4. Technology Forecasting I.pdf - Naarm

th105 FoCARSFoundation Course For Agricultural Research Service

Digital Repository of Course Materials

• Stakeholder analysis

• Gender Issues in Agricultural

• Technology Assessment

• Technology Forecasting -II

• Technology Diffusion in Agriculture Sector

• Participatory Technology Development

• On–Farm Research and Constraint Analysis in Technology Adoption

• ITK and its Relevance for Sustainability

• Reforming the Agricultural Extension System in India

• Modernizing National Agricultural Extension Systems:

• A Practical Guide for Policy-Makers of Developing Countries

• Tech Forecasting -I

Course Coordinators K. Kareemulla and S. Ravichandran

Support Team P. Krishnan and P. Namdev

1

TECHNOLOGY FORECASTING

– AN OVERVIEW

D. Rama Rao1

Technology Forecasting (TF) is a planning tool to be at use in

dynamic environments which undergo rapid changes. The

technological backwardness of developing countries is primarily due

to lack of planned attention to the maintenance, development of

technology capabilities and utilising resources efficiently. Rapid

technology progress and the increased rate of obsolescence of

technologies necessitate technology forecasting for any planning

process. Since technologies play a major role in planning of business,

industry, government and society's growth, it becomes essential to

determine its direction and magnitude by systematic analysis and

study.

TF can be defined as a probabilistic prediction of technological

changes in terms of future characteristics of useful machines, systems

or procedures. In other words, technology forecasting attempts to

predict rate of technology advance. Primarily TF attempts to bring

potential future technology into focus. Decision-makers are concerned

about the desirable and undesirable effects of fast growing

technologies. Anticipation of such technologies serve as early warning

signals before a particular technology is imported or manufactured

indigenously.

Need for technology forecasting

The need for technology forecasting is essential for the following

reasons:

Future oriented R & D

Prevention of import of obsolete technologies

Anticipating technical innovation

Shift towards appropriate technology

Effective technology transfer

Development of exportable technologies

1 Former Director, NAARM

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Leap across generations

Rapidity of innovations

Trade restrictions

Avoid surprises

Elements of technology forecasting

There are four essential elements in a technology forecast, namely:

Time

Qualitative

Quantitative

Probability of occurrence depending on the purpose

The time element deals with resource-time relationship. The qualitative

element attempts to identify the factors that are likely to change the

activities or technology trends in areas of interest to the forecaster.

The quantitative element attempts to measure and assess the level of

performance of various technologies. The element of probability of

occurrence attempts to predict different alternatives and its confidence

level.

Technology forecasting methods

The available technology forecasting techniques, both qualitative

and quantitative methods, can be classified into two broad

categories, viz. Exploratory forecasting and normative forecasting.

Various TF techniques are given in Table-1.

Table-1: Technology forecasting techniques

Exploratory forecasting Normative forecasting

Delphi method Operations research models

Analytical methods Network techniques

Multivariate analysis Cross-impact analysis

Trend extrapolation Relevance trees

Growth models SEER

Brainstorming Morphological analysis

Scenario writing Dynamic modeling

Substitution analysis

Input-output models

Monitoring

National Academy of Agricultural Research Management

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i) Exploratory methods

Exploratory methods start with the present state of technology and

quantitatively project future possibilities. In other words, it provides

means for exploring the shape of tomorrow given the state, trends, and

promises of today. These methods are applicable in systems which grow

under a specific environment. Exploratory methods can be grouped

into four categories, viz., intuitive, extrapolative, growth curves and

technology monitoring.

a) Intuitive methods

Intuitive methods are based on the ability of one or more experts to

assess the future. Some commonly used intuitive methods are:

Individual forecasting: Experts in specific fields often prepare

forecasts in their field. This method lacks multidisciplinary interaction,

and is often biased. Probability of failures is high.

Opinion polls: Opinions are obtained from several individuals and

combined. The minimum sample size should be twenty. One of the

disadvantages of this method is that the minority opinion will be

drowned by majority opinion irrespective of its significance.

Panels: A group of experts interact across a table. This has the

advantage of being multidisciplinary. The major disadvantages are:

Extreme views get eliminated

Vociferous or dominant personalities influence the forecast

Some experts don’t agree failure of their earlier stand

Subordinates refrain from speaking against bosses views.

Brainstorming: In situations where evaluation of unconventional

alternatives is needed, the effects of bureaucracy and band wagon have

to be reduced. Brainstorming is a frank and free unconventional

alternative search technique.

Scenario writing: This is a creative method of deriving possible

composite scenarios of future by considering alternate options.

Delphi: The delphi is a group process technique for eliciting, collating

and generally directing expert judgment towards a consensus on a

particular topic. This study is typically conducted by mail through

several rounds of questionnaires for convergence of opinions. The

detailed procedure is described later.

b) Extrapolative methods

Trend extrapolation: The past trend is projected into the future using

linear or semi-logarithmic or double logarithmic extrapolation or curve

fitting techniques. These are the most widely used techniques and are

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cheap in terms of fund requirements. Some extrapolation techniques

are: Linear extrapolation, exponential extrapolation and trend

correlation.

c) Growth curves

Technology capabilities grow mainly in `S' shaped fashion having a

finite limit. In such cases, use of other techniques will lead to erratic

end results. Some of the commonly used growth models are:

Pearl curve

Gompertz curve

Fisher-pry curve

Substitution: In cases where technology substitutions occur

substitution models can be used which are very simple for usage.

d) Technology monitoring

Technology grows many times through breakthroughs, and

prediction of future is possible by monitoring the early signals of the

innovation. This is mainly achieved by a search of patents, radical

alternatives and literature for the embryo of new technologies.

Multivariate analysis: In cases where the cause and effect

relationship is not obvious, multivariate analysis between the

dependent variable and the independent variables can give results

which can be used in forecasting.

ii) Normative methods

Normative methods begin with an objective/ goal and work backwards

to the present to find out the best approach to realise the predetermined

objective. This is need-based in which needed capabilities are

identified for the achievement of the goals. Some of the available

normative techniques for TF are:

Network techniques: These techniques are used for mission-

oriented planning exercises mainly to analyse the road blocks to

achieve the final target of objective. These are widely used and known

mainly as SOON charts (Sequence of opportunities and negatives).

System for Event Evaluation and Review (SEER): This is a modified

variance of Delphi ideal for corporate exercises not necessarily the ones

aimed at consensus. This consists of a single round of event evaluation.

Cross-impact analysis: Different events interact with different

strength towards other course of events, and forecasts are made using

these interactions and random numbers so as to derive combine

forecasts or for depicting the interaction of events.

Morphological analysis: This is a method for structured thinking


5

identifying all possible system alternatives. The main idea behind using

this method is not to miss any options.

Relevance trees: A number of steps may be available to achieve a

given objective but each step may be different in its effectiveness in

terms of cost or profit or probability of success. Evaluation of these

multiple choices may be done through decision trees.

Dynamic modeling: These are computer aided structural modeling

techniques in which time varying effects can be explicitly considered.

Examples: Forvestor model, Mesarovic model, Meadows model (club

of Rome), etc.

Some of the methods are simple variants of techniques used in other

areas. These methods are mainly used to eliminate subjective errors

associated with intuitive forecasting techniques.

Criteria for application of TF

Few searching questions that are relevant for application of

Technology Forecasting are:

1. Is the system prepared to absorb / contribute to arising technology

needs of the Society?

2. Does the system has any mechanism to

anticipate changes?

assess needed technologies?

assign strategies to implement the technologies?

Choice of techniques

The choice of selecting techniques for a particular situation is a

complex problem, and depends on many factors such as:

Purpose for which the forecast is being made

Reliability needed

Precision of the data

Time period and resources available

Ability to combine various interacting factors

The cost of using different forecasting methods vary considerably,

dynamic modelling being more expensive. In general, the cost increases

with the accuracy needed. It is preferable to use a combination of

methods for improving the reliability of forecasts through mutual

reinforcement.

The approaches to be selected for forecasting and elements to be

assessed in forecasting will depend on the purpose for which

technology forecasts are being prepared. Technology forecasting

techniques are used based on purpose (Holroyd, 1979), and various

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methods used for each of the purpose are given in the Table - 2. and

their applications are given in Table-3.

Table-2: Choice of selection of techniques

Table-3: Application of technology forecasting

Technique Questions it tries to answer Applications

Trend

extrapolation

Past versus future

Is there a rate change? Why rate is declining?

Future performance levels

Total market size

R&D Market

planning

Corporate

planning

Growth

curves

Is there a limit?

Sales growth of new product

Setting up new plant capacity

New product

identification

Production

Delphi When can, will or should certain events occur

Setting up goals

What are the elements of future

R&D

Market research

New product

identification

Long range

planning Morphology Can we have a new way?

Did we consider all alternatives? Listing

diversification alternatives

Value

engineering

design

R&D Marketing Cross-impact

analysis

Can interaction of events delay or accelerate the

growth?

LRP R&D

Relevance

tree

Target-orient planning

Problem solution convergence R&D

The Technology Forecasting techniques which can be used in search of

Problem

identification

Relevant factors Trends of

parameters

Relationships

among

parameters

Implications for

action

Brainstorming Brainstorming Extrapolation Dynamic

modelling

Expert consultation

Delphi Delphi Growth curves Morphology Scenario building

Expert opinion Expert opinion

Monitoring

Scenario writing

Substitution Cross-impact

analysis

Relevance tree

Modelling

& Simulation


7

Mathematical

modelling

Thought provocation R&D New products

How to assess alternative policies?

How to change course of action to achieve a

desired result?

Policy

analysis

R&D

Production

marketing

LRP Scenario

writing

Innovation stimulation

Consideration of all interactions

Organisational alternatives

Policy

planning

LRP

R&D New

products

Technology

monitoring

What are the new areas?

What are the likely new products? Prediction of

break through Threats to the existing products

R&D

Production

Production

demand/repla

cement

decisions

R&D Marketing New product

d

e

m

a

n

d

Case-1: Delphi study of future of Indian agriculture

The results of Delphi study conducted during 1976-77 to forecast the

future of Indian Agriculture (Rohtagi et al, 1979) are given below. The

expert panel for this study consisted of scientists and faculty from

universities and R & D laboratories and administrators from

government departments. The questionnaires were sent to 143 persons

in the first round. But the actual respondents were 39 in the first round

and 23 in the second round. The experts were asked to indicate the year

of achievement of the events. Some of the forecasts and the actual

achievements, wherever applicable and available, are given below:

Event Forecast

(achieving

year)

Actual

achievement

Total irrigation to 50 m.ha.

1992

Achieved Double crop to 50 m.ha. 1993 Achieved

Food grains to exceed 141 m.t. 1982 Achieved 1983-84

75% bread fortification by soybean 1991 Not achieved

Fertilizer consumption of 50 kg/ha of cropped

area

1988 Achieved

Fertiliser consumption of 75 kg/ha of cropped

area

2003 Likely to exceed

Post harvest losses below 10% 1986 Achieved

Post harvest losses below 5% 1998 Not possible

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Milk to exceed 40 m.t. 1992 Achieved 1985

Case-2: Delphi study of future of sorghum in India

A forecast of sorghum scenario in India was carried out using

Delphi and trend methods (Rama Rao and Kiresur, 1992). The

sorghum data for total cropped area and total production during

1950-1990 indicate great deal of fluctuation owing to various factors

such as demand, prices, rain fall, seed quality and farmers priorities.

The remarkable drop in area under Sorghum during late 1960s is

noteworthy. This was a forced situation mainly due to near constant

demand coupled with enhanced yield due to introduction of HYVs in

this period i.e. green revolution. Given such an uncertain situation,

forecast of sorghum was made in terms of its cropped area,

production, productivity, demand for alternate uses and relative

price structure.

Scenario during 1992: India is the largest producer of sorghum with

about 11 per cent of the total area under food grains (128 m.ha) and 7

per cent of the total food grain production (172 m. tonnes) during

1990-91. Rabi sorghum is grown in an area of about 5.9 m.ha. and

its productivity is 594 kg/ha. Because of its good quality it is

preferred for human (traditional) consumption. About 71 per cent of

the total sorghum production comes from kharif crop which is grown

in a larger area (8.6 m.ha) and has improved productivity (974

kg/ha.). Though the production and productivity are high, its

relatively poor quality has led to low demand. Thus, there is a

gradual reduction in its area over the years. This situation may not

change unless and until superior quality varieties/hybrids are evolved

and/or alternate uses are made feasible.

Forecast: The forecasts made by Delphi are presented below:

Forecasted Parameter/Event Actual for 1990-91 Forecast for 2000

AD

Area (million ha.)

14.50

12.00 Production (million tonnes) 11.88 12.09

Yield (kg/ha) 819 949

Percentage area under HYVs 52.1 60.0

No. of years for sorghum to

become attractive for alternate

uses.....:

N.A.

10


9

The forecast for area under HYVs is 67 per cent by trend method and

60 per cent by Delphi method; the latter appears logical since

increased area under HYVs should reflect in decrease of cropped area

as the rate of demand change is unlikely to increase by 2000 AD. With

regard to the procurement prices of cereals, the forecast indicates

continuation of government policies as in the past. To promote

sorghum production, the experts strongly opined for a relatively higher

support price than now. This refers to governmental policy on food

grain procurement. As a corollary to present forecast, the total cropped

area will decrease to about 10 m. ha. when the average yield increases

to about 1500 Kg/ha. But the forecast does not indicate such a

possibility by 2000 AD. However, this will be relevant as a long term

forecast or alternatively should there be any breakthrough on the HYVs

front. Another area of concern is the advancements in dry land crops.

Any breakthrough in these crops will have severe impact on sorghum as

such crops are likely to grow at the expense of sorghum.

Bibliography

Ayres, R.U., 1969, Technological Forecasting and Long Range

Planning, McGraw - Hill, New York.

Box, G.E.P. and Jenkins, G.M., 1976, Time Series Analysis:

Forecasting and Control, Holden- Day, San Fransico.

Bright, J.R., 1978, Practical Technological Forecasting, Industrial

Management Centre, Austin. Cetron, M.J., 1971, Technological

Forecasting - A Practical Approach, Gardon & Breach

Sci.Pub., London.

Coates, J.F., 1976, Technology Assessment - A Tool Kit, Chemtech,

372-383.

Gordon, T.J. and Hayward, 1968, Initial Experiments with the Cross

Impact Matrix Method of Forecasting, Futures, Vol.1, No.2., pp

100-116.

Holroyd, P. 1979, Some Recent Methodologies in Future Studies,

R&D Mgmt, Vol 9, No3, pp107-116.

Jones, H. and Twiss, B.C., 1979, Forecasting Technology for

Planning Decisions, Macmillan, London.

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Lanford, H.W., 1972, Technological Forecasting Methodologies,

American Management Association, New York.

Makridakis, S.,Wheelwright, S.C. and Megee, V.E., 1983,

Forecasting: Methods and Applications, Wiley, New York.

Martino, J.P., 1972, An Introduction to Technological Forecasting,

Gardon and Breach Sci. Pub., New York

Martino, J.P., 1983, Technological Forecasting for Decision Making,

Elsevier, North-Holland. Porter, A.L., 1980, A Guidebook for

Technology Assessment Impact Analysis, Elsevier, New York.

Rama Rao, D. and Kiresur, V., 1994, Technological Forecasting of

Sorghum Scenario in India, NAARM, Hyderabad.

Rohtagi, P.K., Rohtagi, K. and Bowonder, B., 1979,

Technological Forecasting, Tata McGraw-Hill

Pub.Co.Ltd.,Delhi.

Saaty, T.L., 1988, Multicriteria Decision Making, McGraw-Hill, New

York.

Saaty, T.L. and Kearns, K.P., 1985, Analytical Planning, Pergamon

Press, Oxford.

Wheelright, S.C. and Makridakis, S., 1973 & 1980, Forecasting

Methods for Management, Wiley, New York

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