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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-1
Operations Operations ManagementManagement
ForecastingForecastingChapter 4Chapter 4
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
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SATIŞ TAHMİNLEMESİSATIŞ TAHMİNLEMESİ
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BİR İŞLETLETMENİN BAŞARISI BİR İŞLETLETMENİN BAŞARISI İLERİYİ NE KADAR İYİ İLERİYİ NE KADAR İYİ
GÖREBİLMESİNE VE UYGUN GÖREBİLMESİNE VE UYGUN STRATEJİLER GELİŞTİRMESİNE STRATEJİLER GELİŞTİRMESİNE
BAĞLIDIRBAĞLIDIR
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YÖNETİCİLERİN EN TEMEL GÖREVİ:YÖNETİCİLERİN EN TEMEL GÖREVİ:PLAN YAPMAKTIRPLAN YAPMAKTIR
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PLANLAR GELECEĞE YÖNELİKTİRPLANLAR GELECEĞE YÖNELİKTİR
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ÖYLEYSE GELEÇEĞİ TAHMİNLEMEK ÖYLEYSE GELEÇEĞİ TAHMİNLEMEK ZORUNDAYIZZORUNDAYIZ
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YÖNETİÇİLERİN ALDIĞI YÖNETİÇİLERİN ALDIĞI KARARLARIN BİR ÇOĞUNDA AZ KARARLARIN BİR ÇOĞUNDA AZ YADA ÇOK BİR TÜR TAHMİN YER YADA ÇOK BİR TÜR TAHMİN YER
ALIRALIR
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ÖRNEKÖRNEK
BİR BÜRO ŞEFİNİN CUMA GÜNÜ BAZI MEMURLARINA İZİN VEREBİLMEK İÇİN CUMA GÜNÜN İŞ YÜKÜNÜ TAHMİN ETMESİ
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
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ASLINDA GÜNLÜK HAYATIMIZIN BİR ASLINDA GÜNLÜK HAYATIMIZIN BİR ÇOK KESİTİNDE DE TAHMİN ÇOK KESİTİNDE DE TAHMİN
YAPIYORUZYAPIYORUZ
ÖRNEK;ÖRNEK;
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
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ANCAK BU TAHMİNLERİN BİR ANCAK BU TAHMİNLERİN BİR KISMI OLDUKÇA KOLAYKEN KISMI OLDUKÇA KOLAYKEN
TAHMİNLENMESİ ÇOK ZOR OLAN TAHMİNLENMESİ ÇOK ZOR OLAN KONULARDA VARDIRKONULARDA VARDIR
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ÖRNEĞİN BİR OTOMOTİV ÖRNEĞİN BİR OTOMOTİV ENDÜSTRİSİNİN FİNANS ENDÜSTRİSİNİN FİNANS
MÜDÜRÜNÜN GELEÇEK YILKI MÜDÜRÜNÜN GELEÇEK YILKI MEVSİMLİK FİNANS İHTİYACINI MEVSİMLİK FİNANS İHTİYACINI
TAHMİNLEMESİTAHMİNLEMESİ
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Forecasting and optimization are Forecasting and optimization are complexcomplex
Come on! It can‘t go
wrong every time...
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Select significant attributes for Select significant attributes for your forecasteryour forecaster
Which oneIs mine?
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What is Forecasting?What is Forecasting?
Process of predicting a future event
Underlying basis of all business decisions Production Inventory Personnel Facilities
Sales will be $200 Million!
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TAHMİNLEME NEDİR?TAHMİNLEME NEDİR?
EDMUND BURK’YE GÖRE: geçmişe bakarak geleceği hiçbir zaman planlayamassınız.
PATRICK HENRY İSE; geçmiş olmadan geleceği hiçbir şekilde yargılayacak bir yol bilmiyorum der.
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tanımlartanımlar
“ Tahmin;geleçekteki muhtemel olayların belirli bir zamanda saptanması,hesaplanması ya da kestirimidir.
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TAHMİNLEME SİSTEMİTAHMİNLEME SİSTEMİ
ÇİZİM
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GİRDİLER*PAZAR DURUMLAR*GEÇMİŞ SATIŞLAR*İŞLETME STRATEJİLERİ*ENDÜSTRİNİN DURUMU*EKONOMİNİN DURUMU*TİÇARİ REKABET DURUMU*ÜRETİM KAPASİTESİ*IHUKUKİ VE POLİTİK FAKTÖRLER*DİĞER FAKTÖRLER
TAHMİNLEME TEKNİKLERİ VEYA MODELLERİ
ÇIKTILAR•HER MAL İÇİN HER ZAMAN DÖNEMİNDE BEKLENEN TALEP *DİGER FAKTÖRLER
KARAR VERİCİ
SATIŞ TAHMİNİHER MAL İÇİN HER ZAMAN DÖNEMİNDE TALEP TAHMİNİ
BAŞKALARININ ÖNERİLERİRİSK DURUMUTECRÜBEİNSİYATİF VE HİSKİŞİSEL DEĞERLER VE GÜDÜLERSOSYAL VE KÜLTÜREL DEGERLERÖTEKİ FAKTÖRLER
TAHMİNLEME SİSTEMİ
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Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments
Medium-range forecast 3 months to 3 years Sales & production planning, budgeting
Long-range forecast 3+ years New product planning, facility location
Types of Forecasts by Time Types of Forecasts by Time HorizonHorizon
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Short-term vs. Longer-term ForecastingShort-term vs. Longer-term Forecasting Medium/long range forecasts deal with more
comprehensive issues and support management decisions regarding planning and products, plants and processes.
Short-term forecasting usually employs different methodologies than longer-term forecasting
Short-term forecasts tend to be more accurate than longer-term forecasts.
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Influence of Product Life CycleInfluence of Product Life Cycle
Stages of introduction and growth require longer forecasts than maturity and decline
Forecasts useful in projecting staffing levels, inventory levels, and factory capacity
as product passes through life cycle stages
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Strategy and Issues During a Strategy and Issues During a Product’s LifeProduct’s Life
Introduction Growth Maturity Decline
Standardization
Less rapid product changes - more minor changes
Optimum capacity
Increasing stability of process
Long production runs
Product improvement and cost cutting
Little product differentiation
Cost minimization
Over capacity in the industry
Prune line to eliminate items not returning good margin
Reduce capacity
Forecasting critical
Product and process reliability
Competitive product improvements and options
Increase capacity
Shift toward product focused
Enhance distribution
Product design and development critical
Frequent product and process design changes
Short production runs
High production costs
Limited models
Attention to quality
Best period to increase market share
R&D product engineering critical
Practical to change price or quality image
Strengthen niche
Cost control critical
Poor time to change image, price, or quality
Competitive costs become critical
Defend market position
OM
Str
ateg
y/Is
sues
Com
pany
Str
ateg
y/Is
sues
HDTV
CD-ROM
Color copiers
Drive-thru restaurants Fax machines
Station wagons
Sales
3 1/2” Floppy disks
Internet
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Types of ForecastsTypes of Forecasts
Economic forecasts Address business cycle, e.g., inflation rate, money
supply etc.
Technological forecasts Predict technological change Predict new product sales
Demand forecasts Predict existing product sales
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Seven Steps in ForecastingSeven Steps in Forecasting
Determine the use of the forecast Select the items to be forecast Determine the time horizon of the forecast Select the forecasting model(s) Gather the data Make the forecast Validate and implement results
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Product Demand Charted over 4 Product Demand Charted over 4 Years with Trend and SeasonalityYears with Trend and Seasonality
Year1
Year2
Year3
Year4
Seasonal peaks Trend component
Actual demand line
Average demand over four years
Dem
and
for p
rodu
ct o
r ser
vice
Random variation
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Involves small group of high-level managers Group estimates demand by working together
Combines managerial experience with statistical models
Relatively quick ‘Group-think’
disadvantage
© 1995 Corel Corp.
Jury of Executive OpinionJury of Executive Opinion
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Sales Force CompositeSales Force Composite
Each salesperson projects their sales
Combined at district & national levels
Sales rep’s know customers’ wants
Tends to be overly optimistic
SalesSales
© 1995 Corel Corp.
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Delphi MethodDelphi Method
Iterative group process
3 types of people Decision makers Staff Respondents
Reduces ‘group-think’ Respondents Respondents
Staff Staff
Decision MakersDecision Makers(Sales?)
(What will sales be? survey)
(Sales will be 45, 50, 55)
(Sales will be 50!)
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Consumer Market SurveyConsumer Market Survey
Ask customers about purchasing plans
What consumers say, and what they actually do are often different
Sometimes difficult to answer
How many hours will you use the
Internet next week?
How many hours will you use the
Internet next week?
© 1995 Corel Corp.
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Overview of Quantitative ApproachesOverview of Quantitative Approaches
Naïve approach Moving averages Exponential smoothing Trend projection
Linear regression
Time-series Models
Associative models
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Quantitative Forecasting MethodsQuantitative Forecasting Methods (Non-Naive)(Non-Naive)
QuantitativeForecasting
LinearRegression
AssociativeModels
ExponentialSmoothing
MovingAverage
Time SeriesModels
TrendProjection
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Set of evenly spaced numerical data Obtained by observing response variable at regular time
periods
Forecast based only on past values Assumes that factors influencing past and present will
continue influence in future
ExampleYear: 1993 1994 1995 1996 1997
Sales: 78.7 63.5 89.7 93.2 92.1
What is a Time Series?What is a Time Series?
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TrendTrend
SeasonalSeasonal
CyclicalCyclical
RandomRandom
Time Series ComponentsTime Series Components
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Persistent, overall upward or downward pattern
Due to population, technology etc. Several years duration
Mo., Qtr., Yr.
Response
© 1984-1994 T/Maker Co.
Trend ComponentTrend Component
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Regular pattern of up & down fluctuations Due to weather, customs etc. Occurs within 1 year
Mo., Qtr.
Response
Summer
© 1984-1994 T/Maker Co.
Seasonal ComponentSeasonal Component
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Repeating up & down movements Due to interactions of factors influencing
economy Usually 2-10 years duration
Mo., Qtr., Yr.Mo., Qtr., Yr.
ResponseResponseCycle
Cyclical ComponentCyclical Component
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Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen
events Union strike Tornado
Short duration & nonrepeating
© 1984-1994 T/Maker Co.
Random ComponentRandom Component
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Any observed value in a time series is the product (or sum) of time series components
Multiplicative model Yi = Ti · Si · Ci · Ri (if quarterly or mo. data)
Additive model Yi = Ti + Si + Ci + Ri (if quarterly or mo. data)
General Time Series ModelsGeneral Time Series Models
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Naive ApproachNaive Approach
Assumes demand in next period is the same as demand in most recent period e.g., If May sales were 48, then
June sales will be 48
Sometimes cost effective & efficient
© 1995 Corel Corp.
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MA is a series of arithmetic means
Used if little or no trend
Used often for smoothing Provides overall impression of data over time
Equation
MAMAnn
nn Demand inDemand in PreviousPrevious PeriodsPeriods
Moving Average MethodMoving Average Method
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You’re manager of a museum store that sells historical replicas. You want to forecast sales (000) for 1998 using a 3-period moving average.
1993 41994 61995 51996 31997 7
© 1995 Corel Corp.
Moving Average ExampleMoving Average Example
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Moving Average SolutionMoving Average Solution
Time ResponseYi
MovingTotal(n=3)
MovingAverage
(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3 = 51999 72000 NA
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Moving Average SolutionMoving Average Solution
Time ResponseYi
MovingTotal(n=3)
MovingAverage
(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3 = 51999 7 6+5+3=14 14/3=4 2/32000 NA
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Moving Average SolutionMoving Average Solution
Time ResponseYi
MovingTotal(n=3)
MovingAverage
(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3=5.01999 7 6+5+3=14 14/3=4.72000 NA 5+3+7=15 15/3=5.0
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95 96 97 98 99 00Year
Sales
2
4
6
8 Actual
Forecast
Moving Average GraphMoving Average Graph
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Used when trend is present Older data usually less important
Weights based on intuition Often lay between 0 & 1, & sum to 1.0
Equation
WMA =WMA =ΣΣ(Weight for period (Weight for period nn) (Demand in period ) (Demand in period nn))
ΣΣWeightsWeights
Weighted Moving Average MethodWeighted Moving Average Method
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Actual Demand, Moving Average, Actual Demand, Moving Average, Weighted Moving AverageWeighted Moving Average
0
5
10
15
20
25
30
35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Sal
es D
eman
d
Actual sales
Moving average
Weighted moving average
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Increasing n makes forecast less sensitive to changes
Do not forecast trend well Require much historical
data © 1984-1994 T/Maker Co.
Disadvantages ofDisadvantages of Moving Average Methods Moving Average Methods
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Form of weighted moving average Weights decline exponentially Most recent data weighted most
Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen
Involves little record keeping of past data
Exponential Smoothing MethodExponential Smoothing Method
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Ft = At - 1 + (1-)At - 2 + (1- )2·At - 3
+ (1- )3At - 4 + ... + (1- )t-1·A0
Ft = Forecast value
At = Actual value = Smoothing constant
Ft = Ft-1 + (At-1 - Ft-1) Use for computing forecast
Exponential Smoothing EquationsExponential Smoothing Equations
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You’re organizing a Kwanza meeting. You want to forecast attendance for 2000 using exponential smoothing ( = .10). The1995 forecast was 175.
1995 1801996 1681997 1591996 1751999 190
© 1995 Corel Corp.
Exponential Smoothing ExampleExponential Smoothing Example
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Ft = Ft-1 + ·(At-1 - Ft-1)
TimeTime ActualForecast, F t
(αα = = .10.10))
19951995 180 175.00 (Given)
19961996 168168
19971997 159159
19981998 175175
19991999 190190
20002000 NANA
175.00 +175.00 +
Exponential Smoothing SolutionExponential Smoothing Solution
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Ft = Ft-1 + ·(At-1 - Ft-1)
TimeTime ActualForecast, F t
(αα = = .10.10))
19951995 180180 175.00 (Given)175.00 (Given)
19961996 168168 175.00 + 175.00 + .10.10((
19971997 159159
19981998 175175
19991999 190190
20002000 NANA
Exponential Smoothing SolutionExponential Smoothing Solution
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Ft = Ft-1 + ·(At-1 - Ft-1)
TimeTime ActualActualForecast, Forecast, FFtt
((αα = = .10.10))
19951995 180180 175.00 (Given)175.00 (Given)
19961996 168168 175.00 + 175.00 + .10.10(180(180 - -
19971997 159159
19981998 175175
19991999 190190
20002000 NANA
Exponential Smoothing SolutionExponential Smoothing Solution
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Ft = Ft-1 + ·(At-1 - Ft-1)
Time ActualForecast, Ft
(αα = = .10.10))
19951995 180180 175.00 (Given)175.00 (Given)
19961996 168168 175.00 + 175.00 + .10.10(180(180 - 175.00 - 175.00))
19971997 159159
19981998 175175
19991999 190190
20002000 NANA
Exponential Smoothing SolutionExponential Smoothing Solution
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Ft = Ft-1 + ·(At-1 - Ft-1)
TimeTime ActualActualForecast, Forecast, FFtt
((αα= = .10.10))
19951995 180180 175.00 (Given)175.00 (Given)
19961996 168168 175.00 +175.00 + .10.10(180 (180 - 175.00- 175.00)) = 175.50 = 175.50
19971997 159159
19981998 175175
19991999 190190
20002000 NANA
Exponential Smoothing SolutionExponential Smoothing Solution
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Ft = Ft-1 + ·(At-1 - Ft-1)
Time ActualForecast, F t
(αα = = .10.10))
1995 180 175.00 (Given)
19941994 168168 175.00 + .10(180 - 175.00) = 175.50175.00 + .10(180 - 175.00) = 175.50
19951995 159159 175.50175.50 ++ .10.10(168 -(168 - 175.50175.50)) = 174.75= 174.75
19961996 175175
19971997 190190
19981998 NANA
Exponential Smoothing SolutionExponential Smoothing Solution
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Ft = Ft-1 + ·(At-1 - Ft-1)
Time ActualForecast, F t
(α = .10)
19951995 180180 175.00 (Given)175.00 (Given)
1996 168 175.00 + .10(180 - 175.00) = 175.50
19971997 159159 175.50 + .10(168 - 175.50) = 174.75175.50 + .10(168 - 175.50) = 174.75
19981998 175175
19991999 190190
20002000 NANA
174.75174.75 ++ .10.10(159(159 - - 174.75174.75))= 173.18= 173.18
Exponential Smoothing SolutionExponential Smoothing Solution
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Ft = Ft-1 + ·(At-1 - Ft-1)
Time ActualForecast, F t
(α = .10)
1995 180 175.00 (Given)
19961996 168168 175.00 + .10(180 - 175.00) = 175.50175.00 + .10(180 - 175.00) = 175.50
19971997 159159 175.50 + .10(168 - 175.50) = 174.75175.50 + .10(168 - 175.50) = 174.75
19981998 175175 174.75 + .10(159 - 174.75) = 173.18174.75 + .10(159 - 174.75) = 173.18
19991999 190190 173.18 + 173.18 + .10.10(175(175 - 173.18- 173.18)) = 173.36= 173.36
20002000 NANA
Exponential Smoothing SolutionExponential Smoothing Solution
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-61
Ft = Ft-1 + ·(At-1 - Ft-1)
Time ActualForecast, F t
(α = .10)
19951995 180180 175.00 (Given)175.00 (Given)
19961996 168168 175.00 + .10(180 - 175.00) = 175.50175.00 + .10(180 - 175.00) = 175.50
19971997 159159 175.50 + .10(168 - 175.50) = 174.75175.50 + .10(168 - 175.50) = 174.75
19981998 175175 174.75 + .10(159 - 174.75) = 173.18174.75 + .10(159 - 174.75) = 173.18
19991999 190190 173.18 + .10(175 - 173.18) = 173.36173.18 + .10(175 - 173.18) = 173.36
20002000 NANA 173.36173.36 + + .10.10(190(190 - 173.36- 173.36) = 175.02) = 175.02
Exponential Smoothing SolutionExponential Smoothing Solution
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-62
Year
Sales
140150160170180190
93 94 95 96 97 98
Actual
Forecast
Exponential Smoothing GraphExponential Smoothing Graph
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-63
Ft = At - 1 + (1- )At - 2 + (1- )2At - 3 + ...
Forecast Effects ofForecast Effects of Smoothing Constant Smoothing Constant
Weights
Prior Period
2 periods ago
(1 - )
3 periods ago
(1 - )2
=
= 0.10
= 0.90
10%
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-64
Ft = At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...
Forecast Effects ofForecast Effects of Smoothing Constant Smoothing Constant
Weights
Prior Period
2 periods ago
(1 - )
3 periods ago
(1 - )2
=
= 0.10
= 0.90
10% 9%
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-65
Ft = At - 1 + (1- )At - 2 + (1- )2At - 3 + ...
Forecast Effects ofForecast Effects of Smoothing Constant Smoothing Constant
Weights
Prior Period
2 periods ago
(1 - )
3 periods ago
(1 - )2
=
= 0.10
= 0.90
10% 9% 8.1%
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-66
Ft = At - 1 + (1- )At - 2 + (1- )2At - 3 + ...
Forecast Effects ofForecast Effects of Smoothing Constant Smoothing Constant
Weights
Prior Period
2 periods ago
(1 - )
3 periods ago
(1 - )2
=
= 0.10
= 0.90
10% 9% 8.1%
90%
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-67
Ft = At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...
Forecast Effects ofForecast Effects of Smoothing Constant Smoothing Constant
Weights
Prior Period
2 periods ago
(1 - )
3 periods ago
(1 - )2
=
= 0.10
= 0.90
10% 9% 8.1%
90% 9%
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-68
Ft = At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...
Forecast Effects ofForecast Effects of Smoothing Constant Smoothing Constant
Weights
Prior Period
2 periods ago
(1 - )
3 periods ago
(1 - )2
=
= 0.10
= 0.90
10% 9% 8.1%
90% 9% 0.9%
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-69
Choosing Choosing
Seek to minimize the Mean Absolute Deviation (MAD)
If: Forecast error = demand - forecast
Then:n
errorsforecast MAD