information.
So the maximum Thompson should pay for the additional
information is $60,000.
Hall

Expected Opportunity LossExpected opportunity loss (EOL) is
the cost of not picking the best solution.First construct an
opportunity loss table.For each alternative, multiply the
opportunity loss by the probability of that loss for each possible
outcome and add these together.Minimum EOL will always
result in the same decision as maximum EMV.Minimum EOL
will always equal EVPI.
Hall
Expected Opportunity Loss
EOL (large plant)= (0.50)($0) + (0.50)($180,000)
= $90,000
EOL (small plant)= (0.50)($100,000) + (0.50)($20,000)
= $60,000
EOL (do nothing)= (0.50)($200,000) + (0.50)($0)
= $100,000
MARKET ($)UNFAVORABLE MARKET ($)EOLConstruct a
large plant0180,00090,000Construct a small
plant100,00020,00060,000Do

nothing200,0000100,000Probabilities0.500.50
Minimum EOL
Hall
Sensitivity AnalysisSensitivity analysis examines how the
decision might change with different input data.For the
Thompson Lumber example:
P = probability of a favorable market
(1 – P) = probability of an unfavorable market
Hall
Sensitivity Analysis
EMV(Large Plant)= $200,000P – $180,000)(1 – P)
= $200,000P – $180,000 + $180,000P

= $380,000P – $180,000
EMV(Small Plant)= $100,000P – $20,000)(1 – P)
= $100,000P – $20,000 + $20,000P
= $120,000P – $20,000
EMV(Do Nothing)= $0P + 0(1 – P)
= $0
Hall
EMV (do nothing)
Figure 3.1
$300,000
$200,000
$100,000
0
–$100,000
–$200,000
EMV Values
EMV (large plant)

EMV (small plant)
Point 1
Point 2
.167
.615
1
Values of P
Hall
Point 1:
EMV(do nothing) = EMV(small plant)
Point 2:
EMV(small plant) = EMV(large plant)
Hall

Figure 3.1BEST
ALTERNATIVERANGE OF P
VALUESDo nothingLess than 0.167Construct a small
plant0.167 – 0.615Construct a large plantGreater than 0.615
$300,000
$200,000
$100,000
0
–$100,000
–$200,000
EMV Values
EMV (large plant)
EMV (small plant)
EMV (do nothing)
Point 1
Point 2
.167
.615

1
Values of P
Hall
Using Excel
Program 3.1A
Input Data for the Thompson Lumber Problem Using Excel QM
Hall

Using Excel
Program 3.1B
Output Results for the Thompson Lumber Problem Using Excel
QM
Hall
Decision TreesAny problem that can be presented in a decision
table can also be graphically represented in a decision
tree.Decision trees are most beneficial when a sequence of
decisions must be made.All decision trees contain decision
points or nodes, from which one of several alternatives may be
chosen.All decision trees contain state-of-nature points or
nodes, out of which one state of nature will occur.
Hall
Five Steps of
Decision Tree Analysis
Define the problem.

Structure or draw the decision tree.
Assign probabilities to the states of nature.
Estimate payoffs for each possible combination of alternatives
and states of nature.
Solve the problem by computing expected monetary values
(EMVs) for each state of nature node.
Hall
Structure of Decision TreesTrees start from left to right.Trees
represent decisions and outcomes in sequential order.Squares
represent decision nodes.Circles represent states of nature
nodes.Lines or branches connect the decisions nodes and the
states of nature.

Hall
Thompson’s Decision Tree
Figure 3.2
Favorable Market
Unfavorable Market
Favorable Market
Unfavorable Market
Do Nothing
Construct Large Plant
1

Construct Small Plant
2
A Decision Node
A State-of-Nature Node
Hall
Thompson’s Decision Tree
Favorable Market
Unfavorable Market
Favorable Market
Unfavorable Market
Do Nothing
Construct Large Plant
1
Construct Small Plant
2
Figure 3.3

Alternative with best EMV is selected
EMV for Node 1 = $10,000
= (0.5)($200,000) + (0.5)(–$180,000)
EMV for Node 2 = $40,000
= (0.5)($100,000)
+ (0.5)(–$20,000)
Payoffs
$200,000
–$180,000
$100,000
–$20,000
$0
(0.5)
(0.5)
(0.5)
(0.5)
Hall

Thompson’s Complex Decision Tree
Figure 3.4
First Decision Point
Second Decision Point
Favorable Market (0.78)
Unfavorable Market (0.22)

Large Plant
Small Plant
No Plant
6
7
Conduct Market Survey
Do Not Conduct Survey
Large Plant

Small Plant
No Plant
2
3
Large Plant
Small Plant
No Plant
4
5
1
Results
Favorable
Results
Negative
Survey (0.45)
Survey (0.55)
–$190,000
$190,000

$90,000
–$30,000
–$10,000
–$180,000
$200,000
$100,000
–$20,000
$0
–$190,000
$190,000
$90,000
–$30,000
–$10,000
Payoffs
Hall
1.Given favorable survey results,
EMV(node 2)= EMV(large plant | positive survey)
= (0.78)($190,000) + (0.22)(–$190,000) = $106,400
EMV(node 3)= EMV(small plant | positive survey)
= (0.78)($90,000) + (0.22)(–$30,000) = $63,600

EMV for no plant = –$10,000
2.Given negative survey results,
EMV(node 4)= EMV(large plant | negative survey)
= (0.27)($190,000) + (0.73)(–$190,000) = –$87,400
EMV(node 5)= EMV(small plant | negative survey)
= (0.27)($90,000) + (0.73)(–$30,000) = $2,400
EMV for no plant = –$10,000
Hall
3.Compute the expected value of the market survey,
EMV(node 1)= EMV(conduct survey)
= (0.45)($106,400) + (0.55)($2,400)
= $47,880 + $1,320 = $49,200
4.If the market survey is not conducted,
EMV(node 6)= EMV(large plant)
= (0.50)($200,000) + (0.50)(–$180,000) = $10,000
EMV(node 7)= EMV(small plant)
= (0.50)($100,000) + (0.50)(–$20,000) = $40,000
EMV for no plant = $0
5.The best choice is to seek marketing information.

Hall
Figure 3.5
First Decision Point
Second Decision Point

Large Plant
Small Plant
No Plant
Large Plant
Small Plant
No Plant

Large Plant
Small Plant
No Plant
–$190,000
$190,000
$90,000
–$30,000
–$10,000
–$180,000
$200,000
$100,000
–$20,000
$0
–$190,000
$190,000
$90,000
–$30,000
–$10,000
Payoffs

Conduct Market Survey
Do Not Conduct Survey
Results
Favorable
Results
Negative
Survey (0.45)

Survey (0.55)
$40,000
$2,400
$106,400
$49,200
$106,400
$63,600
–$87,400
$2,400
$10,000
$40,000
Hall
Expected Value of Sample InformationSuppose Thompson wants
to know the actual value of doing the survey.
=(EV with sample information + cost)
– (EV without sample information)
EVSI = ($49,200 + $10,000) – $40,000 = $19,200
Expected value
with sample

information, assuming
no cost to gather it
Expected value
of best decision
without sample
information
EVSI = –
Hall
How sensitive are the decisions to changes in the
probabilities?How sensitive is our decision to the probability of
a favorable survey result? That is, if the probability of a
favorable result (p = .45) where to change, would we make the
same decision? How much could it change before we would
make a different decision?

Hall
p =probability of a favorable survey result
(1 – p) =probability of a negative survey result
EMV(node 1)= ($106,400)p +($2,400)(1 – p)
= $104,000p + $2,400
We are indifferent when the EMV of node 1 is the same as the
EMV of not conducting the survey, $40,000
$104,000p + $2,400= $40,000
$104,000p= $37,600
p= $37,600/$104,000 = 0.36
If p<0.36, do not conduct the survey. If p>0.36, conduct the
survey.
Hall
Bayesian Analysis

There are many ways of getting probability data. It can be
based on:Management’s experience and intuition.Historical
data.Computed from other data using Bayes’ theorem.Bayes’
theorem incorporates initial estimates and information about the
accuracy of the sources.It also allows the revision of initial
estimates based on new information.
Hall

Calculating Revised Probabilities
In the Thompson Lumber case we used these four conditional
probabilities:
P (favorable market(FM) | survey results positive) = 0.78
P (unfavorable market(UM) | survey results positive) = 0.22
P (favorable market(FM) | survey results negative) = 0.27
P (unfavorable market(UM) | survey results negative) = 0.73But
how were these calculated?The prior probabilities of these
markets are:
P (FM) = 0.50
P (UM) = 0.50
Hall

Through discussions with experts Thompson has learned the
information in the table below.He can use this information and
Bayes’ theorem to calculate posterior probabilities.
Table 3.12STATE OF NATURERESULT OF
SURVEYFAVORABLE MARKET
(FM)UNFAVORABLE MARKET (UM)Positive (predicts
favorable market for product)P (survey positive | FM)
= 0.70P (survey positive | UM)
= 0.20Negative (predicts unfavorable market for product)P
(survey negative | FM)
= 0.30P (survey negative | UM)
= 0.80
Hall

Recall Bayes’ theorem:
For this example, A will represent a favorable market and B
will represent a positive survey.
where
Hall
P (FM | survey positive)P (UM | survey positive)
Hall

Table 3.13
Probability Revisions Given a Positive SurveyPOSTERIOR
PROBABILITYSTATE OF NATURECONDITIONAL
PROBABILITY P(SURVEY POSITIVE | STATE OF
NATURE)PRIOR PROBABILITYJOINT
PROBABILITYP(STATE OF NATURE | SURVEY
POSITIVE)FM0.70X 0.50=0.350.35/0.45 = 0.78UM0.20X
0.50=0.100.10/0.45 = 0.22P(survey results positive) =0.451.00
Hall

P (FM | survey negative)P (UM | survey negative)
Hall
Table 3.14
Probability Revisions Given a Negative SurveyPOSTERIOR
PROBABILITYSTATE OF NATURECONDITIONAL
PROBABILITY P(SURVEY NEGATIVE | STATE OF
NATURE)PRIOR PROBABILITYJOINT
PROBABILITYP(STATE OF NATURE | SURVEY

NEGATIVE)FM0.30X 0.50=0.150.15/0.55 = 0.27UM0.80X
0.50=0.400.40/0.55 = 0.73P(survey results positive) =0.551.00
Hall
Using Excel
Program 3.2A
Formulas Used for Bayes’ Calculations in Excel
Hall
Using Excel
Program 3.2B
Results of Bayes’ Calculations in Excel
Hall

Potential Problems Using Survey Results
We can not always get the necessary data for analysis.Survey
results may be based on cases where an action was
taken.Conditional probability information may not be as
accurate as we would like.
Hall
Utility Theory Monetary value is not always a true indicator of
the overall value of the result of a decision.The overall value of
a decision is called utility.Economists assume that rational
people make decisions to maximize their utility.
Hall

Utility Theory
Figure 3.6
Your Decision Tree for the Lottery Ticket
Heads (0.5)
Tails (0.5)
$5,000,000
$0
Accept Offer
Reject Offer
$2,000,000
EMV = $2,500,000
Hall

Utility Theory Utility assessment assigns the worst outcome a
utility of 0, and the best outcome, a utility of 1.A standard
gamble is used to determine utility values.When you are
indifferent, your utility values are equal.
Expected utility of alternative 2 =Expected utility of alternative
1
Utility of other outcome =(p)(utility of best outcome, which is
1)
+ (1 – p)(utility of the worst outcome, which is 0)
Utility of other outcome =(p)(1) + (1 – p)(0) = p
Hall
Standard Gamble for Utility Assessment
Figure 3.7
Best Outcome
Utility = 1
Worst Outcome
Utility = 0
Other Outcome
Utility = ?
(p)
(1 – p)

Alternative 1
Alternative 2
Hall
Investment ExampleJane Dickson wants to construct a utility
curve revealing her preference for money between $0 and
$10,000.A utility curve plots the utility value versus the
monetary value.An investment in a bank will result in
$5,000.An investment in real estate will result in $0 or
$10,000.Unless there is an 80% chance of getting $10,000 from
the real estate deal, Jane would prefer to have her money in the
bank.So if p = 0.80, Jane is indifferent between the bank or the
real estate investment.
Hall

Investment Example
Figure 3.8
Utility for $5,000 = U($5,000)= pU($10,000) + (1 – p)U($0)
= (0.8)(1) + (0.2)(0) = 0.8
p = 0.80
(1 – p) = 0.20
Invest in
Real Estate
Invest in Bank
$10,000
U($10,000) = 1.0
$0
U($0.00) = 0.0
$5,000
U($5,000) = p = 0.80
Hall

Investment Example
Utility for $7,000 = 0.90
Utility for $3,000 = 0.50We can assess other utility values in
the same way.For Jane these are:Using the three utilities for
different dollar amounts, she can construct a utility curve.
Hall
Utility Curve
Figure 3.9
U ($7,000) = 0.90
U ($5,000) = 0.80
U ($3,000) = 0.50
U ($0) = 0
1.0 –
0.9 –

0.8 –
0.7 –
0.6 –
0.5 –
0.4 –
0.3 –
0.2 –
0.1 –
|||||||||||
$0$1,000$3,000$5,000$7,000$10,000
Monetary Value
Utility
U ($10,000) = 1.0
Hall
Utility CurveJane’s utility curve is typical of a risk avoider.She
gets less utility from greater risk.She avoids situations where
high losses might occur.As monetary value increases, her utility
curve increases at a slower rate.
A risk seeker gets more utility from greater riskAs monetary

value increases, the utility curve increases at a faster rate.
Someone with risk indifference will have a linear utility curve.
Hall
Preferences for Risk
Figure 3.10
Monetary Outcome
Utility
Risk Avoider
Risk Indifference
Risk Seeker
Hall
Utility as a

Decision-Making CriteriaOnce a utility curve has been
developed it can be used in making decisions.This replaces
monetary outcomes with utility values.The expected utility is
computed instead of the EMV.
Hall
Utility as a
Decision-Making CriteriaMark Simkin loves to gamble.He plays
a game tossing thumbtacks in the air.If the thumbtack lands
point up, Mark wins $10,000.If the thumbtack lands point down,
Mark loses $10,000.Mark believes that there is a 45% chance
the thumbtack will land point up.Should Mark play the game
(alternative 1)?
Hall
Utility as a
Decision-Making Criteria

Figure 3.11
Decision Facing Mark Simkin
Tack Lands Point Up (0.45)
Alternative 1
Mark Plays the Game
Alternative 2
$10,000
–$10,000
$0
Tack Lands
Point Down (0.55)
Mark Does Not Play the Game
Hall
Utility as a

Decision-Making CriteriaStep 1– Define Mark’s utilities.
U (–$10,000) = 0.05
U ($0) = 0.15
U ($10,000) = 0.30Step 2 –Replace monetary values with
utility values.
E(alternative 1: play the game)= (0.45)(0.30) + (0.55)(0.05)
= 0.135 + 0.027 = 0.162
E(alternative 2: don’t play the game)= 0.15
Hall
Utility Curve for Mark Simkin
Figure 3.12
1.00 –
0.75 –

0.50 –
0.30 –
0.25 –
0.15 –
0.05 –
0 –
|||||
–$20,000–$10,000$0$10,000$20,000
Monetary Outcome
Utility
Hall
Utility as a

Decision-Making Criteria
Figure 3.13
Using Expected Utilities in Decision Making
Tack Lands Point Up (0.45)
Alternative 1
Mark Plays the Game
Alternative 2
0.30
0.05
0.15
Tack Lands
Point Down (0.55)
Don’t Play
Utility
E = 0.162

Hall
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.
Hall
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Chapter 1
To accompany
Introduction to Quantitative Analysis
Hall

*
Learning Objectives
Describe the quantitative analysis approach
Understand the application of quantitative analysis in a real
situation
Describe the use of modeling in quantitative analysis
Use computers and spreadsheet models to perform quantitative
analysis
Discuss possible problems in using quantitative analysis
Perform a break-even analysis
Hall
Chapter Outline
1.1Introduction
1.2What Is Quantitative Analysis?
1.3The Quantitative Analysis Approach

1.4How to Develop a Quantitative Analysis Model
1.5The Role of Computers and Spreadsheet Models in the
Quantitative Analysis Approach
1.6Possible Problems in the Quantitative Analysis Approach
1.7Implementation — Not Just the Final Step
Hall
IntroductionMathematical tools have been used for thousands of
years.Quantitative analysis can be applied to a wide variety of
problems.It’s not enough to just know the mathematics of a
technique.One must understand the specific applicability of the
technique, its limitations, and its assumptions.
Hall
Examples of Quantitative AnalysesIn the mid 1990s, Taco Bell
saved over $150 million using forecasting and scheduling
quantitative analysis models.NBC television increased revenues
by over $200 million between 1996 and 2000 by using
quantitative analysis to develop better sales plans.Continental

Airlines saved over $40 million in 2001 using quantitative
analysis models to quickly recover from weather delays and
other disruptions.
Hall
Quantitative analysis is a scientific approach to managerial
decision making in which raw data are processed and
manipulated to produce meaningful information.
What is Quantitative Analysis?
Raw Data
Meaningful
Information
Quantitative
Analysis
Hall

Quantitative factors are data that can be accurately calculated.
Examples include:Different investment alternativesInterest
ratesInventory levelsDemandLabor costQualitative factors are
more difficult to quantify but affect the decision process.
Examples include: The weatherState and federal
legislationTechnological breakthroughs.
What is Quantitative Analysis?
Hall
The Quantitative Analysis Approach
Figure 1.1
Implementing the Results
Analyzing the Results
Testing the

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