Technology of Forecasting and
Assessment
INE 222
1
Evaluation Scheme
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midterm tests 10
30=20%
Assignment Problem in Class 10
Lectures and projects 10
30=20%
Oral exam 30
90=60%
90
60%
Final exam 90
• Total 150=100%
2
Course outline
CHAPTER 1: Forecasting Techniques
Chapter 2 : Leontief's
Leontief s Input
Input-Output
Output Model
Ch t 3:
Chapter
3 Introduction
I t d ti to
t Decision
D i i Making
M ki
Chapter 4: Decision Making Methodology
3
Chapter1
p
Forecasting
4
Learning
g Objectives
j
After completing this Chapter, students will be able to:
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Describe the importance of forecasting.
List the elements of a good forecast
Outline the steps in the forecasting process
Explain various components of a time series.
Choose an appropriate forecasting model.
Describe averaging techniques, trend and seasonal techniques,
and regression analysis, and solve typical problems.
Analyze and evaluate forecasting
f
errors.
Use the Link Relative Method.
Use Holt’s
Holt s Method Forecasting approach
Explain Forecast Accuracy measures
Use computers to solve basic forecasting problems
Ch1- Forecasting
 Introduction
I t d ti
• Every day, managers make decisions without knowing
what will happen in the future. Inventory is ordered though
no one knows what sales will be, new equipment is
purchased
h d though
h
h no one knows
k
the
h ddemandd for
f products,
d
and investments are made though no one knows what
profits
fit will
ill be.
b
• Managers are always trying to reduce this uncertainty and
to make
k bbetter estimates
i
off what
h will
ill happen
h
in
i the
h future.
f
Accomplishing this is the main purpose of forecasting.
• There are many ways to forecast the future, such as moving
averages, exponential smoothing, trend projections, and
least squares regression analysis.
6
What is forecasting?
• A forecast is a prediction of some future event or events
• Forecasting is the scientific methodology that uses past
data along to come up with a forecast of future demand.
• Forecasting
g " Is a pprocess of estimatingg a future event
by casting forward past data. The past data are
systematically combined in a predetermined way to
obtained the estimate of the future“
f t re“
• Prediction "Is a process of estimating a future event
based on subjective considerations other than just past
data; these subjective considerations need not be
combined in a predetermined way”
• As suggested by Neils Bohr, making good predictions is
not always easy. Famously “bad” forecasts include the
following Bad Predictions:
7
Forecasting
• Predict the next number in the pattern:
a) 3.7,
37
37
3.7,
37
3.7,
37
3.7,
37
3.7,
?
b) 2.5,
25
45
4.5,
65
6.5,
85
8.5,
10 5
10.5,
?
c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ? , ?
8
Forecasting
• Predict the next number in the pattern:
a) 3.7,
37
37
3.7,
37
3.7,
37
3.7,
3 7 3.7
3.7,
37
b) 2.5,
25
45
4.5,
65
6.5,
85
8.5,
10 5 12.5
10.5,
12 5
c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5,9.0, 11.5
9
Objective of Forecasting models
The aim of forecasting is to make:
 The best possible predictions for future events,
events
 Minimize errors
 Provide the most reliable information possible.
10
Importance
p
of Forecasting
g
Departments
epa t e ts tthroughout
oug out tthe
eo
organization
ga at o depe
depend
d
on forecasts to formulate and execute their plans.
Finance needs forecasts to project cash flows and
capital
p
requirements.
q
Human resources need forecasts to anticipate
p
hiring needs.
Production needs forecasts to plan production
levels,, workforce,, material requirements,
q
,
inventories, etc.
11
Importance
p
of Forecasting
g
Demand is not the only variable of interest to
forecasters.
Manufacturers also forecast worker
absenteeism, machine availability, material costs,
transportation and production lead times, etc.
Besides demand, service providers are also
interested in forecasts of population, of other
demographic variables, of weather, etc.
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Seven Steps
p in Forecasting
g
1. Determine the use of the forecast
1
2. Select the items to be forecasted
3. Determine the time horizon of the
forecast
4. Select the forecasting model(s)
5. Gather the data
6 Make the forecast
6.
7. Validate and implement results
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• These steps are a systematic way of initiating,
designing, and implementing a forecasting
system
• When used regularly over time, data is collected
routinelyy and calculations p
performed
automatically
• There is seldom one superior forecasting system
• Different organizations may use different
techniques
• Whatever tool works best for a firm is the one
th should
they
h ld use
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Elements of a Good Forecast
1. The forecasting should be timely. Usually, a certain
amount of time is needed to respond to the information
contained
i d in
i a forecasting.
f
i The
Th forecasting
f
i horizon
h i
must
cover the time necessary to implement possible changes.
2 The
2.
h fforecast should
h ld be
b accurate andd the
h ddegree off
accuracy should be stated.
3. The forecast should be reliable; it should work
consistently.
4. The forecast should be expressed in meaningful units.
5. The forecast should be in writing.
6. The forecast technique should be simple to understand
and use
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Elements of a Good Forecast
It should be timely
y
It should be as accurate as possible
It should be reliable
It should be in meaningful units
It should
h ld b
be presented
t d iin writing
iti
The method should be easy to use and
understand in most cases.
16
Types of Forecasts by Time Horizon
• Based
B
d on th
the titime
1. long-term forecasts look ahead several years – the time
ttypically
i ll needed
d d tto build
b ild a new ffactory,
t
ffacility
ilit llocation,
ti
research and development. (strategic planning)
> 2 years
2. medium-term forecasts look ahead between three
months and ttwo
o years
ears –the
the time ttypically
picall needed to
replace an old product with a new one
3 months to 2 years
3. short-term forecasts cover the next few weeks –
describing the continuing demand for a product
product,
Purchasing, job scheduling, workforce levels, job
assignments, and production levels
Usually < 3 months
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Types
yp of Forecasts by
y Time Horizon
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Demand
Demand
Forms of Forecast Movement
Random
movement
Time
(b) Cycle
Demand
Demand
Time
(a) Trend
Time
(c) Seasonal pattern
Time
(d) Trend with seasonal pattern
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Forecasting Approaches
1- Qualitative Methods
1 Used
1.
U d when
h situation
i
i iis vague and
d
little data exist
 New products
 New technology
2. Involves intuition, experience
 e.g., forecasting sales on Internet
20
Forecasting Approaches
2- Quantitative Methods
1. Used when situation is ‘stable’ and
historical data exist
 Existing products
 Current technology
2. Involves mathematical techniques
q
 e.g., forecasting sales of color
televisions
21
Classification of Forecasting Models
Forecasting
Techniques
Qualitative
Models
Delphi
Methods
Quantitative
Models
Time-Series Methods
Causal
Methods
Naive approach
Jury of Executive
Opinion
Regression Analysis
Moving
g
Average
Sales Force
Composite
Exponential
Smoothing
Consumer
Market Survey
Trend
Projections
Report
Decomposition
Multiple
Regression
22
Overview of Q
Quantitative Approaches
pp
 N
Naive
i approach
h
 Moving
g averages
g
 Exponential
smoothing
 Trend projection
p j
 Linear regression
 Multiple Regression
Time-Series
Ti
TimeS i
Models
Associative
Model
M d l
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