Operations
Management
Forecasting
Chapter 4
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SATIŞ TAHMİNLEMESİ
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BİR İŞLETLETMENİN BAŞARISI
İLERİYİ NE KADAR İYİ
GÖREBİLMESİNE VE UYGUN
STRATEJİLER GELİŞTİRMESİNE
BAĞLIDIR
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YÖNETİCİLERİN EN TEMEL GÖREVİ:
PLAN YAPMAKTIR
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PLANLAR GELECEĞE YÖNELİKTİR
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ÖYLEYSE GELEÇEĞİ TAHMİNLEMEK
ZORUNDAYIZ
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YÖNETİÇİLERİN ALDIĞI
KARARLARIN BİR ÇOĞUNDA AZ
YADA ÇOK BİR TÜR TAHMİN YER
ALIR
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ÖRNEK
BİR BÜRO ŞEFİNİN CUMA GÜNÜ BAZI
MEMURLARINA İZİN VEREBİLMEK İÇİN
CUMA GÜNÜN İŞ YÜKÜNÜ TAHMİN ETMESİ
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ASLINDA GÜNLÜK HAYATIMIZIN BİR
ÇOK KESİTİNDE DE TAHMİN
YAPIYORUZ
ÖRNEK;
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ANCAK BU TAHMİNLERİN BİR
KISMI OLDUKÇA KOLAYKEN
TAHMİNLENMESİ ÇOK ZOR OLAN
KONULARDA VARDIR
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ÖRNEĞİN BİR OTOMOTİV
ENDÜSTRİSİNİN FİNANS
MÜDÜRÜNÜN GELEÇEK YILKI
MEVSİMLİK FİNANS İHTİYACINI
TAHMİNLEMESİ
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Forecasting and optimization are
complex
Come on! It can‘t go
wrong every time...
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Select significant attributes for
your forecaster
Which one
Is mine?
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What is Forecasting?
 Process of predicting a
future event
Sales will
be $200
Million!
 Underlying basis of
all business decisions




Production
Inventory
Personnel
Facilities
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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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tanımlar
“ Tahmin;geleçekteki muhtemel olayların
belirli bir zamanda saptanması,hesaplanması
ya da kestirimidir.
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TAHMİNLEME SİSTEMİ
ÇİZİM
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TAHMİNLEME SİSTEMİ
GİRDİLER
*PAZAR DURUMLAR
*GEÇMİŞ SATIŞLAR
*İŞLETME STRATEJİLERİ
*ENDÜSTRİNİN DURUMU
*EKONOMİNİN DURUMU
*TİÇARİ REKABET DURUMU
*ÜRETİM KAPASİTESİ
*IHUKUKİ VE POLİTİK FAKTÖRLER
*DİĞER FAKTÖRLER
ÇIKTILAR
TAHMİNLEME TEKNİKLERİ
VEYA MODELLERİ
SATIŞ TAHMİNİ
HER MAL İÇİN HER ZAMAN
DÖNEMİNDE TALEP TAHMİNİ
KARAR VERİCİ
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•HER MAL İÇİN HER ZAMAN
DÖNEMİNDE BEKLENEN
TALEP
*DİGER FAKTÖRLER
BAŞKALARININ ÖNERİLERİ
RİSK DURUMU
TECRÜBE
İNSİYATİF VE HİS
KİŞİSEL DEĞERLER VE GÜDÜLER
SOSYAL VE KÜLTÜREL DEGERLER
ÖTEKİ FAKTÖRLER
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Types of Forecasts by Time
Horizon
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

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Short-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 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
Product’s Life
Introduction
Company Strategy/Issues
Best period to
increase market
share
R&D product
engineering critical
Growth
Maturity
Practical to change
price or quality image
Poor time to change
image, price, or quality
Competitive costs become
critical
Strengthen niche
Fax machines
CD-ROM
Color copiers
Cost control
critical
Defend market position
Drive-thru restaurants
Sales
Decline
3 1/2”
Floppy
disks
Station
wagons
Internet
HDTV
OM Strategy/Issues
Product design and
development critical
Frequent product and
process design changes
Short production runs
High production costs
Forecasting critical
Standardization
Product and process
reliability
Less rapid product
changes - more minor
changes
Competitive product
improvements and options
Increase capacity
Limited models
Shift toward product
focused
Attention to quality
Enhance distribution
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Optimum capacity
Increasing stability of
process
Long production runs
Product improvement and
cost cutting
4-23
Little product
differentiation
Cost minimization
Over capacity in the
industry
Prune line to eliminate
items not returning good
margin
Reduce capacity
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Types 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 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
Years with Trend and Seasonality
Demand for product or service
Seasonal peaks
Trend component
Actual
demand line
Random
variation
Year
1
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Year
2
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Average demand
over four years
Year
3
Year
4
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Jury of Executive Opinion
 Involves small group of high-level managers

Group estimates demand by working together
 Combines managerial experience with
statistical models
 Relatively quick
 ‘Group-think’
disadvantage
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© 1995 Corel Corp.
Sales Force Composite
 Each salesperson
projects their sales
 Combined at district &
national levels
 Sales rep’s know
customers’ wants
 Tends to be overly
optimistic
Sales
© 1995 Corel Corp.
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Delphi Method
Iterative group
process
3 types of people
Decision makers
 Staff
 Respondents

Decision Makers
Staff
(What will
sales be?
survey)
Reduces ‘groupthink’
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(Sales?)
(Sales will be 50!)
Respondents
(Sales will be 45, 50, 55)
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Consumer Market Survey
 Ask customers
about purchasing
plans
 What consumers
say, and what they
actually do are
often different
 Sometimes difficult
to answer
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How many hours will
you use the Internet
next week?
© 1995 Corel
Corp.
4-30
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Overview of Quantitative Approaches
Naïve approach
Moving averages
Exponential smoothing
Trend projection
Time-series
Models
Linear regression
Associative
models
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Quantitative Forecasting Methods
(Non-Naive)
Quantitative
Forecasting
Associative
Models
Time Series
Models
Moving
Average
Exponential
Smoothing
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Trend
Projection
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Linear
Regression
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What is a Time Series?
 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
 Example
Year:
Sales:
1993
78.7
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1994
63.5
4-33
1995
89.7
1996
93.2
1997
92.1
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Time Series Components
Trend
Cyclical
Seasonal
Random
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Trend Component
Persistent, overall upward or downward
pattern
Due to population, technology etc.
Several years duration
Response
Mo., Qtr., Yr.
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© 1984-1994 T/Maker Co.
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Seasonal Component
Regular pattern of up & down fluctuations
Due to weather, customs etc.
Occurs within 1 year
Summer
Response
© 1984-1994 T/Maker Co.
Mo., Qtr.
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Cyclical Component
Repeating up & down movements
Due to interactions of factors influencing
economy
Usually 2-10 years duration
Cycle
Response

Mo., Qtr., Yr.
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Random Component
Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen
events
© 1984-1994 T/Maker Co.
Union strike
 Tornado

Short duration &
nonrepeating
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General Time Series Models
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
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(if quarterly or mo. data)
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Naive 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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Moving Average Method
 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
Demand in Previous n Periods

MA 
n
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Moving Average Example
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
4
1994
6
1995
5
1996
3
1997
7
© 1995 Corel Corp.
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Moving Average Solution
Time
1995
1996
1997
1998
1999
2000
Response
Yi
4
6
5
3
7
Moving
Total
(n=3)
NA
NA
NA
4+6+5=15
Moving
Average
(n=3)
NA
NA
NA
15/3 = 5
NA
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Moving Average Solution
Time
1995
1996
1997
1998
1999
2000
Response
Yi
4
6
5
3
7
Moving
Total
(n=3)
NA
NA
NA
4+6+5=15
6+5+3=14
Moving
Average
(n=3)
NA
NA
NA
15/3 = 5
14/3=4 2/3
NA
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Moving Average Solution
Time
1995
1996
1997
1998
1999
2000
Response
Yi
4
6
5
3
7
NA
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Moving
Total
(n=3)
NA
NA
NA
4+6+5=15
6+5+3=14
5+3+7=15
4-45
Moving
Average
(n=3)
NA
NA
NA
15/3=5.0
14/3=4.7
15/3=5.0
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Moving Average Graph
Sales
8
Actual
6
Forecast
4
2
95
96
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97 98
Year
4-46
99
00
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Weighted Moving Average Method
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 =
Σ(Weight for period n) (Demand in period n)
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ΣWeights
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Actual Demand, Moving Average,
Weighted Moving Average
35
Sales Demand
30
25
Weighted moving average
Actual sales
20
15
10
Moving average
5
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
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Disadvantages of
Moving Average Methods
Increasing n makes forecast less
sensitive to changes
Do not forecast trend well
Require much historical
data
© 1984-1994 T/Maker Co.
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Exponential Smoothing Method
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
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Exponential Smoothing Equations
 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
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Exponential Smoothing Example
You’re organizing a Kwanza meeting. You
want to forecast attendance for 2000 using
exponential smoothing
( = .10). The1995 forecast was 175.
1995
180
1996
168
1997
159
1996
175
1999
190
© 1995 Corel Corp.
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, F t
(α = .10)
Time Actual
1995
180
1996
168
1997
159
1998
175
1999
190
2000
NA
175.00 (Given)
175.00 +
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, F t
(α = .10)
Time Actual
1995
180
1996
168
1997
159
1998
175
1999
190
2000
NA
175.00 (Given)
175.00 + .10(
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, Ft
(α = .10)
Time Actual
1995
180
1996
168
1997
159
1998
175
1999
190
2000
NA
175.00 (Given)
175.00 + .10(180 -
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, Ft
(α = .10)
Time Actual
1995
180
1996
168
1997
159
1998
175
1999
190
2000
NA
175.00 (Given)
175.00 + .10(180 - 175.00)
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, Ft
(α = .10)
Time Actual
1995
180
1996
168
1997
159
1998
175
1999
190
2000
NA
175.00 (Given)
175.00 + .10(180 - 175.00) = 175.50
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, F t
(α = .10)
Time
Actual
1995
180
1994
168
175.00 + .10(180 - 175.00) = 175.50
1995
159
175.50 + .10(168 - 175.50) = 174.75
1996
175
1997
190
1998
NA
175.00 (Given)
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, F t
(α = .10)
Time
Actual
1995
180
1996
168
175.00 + .10(180 - 175.00) = 175.50
1997
159
175.50 + .10(168 - 175.50) = 174.75
1998
175
174.75 + .10(159 - 174.75)= 173.18
1999
190
2000
NA
175.00 (Given)
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Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, F t
(α = .10)
Time
Actual
1995
180
1996
168
175.00 + .10(180 - 175.00) = 175.50
1997
1998
159
175.50 + .10(168 - 175.50) = 174.75
175
174.75 + .10(159 - 174.75) = 173.18
1999
190
173.18 + .10(175 - 173.18) = 173.36
2000
NA
175.00 (Given)
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
4-60
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Exponential Smoothing Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Forecast, F t
(α = .10)
Time
Actual
1995
180
1996
168
175.00 + .10(180 - 175.00) = 175.50
1997
159
175.50 + .10(168 - 175.50) = 174.75
1998
175
174.75 + .10(159 - 174.75) = 173.18
1999
190
173.18 + .10(175 - 173.18) = 173.36
2000
NA
173.36 + .10(190 - 173.36) = 175.02
175.00 (Given)
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Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Exponential Smoothing Graph
Sales
190
180
170
160
150
140
93
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
Actual
Forecast
94
95 96
Year
4-62
97
98
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Forecast Effects of
Smoothing Constant 
Ft =  At - 1 + (1- )At - 2 + (1- )2At - 3 + ...
Weights
=
Prior Period
2 periods ago 3 periods ago

= 0.10
(1 - )
(1 - )2
10%
= 0.90
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Forecast Effects of
Smoothing Constant 
Ft =  At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...
Weights
=
Prior Period
= 0.10
2 periods ago 3 periods ago

(1 - )
10%
9%
(1 - )2
= 0.90
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
4-64
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Forecast Effects of
Smoothing Constant 
Ft =  At - 1 + (1- )At - 2 + (1- )2At - 3 + ...
Weights
=
Prior Period
= 0.10
2 periods ago 3 periods ago

(1 - )
(1 - )2
10%
9%
8.1%
= 0.90
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
4-65
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Forecast Effects of
Smoothing Constant 
Ft =  At - 1 + (1- )At - 2 + (1- )2At - 3 + ...
Weights
=
Prior Period
2 periods ago 3 periods ago

(1 - )
(1 - )2
= 0.10
10%
9%
8.1%
= 0.90
90%
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
4-66
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Forecast Effects of
Smoothing Constant 
Ft =  At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...
Weights
=
Prior Period
2 periods ago 3 periods ago

(1 - )
(1 - )2
= 0.10
10%
9%
8.1%
= 0.90
90%
9%
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
4-67
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Forecast Effects of
Smoothing Constant 
Ft =  At - 1 + (1- ) At - 2 + (1- )2At - 3 + ...
Weights
=
Prior Period
2 periods ago 3 periods ago

(1 - )
(1 - )2
= 0.10
10%
9%
8.1%
= 0.90
90%
9%
0.9%
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
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© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
Choosing 
Seek to minimize the Mean Absolute Deviation (MAD)
If:
Then:
Forecast error = demand - forecast
MAD 
PowerPoint presentation to accompany Operations
Management, 6E (Heizer & Render)
 forecast errors
n
4-69
© 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458