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Chapter 2:
Aspects of project preparation and analysis
Mohammed seid Hussen
Lecturer of Economics
Debre Berhan University
College of Business and Economics
meetmame@me.com

March, 2013

Forestry

Wind power Hydro power
Heavy
Industry

Bio-fuels
Transport
Animal waste

Sewage / Landfill
wastewater
Bagasse
4/6/2013 Prepared by Mohammed S. 2

2.1. Demand and market analysis

• is to identify the needs of the consumers and
determine whether they are willing and have
the capability to pay for a given product.
• should be carried out for the following main
reasons:
– whether the goods and services required by the
community
– to estimate the volume which it would wish to
acquire at given prices

4/6/2013 by Mohammed S. 3

• market study should include
determination of potential demand for the
project’s output and the volume at given price
range
target group
time frame for the demand
• relevant both to projects which produce
– marketable” goods and services
– social goods and are supplied ‘free’ which do
not, such as schools, hospitals, roads and the like


• Market analysis is basically concerned with
the following questions
– What is the product/service to for which feasibility
study is to be undertaken?
• What is the specific need which is the basis for the
product/service?
• Are there alternative ways of satisfying the need?
– What would be the aggregate demand of the
proposed product/service in future?
– What would be the market share of the project
under appraisal?


– What is the ongoing and competitive selling price?
– Will the realization of the project affect the selling
price(s) of the products/services?
– What are the marketing strategies that enable the
firm to enter into a market and capture adequate
market size?


Saying on perspectives on effective demand and market promotion
A saying goes, “an economist and marketer were sent to make
market study for shoes in an island. Immediately after their
arrival, they observed that the people there were all barefoot.
Both had to write independent reports. The economist reported
that there is no market because there is no revealed demand for
shoes as the entire population is barefoot. The market reported
that there is big, untapped market, no has not entered into the
market and hence he appreciated the possibility of taking the
entire market. But he/she qualified the fact that there is a need for
promotional work.”


• To answer the above questions the project
analyst requires information
– Consumption trends in the past and the present
consumption levels
– Past and present supply positions
– Production possibilities and constraints
– Imports and exports
– Cost structure
– Elasticity of demand
– Consumer
behavior, intentions, attitudes, preferences, and
requirements

• it should be carried out in an orderly and
systematic manner
– Situational analysis and specification of objectives
– Collection of secondary information
– Conduct of market survey
– Characterization of the market
– Demand forecasting
– Market planning


2.2 situational analysis and specification of the
objectives

• the project analyst may talk to
consumers, competitors, middlemen, and
other in the industry
• also look at the preferences and purchasing
power of consumer’s, actions and strategies of
competitors and practices of the
middlemen/distributors, whole sellers and
retailers/.


Key steps in market and demand analysis and their
inter-relationships
Collection of Demand
Secondary Forecasting
Information

Situational
Characterization
Analysis and
of the Market
Specifications
of Objectives

Conduct of Market
Market Survey Planning

4/6/2013 Mohammed Seid 11

Example: suppose a given project aims at producing wheat in a
given locality. The project initiator and implementer need
information about where and how to market their product. The
objective of the market and demand analysis in this case may be
to answer some of the following questions.
– Who are the buyers of this product
– What is the total current demand for wheat?
– How is the demand distributed temporally /pattern of sale over the
year and geographically?
– What price will the consumers be willing to pay for the product?
– How can consumers be convinced that wheat could be substituted for
other foodstuffs?
– What channels of distributions are most suited for the product?
– What trade margins will induce distributors to carry it out?
– What are the possible immediate sales?


2.3 method of data collection
• two principal sources of assembling market
information
– Secondary data sources;
– Primary data sources.


ㅁ. Indirect (Secondary) Sources
-documents
-statistics
-key informant approach

ㅁ. Direct ( primary) Sources

-interview
-focus group discussions
-questionnaires or surveys
-direct observation

SEVEN DATA COLLECTION TECHNIQUES FOR DEVELOPMENT
PROJECT NEEDS ASSESSMENT
Secondary sources
Method Definition Sources Advantages Disadvantages

Documentary Systematic Libraries; Already collected; Not always available
Research reading of Scholars; Low level of effort on topics needed;
needs data Officials; to analyze. Can be dated; usually
compiled by Specialized incomplete.
secondary
sources agencies

Statistics and Sorting and Ministry data Available in most May contain gaps;
Planning analyzing bases; line ministries; usually unaggregated;
Data information from Planning easy to obtain; Requires a specialist
extant data departments; Can be to analyze it.
bases Statistics voluminous
centres
Key Interviewing of People or Economical; relies Informants may inject
Information knowledgeable agencies on knowledgeable their own biases.
Approach secondary which are in a informants.
sources position to
know about
the subjects

SEVEN DATA COLLECTION TECHNIQUES FOR DEVELOPMENT PROJECT NEEDS
ASSESSMENT
Primary sources

Method Definition Sources Advantages Disadvantages
Interview Soliciting and Primary Goes right to the Can be expensive;
Recording sources like source of the requires some special
Information project information. interviewing skills.
By asking beneficiaries
Questions
Focus Group Small Group Primary Introduces Somewhat of an
discussion sources like element of artificial setting for
focuses on project spontaneity since such a discussion
development beneficiaries discussion is un- may inhibit some.
Problems. guided.
Questionnaire Published list of Primary or When well done, it Difficult to construct;
questions to be secondary obtains highly Requires high degree
answered by sources reliable data. of skill.
every informant.
Direct Firsthand Primary Lacks artificiality of Can be expensive if
Observation exposure of the sources like other methods; lots of exposure is
project team to project Gives assessor required; difficult to
the behaviour or beneficiaries standardize data.
good exposure.
phenomenon
being assessed.

Forecasting
• Predicting the future
• Qualitative forecast
methods
– subjective
• Quantitative forecast
methods
– based on mathematical
formulas

12-17

Types of Forecasting Methods
• Depend on
– time frame
– demand behavior
– causes of behavior

12-18

Time Frame
• Indicates how far into the future is forecast
– Short- to mid-range forecast
• typically encompasses the immediate future
• daily up to two years
– Long-range forecast
• usually encompasses a period of time longer than two
years

12-19

Demand Behavior
• Trend
– a gradual, long-term up or down movement of demand
• Random variations
– movements in demand that do not follow a pattern
• Cycle
– an up-and-down repetitive movement in demand
• Seasonal pattern
– an up-and-down repetitive movement in demand
occurring periodically

12-20

Causes of Behavior
• Analytical
• Cause effect relationship basis
• Quantitative
• Explicit


DEMAND FORECASTING

• Qualitative Methods
– These methods rely essentially on the judgment
of experts to translate qualitative information
into quantitative estimates
– Used to generate forecasts if historical data are
not available (e.g., introduction of new product)
– The important qualitative methods are:
• Jury of Executive Method
• Delphi Method

JURY OF EXECUTIVE OPINION METHOD
• Rationale
– Upper-level management has best information on latest
product developments and future product launches
• Approach
– Small group of upper-level managers collectively develop
forecasts – Opinion of Group
• Main advantages
– Combine knowledge and expertise from various
functional areas
– People who have best information on future
developments generate the forecasts


JURY OF EXECUTIVE OPINION METHOD
• Main drawbacks
– Expensive
– No individual responsibility for forecast quality
– Risk that few people dominate the group
– Subjective
– Reliability is questionable
• Typical applications
– Short-term and medium-term demand forecasting


DELPHI METHOD
• Rationale

– Eliciting the opinions of a group of experts with
the help of mail survey

– Anonymous written responses encourage honesty
and avoid that a group of experts are dominated
by only a few members


DELPHI METHOD
• Approach

Coordinator Each expert Coordinator
Sends Initial writes response performs
Questionnaire (anonymous) analysis

Coordinator No
Coordinator
sends updated Consensus Yes
summarizes
questionnaire reached? forecast


DELPHI METHOD

• Main advantages
– Generate consensus
– Can forecast long-term trend without availability
of historical data
• Main drawbacks
– Slow process
– Experts are not accountable for their responses
– Little evidence that reliable long-term forecasts
can be generated with Delphi or other methods

DELPHI METHOD
• Typical application
– Long-term forecasting
– Technology forecasting


TIME SERIES PROJECTION METHODS
• These methods generate forecasts on the basis of an
analysis of the historical time series.
• Assume that what has occurred in the past will
continue to occur in the future
• Relate the forecast to only one factor - time
The important time series projection methods are:
– Trend Projection Method
– Exponential Smoothing Method
– Moving Average Method


Linear Trend Line

xy - nxy
y = a + bx b =
x2 - nx2
a = y-bx
where
a = intercept of the where
relationship n = number of periods
b = slope of the line
x = time period x
x = = mean of the x values
y = forecast for n
demand for period x y
y = n = mean of the y values

12-30

Least Squares Example
x(PERIOD) y(DEMAND) xy x2
1 73 73 1
2 40 80 4
3 41 123 9
4 37 148 16
5 45 225 25
6 50 300 36
7 43 301 49
8 47 376 64
9 56 504 81
10 52 520 100
11 55 605 121
12 54 648 144
78 557 3867 650

12-31

Least Squares Example (cont.)

78
x = = 6.5
12
557
y = = 46.42
12
xy - nxy 3867 - (12)(6.5)(46.42)
b = 2 - nx2
= =1.72
x 650 - 12(6.5)2

a = y - bx
= 46.42 - (1.72)(6.5) = 35.2

12-32

Linear trend line y = 35.2 + 1.72x
Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units

70 –

60 –
Actual

50 –
Demand

40 –
Linear trend line
30 –

20 –

10 – | | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13
0– Period

12-33

Trend Projection Method

Advantages
• It uses all observations
• The straight line is derived by statistical procedure
• A measure of goodness fit is available

Disadvantages
• More complicated
• The results are valid only when certain conditions are
satisfied


Exponential Smoothing

 Averaging method
 Weights most recent data more strongly
 Reacts more to recent changes
 Widely used, accurate method

12-35

Exponential Smoothing (cont.)

Ft +1 = Dt + (1 - )Ft
where:
Ft +1 = forecast for next period
Dt = actual demand for present period
Ft = previously determined forecast for present
period
= weighting factor, smoothing constant

12-36

Effect of Smoothing Constant

0.0 1.0
If = 0.20, then Ft +1 = 0.20 Dt + 0.80 Ft

If = 0, then Ft +1 = 0 Dt + 1 Ft = Ft
Forecast does not reflect recent data

If = 1, then Ft +1 = 1 Dt + 0 Ft = Dt
Forecast based only on most recent data

12-37

Exponential Smoothing (α=0.30)

PERIOD MONTH DEMAND F2 = D1 + (1 - )F1
1 Jan 37 = (0.30)(37) + (0.70)(37)
2 Feb 40 = 37
3 Mar 41
4 Apr 37 F3 = D2 + (1 - )F2
5 May 45 = (0.30)(40) + (0.70)(37)
6 Jun 50
= 37.9
7 Jul 43
8 Aug 47
F13 = D12 + (1 - )F12
9 Sep 56
10 Oct 52 = (0.30)(54) + (0.70)(50.84)
11 Nov 55 = 51.79
12 Dec 54

12-38

FORECAST, Ft + 1
PERIOD MONTH DEMAND ( = 0.3) ( = 0.5)
1 Jan 37 – –
2 Feb 40 37.00 37.00
3 Mar 41 37.90 38.50
4 Apr 37 38.83 39.75
5 May 45 38.28 38.37
6 Jun 50 40.29 41.68
7 Jul 43 43.20 45.84
8 Aug 47 43.14 44.42
9 Sep 56 44.30 45.71
10 Oct 52 47.81 50.85
11 Nov 55 49.06 51.42
12 Dec 54 50.84 53.21
13 Jan – 51.79 53.61
12-39

70 –

60 – Actual = 0.50

50 –

40 –
Orders

= 0.30
30 –

20 –

10 –

0– | | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13
Month
12-40

Moving Average
• Naive forecast
– demand in current period is used as next period’s
forecast
• Simple moving average
– uses average demand for a fixed sequence of periods
– stable demand with no pronounced behavioral patterns
• Weighted moving average
– weights are assigned to most recent data

12-41

Moving Average:
Naïve Approach

ORDERS
MONTH PER MONTH FORECAST

Jan 120 -
Feb 90 120
Mar 100 90
Apr 75 100
May 110 75
June 50 110
July 75 50
Aug 130 75
Sept 110 130
Oct 90 110
Nov - 90
12-42

Simple Moving Average

n
Di
i=1
MAn =
n
where

n = number of periods in
the moving average
Di = demand in period i

12-43

3-month Simple Moving Average

3
ORDERS MOVING Di
MONTH PER MONTH AVERAGE i=1
MA3 =
Jan 120 – 3
Feb 90 –
Mar 100 – 90 + 110 + 130
Apr 75 103.3 = 3
May 110 88.3
June 50 95.0
July 75 78.3 = 110 orders
Aug 130 78.3 for Nov
Sept 110 85.0
Oct 90 105.0
Nov - 110.0

12-44

5-month Simple Moving Average

ORDERS MOVING
MONTH PER MONTH AVERAGE 5
Di
Jan 120 – i=1
Feb 90 – MA5 =
Mar 100 –
5
Apr 75 –
90 + 110 + 130+75+50
May 110 – =
June 50 99.0
5
July 75 85.0
Aug 130 82.0 = 91 orders
Sept 110 88.0 for Nov
Oct 90 95.0
Nov - 91.0

12-45

Smoothing Effects
150 –

125 – 5-month

100 –
Orders

75 –

50 – 3-month

Actual
25 –

0– | | | | | | | | | | |
Jan Feb Mar Apr May June July Aug Sept Oct Nov
Month
12-46

Weighted Moving Average
n

 Adjusts moving WMAn = i = 1Wi Di
average
method to where
more closely Wi = the weight for period i,
reflect data between 0 and 100
percent
fluctuations
Wi = 1.00

12-47

Weighted Moving Average Example

MONTH WEIGHT DATA
August 17% 130
September 33% 110
October 50% 90
3
November Forecast WMA3 = Wi Di
i=1

= (0.50)(90) + (0.33)(110) + (0.17)(130)

= 103.4 orders
12-48

CAUSAL METHODS

• Causal methods seek to develop forecasts on
the basis of cause-effects relationships
specified in an explicit, quantitative manner.
– Chain Ratio Method
– Consumption Level Method
– End Use Method
– Leading Indicator Method
– Econometric Method


CHAIN RATIO METHOD
• Market Potential for heated coats in the U.S.:
– Population (U) = 280,000,000
– Proportion of U that are age over 16 (A) = 75%
– Proportion of A that are men (M) = 50%
– Proportion of M that have incomes over $65k (I) = 50%
– Proportion of I that live in cold states (C) = 50%
– Proportion of C that ski regularly (S) = 10%
– Proportion of S that are fashion conscious (F) = 30%
– Proportion of F that are early adopters (E) = 10%
– Average number of ski coats purchased per year (Y) = .5
coats
– Average price per coat (P) = $ 200


CHAIN RATIO METHOD
• Market Potential for heated coats in the U.S.:
Market Sales Potential =
UxAxMxIxCxSxFxExY
= 280 Million x 0.75 x 0.50 x 0.50 x 0.50 x 0.10 x 0.30
x 0.10 x200
= $7.88 Million


CONSUMPTION LEVEL METHOD
• This method is used for those products that
are directly consumed. This method measures
the consumption level on the basis of
elasticity coefficients. The important ones are



• Income Elasticity: This reflects the responsiveness
of demand to variations in income. It is calculated
as:
• E1 = [Q2 - Q1/ I2- I1] * [I1+I2/ Q2 +Q1]
• Where E1 =
Income elasticity of demand
Q1 = quantity demanded in the base year
Q2 = quantity demanded in the following year
I1 = income level in the base year
I2 = income level in the following year


• Price Elasticity: This reflects the responsiveness of
demand to variations in price. It is calculated as:
• EP = [Q2 - Q1/ P2- P1] * [P1+P2/ Q2 +Q1]
• Where EP = Price
elasticity of demand Q1 = quantity
demanded in the base year Q2 = quantity
demanded in the following year P1 = price level in
the base year
P2 = price level in the following year


END USE METHOD
• Suitable for estimating demand for intermediate
products
• Also called as consumption coefficient method
Steps
1. Identify the possible uses of the products
2. Define the consumption coefficient of the product
for various uses
3. Project the output levels for the consuming
industries
4. Derive the demand for the project

END USE METHOD

• This method forecasts the demand based on the
consumption coefficient of the various uses of the
product.

Projected Demand for Indchem
Consumption Projected Output Projected Demand for
Coefficient in Year X Indchem in Year X
Alpha 2.0 10,000 20,000
Beta 1.2 15,000 18,000
Kappa 0.8 20,000 16,000
Gamma 0.5 30,000 15,000
Total 69,000

LEADING INDICATOR METHOD
• This method uses the changes in the leading
indicators to predict the changes in the
lagging indicators.
• Two basic steps:
1. Identify the appropriate leading indicator(s)
2. Establish the relationship between the leading
indicator(s) and the variable to forecast.


ECONOMETRIC METHOD
• An advanced forecasting tool, it is a mathematical
expression of economic relationships derived from
economic theory.
• Economic variables incorporated in the model
1. Single Equation Model
Dt = a0 + a1 Pt + a2 Nt
• Where
Dt = demand for a certain product in year t.
Pt = price of the product in year t.
Nt = income in year t.

ECONOMETRIC METHOD
2. Simultaneous equation method
GNPt = Gt + It + Ct
It = a0 + a1 GNPt
Ct = b0 + b1 GNPt
• Where
GNPt = gross national product for year t.
Gt = Governmental purchase for year t.
It = Gross investment for year t.
Ct= Consumption for year t.

ECONOMETRIC METHOD
Advantages
• The process sharpens the understanding of
complex cause – effect relationships
• This method provides basis for testing
assumptions
Disadvantages
• It is expensive and data demanding
• To forecast the behaviour of dependant
variable, one needs the projected values of
independent variables

UNCERTANITIES IN DEMAND
FORECASTING
• Data about past and present markets.
– Lack of standardization:- product, price, quantity,
cost, income….
– Few observations
– Influence of abnormal factors:- war, natural
calamity
• Methods of forecasting
– Inability to handle unquantifiable factors
– Unrealistic assumptions
4/6/2013
– Excessive data requirement
Mohammed Seid 61

UNCERTANITIES IN DEMAND
FORECASTING
• Environmental changes
– Technological changes
– Shift in government policy
– Developments on the international scene
– Discovery of new source of raw material
– Vagaries of monsoon


COPING WITH UNCERTAINTIES

• Conduct analysis with data based on uniform
and standard definitions.
• Ignore the abnormal or out-of-ordinary
observations.
• Critically evaluate the assumptions
• Adjust the projections.
• Monitor the environment.
• Consider likely alternative scenarios.
• Conduct sensitivity analysis

Market planning
• Current marketing situation
- Market, Competition, Distribution, PEST.
• Opportunity and issue analysis - SWOT
• Objectives- Break even, % market share…
• Marketing strategy- target
segment, positioning, 4 Ps
• Action program- Quarter 1, Q2, Q3….


End

Mohammed Seid Hussen
Lecturer of Economics, Debre Berhan University, College
of Business and Economics
meetmame@me.com

4/6/2013 Prepared by Mohammed S. 65

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