PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 4-1 Operations.

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



SATIŞ TAHMİNLEMESİSATIŞ TAHMİNLEMESİ



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



YÖNETİCİLERİN EN TEMEL GÖREVİ:YÖNETİCİLERİN EN TEMEL GÖREVİ:PLAN YAPMAKTIRPLAN YAPMAKTIR



PLANLAR GELECEĞE YÖNELİKTİRPLANLAR GELECEĞE YÖNELİKTİR



ÖYLEYSE GELEÇEĞİ TAHMİNLEMEK ÖYLEYSE GELEÇEĞİ TAHMİNLEMEK ZORUNDAYIZZORUNDAYIZ



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



ÖRNEKÖRNEK

BİR BÜRO ŞEFİNİN CUMA GÜNÜ BAZI MEMURLARINA İZİN VEREBİLMEK İÇİN CUMA GÜNÜN İŞ YÜKÜNÜ TAHMİN ETMESİ





ASLINDA GÜNLÜK HAYATIMIZIN BİR ASLINDA GÜNLÜK HAYATIMIZIN BİR ÇOK KESİTİNDE DE TAHMİN ÇOK KESİTİNDE DE TAHMİN

YAPIYORUZYAPIYORUZ

ÖRNEK;ÖRNEK;



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



ÖRNEĞİN BİR OTOMOTİV ÖRNEĞİN BİR OTOMOTİV ENDÜSTRİSİNİN FİNANS ENDÜSTRİSİNİN FİNANS

MÜDÜRÜNÜN GELEÇEK YILKI MÜDÜRÜNÜN GELEÇEK YILKI MEVSİMLİK FİNANS İHTİYACINI MEVSİMLİK FİNANS İHTİYACINI

TAHMİNLEMESİTAHMİNLEMESİ



Forecasting and optimization are Forecasting and optimization are complexcomplex

Come on! It can‘t go

wrong every time...



Select significant attributes for Select significant attributes for your forecasteryour forecaster

Which oneIs mine?



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!



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.



tanımlartanımlar

“ Tahmin;geleçekteki muhtemel olayların belirli bir zamanda saptanması,hesaplanması ya da kestirimidir.



TAHMİNLEME SİSTEMİTAHMİNLEME SİSTEMİ

ÇİZİM



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



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



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.



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



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



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



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



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



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



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.



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!)



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.



Overview of Quantitative ApproachesOverview of Quantitative Approaches

Naïve approach Moving averages Exponential smoothing Trend projection

Linear regression

Time-series Models

Associative models



Quantitative Forecasting MethodsQuantitative Forecasting Methods (Non-Naive)(Non-Naive)

QuantitativeForecasting

LinearRegression

AssociativeModels

ExponentialSmoothing

MovingAverage

Time SeriesModels

TrendProjection



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?



TrendTrend

SeasonalSeasonal

CyclicalCyclical

RandomRandom

Time Series ComponentsTime Series Components



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



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



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



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



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



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.



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



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



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




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




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



95 96 97 98 99 00Year

Sales

2

4

6

8 Actual

Forecast

Moving Average GraphMoving Average Graph



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



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



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



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



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



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



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



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




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




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




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




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




Ft = Ft-1 + ·(At-1 - Ft-1)


(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




Ft = Ft-1 + ·(At-1 - Ft-1)


(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




Ft = Ft-1 + ·(At-1 - Ft-1)


(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




Year

Sales

140150160170180190

93 94 95 96 97 98

Actual

Forecast

Exponential Smoothing GraphExponential Smoothing Graph



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%



Ft = At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...


Weights

Prior Period

2 periods ago

(1 - )

3 periods ago

(1 - )2

=

= 0.10

= 0.90

10% 9%



Ft = At - 1 + (1- )At - 2 + (1- )2At - 3 + ...


Weights

Prior Period

2 periods ago

(1 - )

3 periods ago

(1 - )2

=

= 0.10

= 0.90

10% 9% 8.1%



Ft = At - 1 + (1- )At - 2 + (1- )2At - 3 + ...


Weights

Prior Period

2 periods ago

(1 - )

3 periods ago

(1 - )2

=

= 0.10

= 0.90

10% 9% 8.1%

90%



Ft = At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...


Weights

Prior Period

2 periods ago

(1 - )

3 periods ago

(1 - )2

=

= 0.10

= 0.90

10% 9% 8.1%

90% 9%



Ft = At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...


Weights

Prior Period

2 periods ago

(1 - )

3 periods ago

(1 - )2

=

= 0.10

= 0.90

10% 9% 8.1%

90% 9% 0.9%



Choosing Choosing

Seek to minimize the Mean Absolute Deviation (MAD)

If: Forecast error = demand - forecast

Then:n

errorsforecast MAD

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 4-1 Operations.

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