J0444
OPERATION MANAGEMENT
Forecasting
Universitas Bina Nusantara
Peramalan


Process of predicting a
future event
Underlying basis of
all business decisions
– Production
– Inventory
– Personnel
– Facilities
$$$
Jenis Peramalan Berdasarkan
Horison Waktu
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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
Short-term vs. Longer-term
Forecasting


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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 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
Product’s Life
Introduction
Best period to
increase market
share
Company
Strategy/Issues
R&D product
engineering critical
Growth
Practical to change
price or quality image
Strengthen niche
Drive-thru restaurants
Maturity
Poor time to change image,
price, or quality
Competitive costs become
critical
Defend market position
Fax machines
CD-ROM
Sales
Color copiers
Decline
Cost control
critical
3 1/2”
Floppy
disks
Internet
Station
wagons
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
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
Jenis Peramalan

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 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
Years with Trend and Seasonality
Demand for product or service
Seasonal peaks
Trend component
Actual
demand line
Random
variation
Year
1
Year
2
Average demand
over four years
Year
3
Year
4
Actual Demand, Moving Average,
Weighted Moving Average
35
Sales Demand
30
25
Weighted moving average
Actual sales
20
15
10
5
Moving average
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Realities of Forecasting



Forecasts are seldom perfect
Most forecasting methods assume that there
is some underlying stability in the system
Both product family and aggregated product
forecasts are more accurate than individual
product forecasts
Overview of Qualitative Methods


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
Jury of executive opinion
– Pool opinions of high-level executives,
sometimes augment by statistical models
Sales force composite
– Estimates from individual salespersons are
reviewed for reasonableness, then aggregated
Delphi method
– Panel of experts, queried iteratively
Consumer Market Survey
– Ask the customer
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
© 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.
Delphi Method
Decision Makers



Iterative group process
3 types of people
– Decision makers
– Staff
– Respondents
Reduces ‘group-think’
Staff
(What will
(Sales?)
(Sales will be 50!)
sales be?
survey)
Respondents
(Sales will be 45, 50, 55)
Overview of Quantitative
Approaches

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Naïve approach
Moving averages
Exponential smoothing
Trend projection
Linear regression
Time-series
Models
Associative
models
Quantitative Forecasting Methods
(Non-Naive)
Quantitative
Forecasting
Associative
Models
Time Series
Models
Moving
Average
Exponential
Smoothing
Trend
Projection
Linear
Regression
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:
1993
Sales:
78.7
1994
63.5
1995
89.7
1996
93.2
1997
92.1
Time Series Components
Trend
Cyclical
Seasonal
Random
General Time Series Models


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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)
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

MA 
Demand in
Previous
n
n Periods
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.
Moving Average Solution
Time
1995
1996
1997
1998
1999
2000
Response
Yi
4
6
5
3
7
NA
Moving
Total
(n=3)
NA
NA
NA
4+6+5=15
Moving
Average
(n=3)
NA
NA
NA
15/3 = 5
Moving Average Solution
Time
1995
1996
1997
1998
1999
2000
Response
Yi
4
6
5
3
7
NA
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
Moving Average Solution
Time
1995
1996
1997
1998
1999
2000
Response
Yi
4
6
5
3
7
NA
Moving
Total
(n=3)
NA
NA
NA
4+6+5=15
6+5+3=14
5+3+7=15
Moving
Average
(n=3)
NA
NA
NA
15/3=5.0
14/3=4.7
15/3=5.0
Moving Average Graph
Sales
8
6
4
2
95
Actual
Forecast
96
97
98
Year
99
00
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)
ΣWeights
Actual Demand, Moving Average,
Weighted Moving Average
Weighted moving average
35
Actual sales
Sales Demand
30
25
20
15
Moving average
10
5
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Disadvantages of
Moving Average Methods
Increasing n makes forecast less sensitive
to changes
 Do not forecast trend well
 Require much historical
data

Exponential Smoothing Method


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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
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
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.
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 +
Exponential Smoothing
Solution
Ft = Ft-1 + · (At-1 - Ft-1)
Time Actual
1995
180
1996
168
1997
159
1998
175
1999
190
2000
NA
Forecast, Ft
(α = .10)
175.00 (Given)
175.00 + .10(180 - 175.00) = 175.50
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)
Exponential Smoothing Graph
Sales
190
180
170
160
150
140
93 94
Actual
Forecast
95 96
Year
97
98
Linear Regression Model

Shows linear relationship between dependent &
explanatory variables
– Example: Sales & advertising (not time)
Y-intercept
^
Y
i
Dependent
(response) variable
Slope
= a
b X
i
Independent (explanatory)
variable