collier 11
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1Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
2007 Thomson o!th"#estern
Operations Management, 2e/Ch. 11 For
ecasting and Demand Planning
2007 Thomson o!th"#estern
Forecasting and
Demand Planning
CHAPTER 11
DAVID A. COLLIER
AND
JAMES R. EVANS
OPERATIONS
MANAGEMENTGoods, Services and Value Chains
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2Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Chapter 11 Learning Objectives1. To understand the need for forecasts andthe implications of information technologyfor forecasting in the value chain.
2. To understand the basic elements offorecasting, namely, the choice ofplanning horizon, dierent types of data
patterns, and ho! to calculate forecastingerrors.
". To be a!are of dierent forecasting
approaches and methods.
#. To understand basic time$seriesforecasting methods, be a!are of more
advanced methods, and use spreadsheetmodels to ma%e forecasts.
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&.To learn the basic ides and method of regression
analysis.
'.To understand the role of human judgment inforecasting and !hen judgmental forecasting is
most appropriate.
(.To %no! that judgment and )uantitative forecastmethodologies can complement one another,and therefore improve overall forecast accuracy.
Chapter 11 Learning Objectives
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Chapter 11 *orecasting and +emand lanning
- Forecasting is the process ofprojecting the values of one or morevariables into the future.
- Poor forecastingcan result in poorinventory and stang decisions,resulting in part shortages,
inade)uate customer service, andmany customer complaints.
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5Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Chapter 11 *orecasting and +emand lanning- /any 0rms integrate forecasting !ithvalue chain and capacity
management systems to ma%ebetter operational decisions. oodforecasting and demand planningsystems result in
higher capacity utilization,
reduced inventories and costs,
more ecient process
performance, more e3ibility,
improved customer service,and
increased ro0t mar ins.
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Chapter 11 *orecasting and +emand lanning
- 4ccurate forecasts are neededthroughout the value chain, andare used by all functional areas of
the organization, includingaccounting, 0nance, mar%eting,operations, and distribution.
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Chapter 11 *orecasting and +emand lanning
- One of the biggest problems !ith
forecasting systems is that theyare driven by dierentdepartmental needs and
incentive systems.- +emand planning soft!are
systems integrate mar%eting,
inventory, sales, operationsplanning, and 0nancial data.
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Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
2007 Thomson o!th"#estern
Exhibit 11.1 The $eed %or Forecasts in a &al!e Chain
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Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
2007 Thomson o!th"#estern
Chapter 11 *orecasting and +emand lanning
SAP Demand Planning moduleenablescompanies to integrate planning information from
dierent departments or organizations into a singledemand plan. The soft!are oers these %eycapabilities5
- /ultilevel lanning
- +ata 4nalysis- 6tatistical *orecasting
- Trade romotion 6upport
- Collaborative +emandlanning
Collaborative demand planning is information$sharing across the entire value chain.
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Exhibit 11.2 'mpact o% Colla(orati)e Demand Planning
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Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
2007 Thomson o!th"#estern
Chapter 11 *orecasting and +emand lanning
Basic Concepts in Forecasting
- Theplanning horizon is the lengthof time on !hich a forecast is based.
This spans from short$rangeforecasts !ith a planning horizon ofunder " months to long$rangeforecasts of 1 to 17 years.
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Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
2007 Thomson o!th"#estern
Chapter 11 *orecasting and +emand lanning
Basic Concepts in Forecasting
- 4 time series is a set ofobservations measured at successivepoints in time or over successive
periods of time. 4 time seriespattern may have one or more of thefollo!ing 0ve characteristics5
Trend Seasonal Cyclical Random Variation
Irregular (one time) Variation
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Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Exhibit 11.3 *inear Trend o% 'nd!strial
Photographic +!ipment
4 trendis the underlying pattern of
gro!th or decline in a time series.
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Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Exhibit 11.4 +-ample o% *inear and $onlinear Trend Patterns
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Exhibit 11.5 easonal Pattern o% ome $at!ral as sage
Seasonal patternsare characterized byrepeatable periods of ups and do!ns over short
periods of time.
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Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Exhibit 11.6 Trend and !siness Ccle Characteristics
3each data point is 1 ear apart4
Cyclical patternsare regular patterns in a
data series that ta%e place over long periodsof time.
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17Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Chapter 11 *orecasting and +emand lanning
Basic Concepts in Forecasting
Random variation8sometimescalled noise9 is the une3plaineddeviation of a time series from apredictable pattern, such as a trend,seasonal, or cyclical pattern.
:ecause of these random variations,forecasts are never 177 percentaccurate.
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Chapter 11 *orecasting and +emand lanning
Basic Concepts in Forecasting
Irregular variationis one$timevariation that is e3plainable. *or
e3ample, a hurricane can cause asurge in demand for buildingmaterials, food, and !ater.
The ne3t e3ample sho!s a timeseries of data representing callvolumes over 2# )uarters from a callcenter at a major 0nancialinstitution. The data is lotted in
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Exhibit
7.7
Exhibit 11.7
Call Center
&ol!me
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Exhibit 11.8 Chart o% Call &ol!me
There is an increasing trend over the si3
years along !ith seasonal patterns !ithineach year.
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Chapter 11 *orecasting and +emand lanning- Forecast error 8et9 is the dierence bet!een the
observed value of the time series and theforecast,or 4t= *t.
- Mean Square Error (MSE)
- Mean Absolute Deviation Error (MAD)
- Mean Absolute Percentage Error (MAPE)
2
Mean Square Error
( n
t=1t
=
e
n
Mean A!solu"e Error
# #n
t
t=1
e
=n
Mean Avera$e Percen" Error
# #n
t=1t
=
pe
n
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Exhibit 11.9 Forecast +rror o% +-ample Time eries Data
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Chapter 11 *orecasting and +emand lanning
Forecast Errors and Accurac
- 4 major dierence bet!een /6; and /4+is that /6; is inuenced much more bylarge forecasts errors than by small errors8because errors are s)uared9.
- /4; is dierent in that the measurementscale factor is eliminated by dividing theabsolute error by the time$series valuedata. This ma%es the measure easier to
interpret.- The selection of the best measure of
forecast accuracy is not a simple matter>
indeed, forecasting e3perts often disagreeon !hich measure should be used.
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24Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Chapter 11 *orecasting and +emand lanning!pes of Forecasting Approac"es
- Judgmental forecasting relies upon
opinions and e3pertise of people indeveloping forecasts.
- Statistical forecasting is based on the
assumption that the future !ill be ane3trapolation of the past.
- /any commercial soft!are pac%ages and
general statistical analysis programs, suchas 666, /initab, and 646, haveforecasting features or modules. ?ariousother stand$alone soft!are pac%ages e3ist
that automate some of these tas%s.
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Exhibit 11.10 Classi%ication o% asic Forecasting Methods
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26Operations Management, 2e/Ch. 11 Forecasting and Demand Planning
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Chapter 11 *orecasting and +emand lanning
Statistical Forecasting Models
The follo!ing list e3plains some of the basic andmore popular statistical forecasting models.
- 6ingle /oving 4verage- @eighed /oving 4verage
- 6ingle ;3ponential 6moothing@hen trend or seasonal factors e3ist, several othermethods are used. These models include5
- +ouble /oving 4verage- +ouble ;3ponential 6moothing- 6eason 4dditive or
/ultiplicative- Aolt$@inters 4dditive
- Aolt$@inters /ultiplicative
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Chapter 11 *orecasting and +emand lanning
Single Moving Average
- 4 moving average 8/49 forecast is anaverage of the most recent B%observations in a time series.
- /4 methods !or% best for short planning
horizons !hen there is no major trend,seasonal, or business cycle patterns.
- 4s the value of B% increases, the forecastreacts slo!ly to recent changes in the
time series data.
- 4 !eighted moving average allo!s aforecaster to put more !eight on recent
observations than older observations.
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Exhibit 11.11 as"Mart Mil5 ales Time"eries Data
E hibit 11 12 % 6 M th
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Exhibit 11.12 !mmar o% 6"Month
Mo)ing")erage Forecasts
E hibit 11 13 Mil5 l F t + l i
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Exhibit 11.13 Mil5"ales Forecast +rror nalsis
E hibit 11 14 8 lt % + l M i T l 3 t
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Exhibit 11.14 8es!lts o% +-cel Mo)ing )erage Tool 3note
misalignment o% %orecasts 9ith the time series4
E hibit 11 15 C i % 6 M th M i d
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Exhibit 11.15 Comparison o% 6"Month Mo)ing )erage and
#eighted Mo)ing )erage Models
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Chapter 11 *orecasting and +emand lanning
Single E#ponential Smoot"ing
- This is a forecast techni)ue thatuses a !eighted average of pasttime$series values to forecast thevalue of the time series in the ne3tperiod.
- The forecast Bsmoothes out the
irregular uctuations in the timeseries.
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Chapter 11 *orecasting and +emand lanning
Single E#ponential Smoot"ing
- 4s the number of data pointsincreases, the !eights associated!ith older data get progressively
smaller.- CBPredictor is an ;3cel add$on
for forecasting. C:redictor !ill
run each forecasting method youselect and !ill recommend the onethat best forecasts your data.
E hibit 11 16 mmar o% ingle + ponential moothing Mil5
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Exhibit 11.16 !mmar o% ingle +-ponential moothing Mil5"
ales Forecasts 9ith : ; 0.2
Exhibit 11 17 raph o% ingle +-ponential moothing
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Exhibit 11.17 raph o% ingle +-ponential moothing
Mil5"ales Forecasts 9ith : ; 0.2
Exhibit 11 18 CBPredictor 'np!t Data Dialog
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Exhibit 11.18 CBPredictor'np!t Data Dialog
Exhibit 11 19 CBPredictor Methods aller Dialog
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Exhibit 11.19 CBPredictorMethods aller Dialog
Exhibit 11 20
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Exhibit 11.20
Portions o%
CBPredictor
Report
#or5sheet
Exhibit 11 21 Data ttri(!tes Ta( o% CBPredictor Dialog
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Exhibit 11.21 Data ttri(!tes Ta( o%CBPredictor Dialog
Exhibit 11 22
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Exhibit 11.22
CBPredictor
8es!lts
Chapter 11 *orecasting and +emand lanning
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Chapter 11 *orecasting and +emand lanning- Regression analysis is a method for
building a statistical model that de0nes a
relationship bet!een a single dependentvariable and one or more independentvariables, all of !hich are numerical.
Yt= a !t
- 6imple linear regression 0nds the bestvalues of a and b using the method ofleast s)uares.
- ;3cel provides a very simple tool to 0ndthe best$0tting regression model for a timeseries by selecting theAdd Trendline
option from the Chart menu.
Exhibit 11 23
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Exhibit 11.23
Call Center
&ol!me
Forecasts %or
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Exhibit 11.24 Factor +nerg Costs
Exhibit 11 25 dd Trendline Dialog
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Exhibit 11.25 dd Trendline Dialog
Exhibit 11 26 dd Trendline Options Ta(
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Exhibit 11.26 dd Trendline Options Ta(
Exhibit 11 27 *east"!ares 8egression Model %or
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Exhibit 11.27 *east !ares 8egression Model %or
+nerg Cost Forecasting
Exhibit 11 28 asoline ales Data
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Exhibit 11.28 asoline ales Data
Exhibit 11.29 Chart o% ales &ers!s Time
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Exhibit 11.29 Chart o% ales &ers!s Time
Exhibit 11.30 M!ltiple 8egression 8es!lts
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Exhibit 11.30 M!ltiple 8egression 8es!lts
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Chapter 11 *orecasting and +emand lanning$udgmental Forecasting
- @hen no historical data is available,only judgmental forecasting ispossible.
- The "elphi approach consists offorecasting by e3pert opinion bygathering judgments and opinions of%ey personnel based on their
e3perience and %no!ledge of thesituation.
Chapter 11 *orecasting and +emand lanning
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Chapter 11 *orecasting and +emand lanning
$udgmental Forecasting
- 4nother common approach to gatheringdata is a survey. 6ample sizes are usuallymuch larger than !ith +elphi> ho!ever, thecost of such surveys can be high.
- The major reasons for using judgmentalmethods are5
reater accuracy,
4bility to incorporate unusual or one$time events, and
The dicultly of obtaining the datanecessary for )uantitative techni)ues.
Chapter 11 *orecasting and +emand lanningForecasting in Practice
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Chapter 11 *orecasting and +emand lanningForecasting in Practice
- /anagers use a variety of
judgmental and )uantitativeforecasting techni)ues.
- 6tatistical methods alone cannot
account for such factors as salespromotions, competitive strategies,unusual economic disturbances, ne!
products, large one time orders,natural disasters or laborcomplications.
Chapter 11 *orecasting and +emand lanning
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Chapter 11 *orecasting and +emand lanningForecasting in Practice
- The 0rst step in developing apractical forecast is to understandthe purpose, time horizon, and levelof aggregation.
- +ierent forecasting methodsre)uire dierent levels of technicalability and understanding ofmathematical principles andassumptions.
Chapter 11 6olved roblem D1
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p
+evelop a three$period and four$periodmoving$average forecasts and singlee3ponential smoothing forecasts. Computethe /4+, /4;, and /6; for each. #hichmethod provides a !etter forecast$
PeriodDeman
d PeriodDeman
d
1
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Chapter 11 6olved roblem D1
:ased on the three error metrics 8/4+, /6;,/4;9 the "$month moving average is the bestmethod among these three.
=0
=2
=>
=?
==
@0
@2
@>
@?
@=
1 2 6 > A ? 7 = @ 10 11 12
Period
Movin
Aver!e
"ore#!$t$
Chapter 11 6olved roblem D2
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Chapter 11 6olved roblem D2
4verage attendance 0gures at a majoruniversityFs home football games havegenerally been increasing as the teamGsperformance and popularity has beenimproving5
Hear 4ttendance1 2',7772 "7,777" "1,&77# #7,777& "",777' "2,277
( "&,777
Chapter 11 6olved roblem D2Solution
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Chapter 11 6olved roblem D2SolutionThe forecast for the ne3t year 8Hear
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Exhibit 11.32 +-ample Call &ol!me Data ( Da %or an5
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p
3see the %ile an5 Forecasting Case
Data.-ls on the t!dent CD"8OM4
Exhibit 11.33 elp Des5 'n!ir &ol!mes ( o!r o% Da 34
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3 4