Aspect of development project prepation

Chapter 2:Aspects of project preparation and analysis

Mohammed seid HussenLecturer of Economics

Debre Berhan University

College of Business and [email protected]

March, 2013

04/11/2023 Prepared by Mohammed S. 2

Wind power

Bagasse

Hydro power

Animal waste

Sewage /wastewater

Landfill

Forestry

Bio-fuelsTransport

Heavy Industry

04/11/2023 by Mohammed S. 3

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


• market study should include determination of potential demand for the

project’s output and the volume at given price range

target grouptime 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

04/11/2023 by Mohammed S. 10

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/.

Situational Analysis and Specifications of Objectives

Collection of Secondary Information

Conduct of Market Survey

Characterization of the Market

Demand Forecasting

Market Planning

1104/11/2023 Mohammed Seid

Key steps in market and demand analysis and their inter-relationships

04/11/2023 by Mohammed S. 12

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?

04/11/2023 by Mohammed S. 13

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

Method Defini-tion

Sources Advan-tages

Disadvan-tages

Documentary Research

Systematicreading of needs data compiled by secondary sources

Libraries;Scholars;Officials;Specialized agencies

Already col-lected;Low level of ef-fort to analyze.

Not always avail-able on topics needed;Can be dated; usu-ally incomplete.

Statistics and Plan-ning Data

Sorting and analyzing in-formation from extant data bases

Ministry data bases; Plan-ning de-partments;Statistics centres

Available in most line min-istries;easy to obtain;Can be volumi-nous

May contain gaps;usually unaggre-gated;Requires a special-ist to analyze it.

Key InformationApproach

Interviewing of knowledge-able sec-ondary sources

People or agencies which are in a position to know about the subjects

Economical; re-lies on knowl-edgeable infor-mants.

Informants may in-ject their own bi-ases.

Secondary sources

SEVEN DATA COLLECTION TECHNIQUES FOR DEVELOPMENT PROJECT NEEDS ASSESSMENT

Method Definition Sources Advan-tages

Disadvan-tages

Interview Soliciting andRecordingInformation By asking Questions

Primary sources like project ben-eficiaries

Goes right to the source of the in-formation.

Can be expensive;requires some spe-cial interviewing skills.

Focus Group Small Group discussion fo-cuses on devel-opmentProblems.


Introduces ele-ment of spon-taneity since discussion is un-guided.

Somewhat of an ar-tificial setting for such a discussion may inhibit some.

Question-naire

Published list of questions to be answered by every infor-mant.

Primary or secondary sources

When well done, it obtains highly reliable data.

Difficult to con-struct;Requires high de-gree of skill.

DirectObservation

Firsthand expo-sure of the project team to the behaviour or phenomenon being assessed.


Lacks artificiality of other meth-ods;Gives assessor good exposure.

Can be expensive if lots of exposure is required; difficult to standardize data.

Primary sources

SEVEN DATA COLLECTION TECHNIQUES FOR DEVELOPMENT PROJECT NEEDS ASSESSMENT

Forecasting

• Predicting the future• Qualitative forecast

methods– subjective

• Quantitative forecast methods– based on mathematical

formulas

12-171704/11/2023 Mohammed Seid

Types of Forecasting Methods

• Depend on– time frame– demand behavior– causes of behavior

12-181804/11/2023 Mohammed Seid

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

12-191904/11/2023 Mohammed Seid

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

12-202004/11/2023 Mohammed Seid

• Analytical • Cause effect relationship basis• Quantitative• Explicit

Causes of Behavior


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 Sends Initial Questionnaire

Each expertwrites response(anonymous)

Coordinatorperformsanalysis

Coordinatorsends updatedquestionnaire

Coordinatorsummarizesforecast

Consensusreached?

YesNo


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 - timeThe important time series projection methods are:

– Trend Projection Method– Exponential Smoothing Method– Moving Average Method


Linear Trend Line

12-30

y = a + bx

wherea = intercept of the relationshipb = slope of the linex = time periody = forecast for demand for period x

b =

a = y - b x

wheren = number of periods

x = = mean of the x values

y = = mean of the y values

xy - nxy

x2 - nx2

xnyn


Least Squares Example

12-31

x(PERIOD) y(DEMAND) xy x2

1 73 73 12 40 80 43 41 123 94 37 148 165 45 225 256 50 300 367 43 301 498 47 376 649 56 504 81

10 52 520 10011 55 605 12112 54 648 144

78 557 3867 650


Least Squares Example (cont.)

12-32

x = = 6.5

y = = 46.42

b = = =1.72

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

3867 - (12)(6.5)(46.42)650 - 12(6.5)2

xy - nxyx2 - nx2

781255712


12-33

Linear trend line y = 35.2 + 1.72x

Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units

70 –

60 –

50 –

40 –

30 –

20 –

10 –

0 –

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

Actual

Dem

and

Period

Linear trend line


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

Trend Projection Method


Exponential Smoothing

12-35

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


Exponential Smoothing (cont.)

12-36

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


Effect of Smoothing Constant

12-37

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

If = 0, then Ft +1 = 0Dt + 1 Ft = Ft

Forecast does not reflect recent data

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


Exponential Smoothing (α=0.30)

12-38

F2 = D1 + (1 - )F1

= (0.30)(37) + (0.70)(37)= 37

F3 = D2 + (1 - )F2

= (0.30)(40) + (0.70)(37)= 37.9

F13 = D12 + (1 - )F12

= (0.30)(54) + (0.70)(50.84)= 51.79

PERIOD MONTH DEMAND

1 Jan 37

2 Feb 40

3 Mar 41

4 Apr 37

5 May 45

6 Jun 50

7 Jul 43

8 Aug 47

9 Sep 56

10 Oct 52

11 Nov 55

12 Dec 54



12-39

FORECAST, Ft + 1

PERIOD MONTH DEMAND ( = 0.3) ( = 0.5)

1 Jan 37 – –2 Feb 40 37.00 37.003 Mar 41 37.90 38.504 Apr 37 38.83 39.755 May 45 38.28 38.376 Jun 50 40.29 41.687 Jul 43 43.20 45.848 Aug 47 43.14 44.429 Sep 56 44.30 45.71

10 Oct 52 47.81 50.8511 Nov 55 49.06 51.4212 Dec 54 50.84 53.2113 Jan – 51.79 53.61



12-40

70 –

60 –

50 –

40 –

30 –

20 –

10 –

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

Actual

Ord

ers

Month

= 0.50

= 0.30


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

12-414104/11/2023 Mohammed Seid

Moving Average:Naïve Approach

12-42

Jan 120Feb 90Mar 100Apr 75May 110June 50July 75Aug 130Sept 110Oct 90

ORDERSMONTH PER MONTH

-120

90100

75110

5075

130110

90Nov -

FORECAST


Simple Moving Average

12-43

MAn =

n

i = 1 Di

nwhere

n = number of periods in the moving

averageDi = demand in

period i


3-month Simple Moving Average

12-44

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH

MA3 =

3

i = 1 Di

3

=90 + 110 + 130

3

= 110 ordersfor Nov

–––

103.388.395.078.378.385.0

105.0110.0

MOVING AVERAGE


5-month Simple Moving Average

12-45

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH MA5 =

5

i = 1 Di

5

=90 + 110 + 130+75+50

5

= 91 ordersfor Nov

––– ––

99.085.082.088.095.091.0

MOVING AVERAGE


Smoothing Effects

12-46

150 –

125 –

100 –

75 –

50 –

25 –

0 –| | | | | | | | | | |

Jan Feb Mar Apr May June July Aug Sept Oct Nov

Actual

Ord

ers

Month

5-month

3-month


Weighted Moving Average

12-47

WMAn = i = 1 Wi Di

where

Wi = the weight for period i, between 0 and 100 percent

Wi = 1.00

Adjusts moving average method to more closely reflect data fluctuations

n


Weighted Moving Average Example

12-48

MONTH WEIGHT DATA

August 17% 130September 33% 110October 50% 90

WMA3 = 3

i = 1 Wi Di

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

= 103.4 orders

November Forecast


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 = U x A x M x I x C x S x F x E x Y= 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 demandQ1 = quantity demanded in the base yearQ2 = quantity demanded in the following yearI1 = 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


• Suitable for estimating demand for intermediate products

• Also called as consumption coefficient methodSteps1. Identify the possible uses of the products2. Define the consumption coefficient of the product

for various uses3. Project the output levels for the consuming

industries4. Derive the demand for the project

END USE METHOD


END USE METHOD

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

Projected Demand for IndchemConsumption

CoefficientProjected Output

in Year XProjected Demand for

Indchem in Year X

AlphaBetaKappaGamma

2.01.20.80.5

10,00015,00020,00030,000Total

20,00018,00016,00015,00069,000


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 model1. Single Equation Model

Dt = a0 + a1 Pt + a2 Nt

• WhereDt = demand for a certain product in year t.

Pt = price of the product in year t.

Nt = income in year t.


ECONOMETRIC METHOD2. Simultaneous equation method

GNPt = Gt + It + Ct

It = a0 + a1 GNPt

Ct = b0 + b1 GNPt

• WhereGNPt = gross national product for year t. Gt = Governmental purchase for year t. It = Gross investment for year t.

Ct= Consumption for year t.5904/11/2023 Mohammed Seid

Advantages• The process sharpens the understanding of

complex cause – effect relationships• This method provides basis for testing

assumptionsDisadvantages• It is expensive and data demanding• To forecast the behaviour of dependant

variable, one needs the projected values of independent variables

ECONOMETRIC METHOD


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– Excessive data requirement


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….


04/11/2023 Prepared by Mohammed S. 65

End

Mohammed Seid HussenLecturer of Economics, Debre Berhan University, College

of Business and [email protected]

Documents

Aspect of development project prepation