Introduction to Technology Forecasting Activities to be carried out Trend Extrapolation [Growth Curve Fitting] Trend Impact Analysis(TIA) Precursor Analysis Long Wave analysis MONITORING AND INTELLIGENCE METHODS Technology Monitoring and Steps in Technology Monitoring Bibliometrics Research Profiling Patent Analysis Text Mining

1. JAMNALAL BAJAJ INSTITUTE
OF
MANAGEMENT STUDIES
MIM
Third Year (Sem VI)
(2015 - 2018)
TECHNOLOGY FORECASTING
NAME ROLL NO
MUFADDAL NULLWALA 15-I-131

2. Topic To be Discuss
1. Introduction to Technology Forecasting
2. Activities to be carried out
3. Trend Extrapolation [Growth Curve Fitting]
4. Trend Impact Analysis(TIA)
5. Precursor Analysis
6. Long Wave analysis
7. MONITORING AND INTELLIGENCE METHODS
8. Technology Monitoring and Steps in Technology Monitoring
9. Bibliometrics
10.Research Profiling
11.Patent Analysis
12.Text Mining

3. • Technology forecasting, in general, applies to all purposeful and systematic
attempts to anticipate and understand the potential direction, rate,
characteristics, and effects of technological change, especially invention,
innovation, adoption, and use. One possible analogy for TF is weather forecasting:
Though imperfect, TF enables better plans and decisions.
• A good forecast can help maximize gain and minimize loss from future conditions.
Introduction to Technology
Forecasting

4. • Trend analysis is the widespread practice of collecting information and attempting to spot a
pattern.
• Trend analysis is a mathematical technique that uses historical results to predict future outcome.
• Trend analysis is used for predicting future events.
• Technological Forecasting (TF) is concerned with the investigation of new trends, radically new
technologies, and new forces which could arise from the interplay of factors such as new public
concerns, national policies and scientific discoveries. Many of these forces are beyond the
control, influence and knowledge of individual companies.
• Technology Foresight is a combination of creative thinking, expert views and alternative
scenarios to make a contribution to strategic planning.
Introduction to Trend Analysis

5. 1. Planning the exercise and getting started
• When planning to start either forecasting or foresighting it is useful to consider:
• The reasons for doing it.
• What resources will be needed and what resources can be made available.
• How long will it take?.
• How to learn the techniques and improve the overall process?
2. Establish the need
• In order to assess if a more systematic approach will be useful the following factors can be
considered:
• The criticality of technologies used by the company.
• The maturity and rate of change of critical technologies.
• The nature of the R&D strategy, (eg whether offensive or defensive).
• The complexity and flexibility of markets and the overall business environment.
Overall process:

6. 3. Coordinating resources
4. Establish and improve the process: forecasting
• Primary activities
• information gathering
• Analysis

7. Activity 1: Collection of relevant information
The major issues to be addressed are:
○ What information and what kind of data are relevant?
○ What sources of information are to be used?
○ How accurate is it?
○ What systems need to be set up to provide information and data on technological developments and trends?
Practical decisions arising from consideration of these issues include:
a. Which journals to monitor, and how.
b. Which conferences and trade fairs to attend.
c. How to share information.
d. Who should participate in which networks.
e. How can an individual’s relevant expertise best be used?
f. What internal data to collect and external data to acquire.
g. How to track performance parameters of competitors’ products?
Activities to be carried out:

8. Activity 2: Analysis of the data by individuals and by various methods and techniques
The major issues to be addressed are:
○ Whose expertise should be used?
○ Which methodologies or techniques are appropriate?
○ Against what criteria or objectives are the analyses to be judged?
○ What data should be used or is relevant?
○ Who are the relevant people to apply the techniques to the data?

9. An extrapolation is kind of like an educated guess or a hypothesis.
When you make an extrapolation, you take facts and observations
about a present or known situation and use them to make a
prediction about what might eventually happen.
Extrapolation comes from the word extra, meaning “outside,” and a shortened form of the word
interpolation. Interpolation might sound like a made-up word, but it’s not. An interpolation is an
insertion between two points. So an extrapolation is an insertion outside any existing points. If you
know something about Monday and Tuesday, you might be able to make an extrapolation about
Wednesday.
Trend Extrapolation [Growth Curve
Fitting]

10. ● Extrapolation and trend analysis rely on historical data to gain insight into future developments
● This type of forecast assumes that the future represents a logical extension of the past and that
predictions can be made by identifying and extrapolating the appropriate trends from the
available data.
● This type of forecasting can work well in certain situations, but the driving forces that shaped the
historical trends must be carefully considered. If these drivers change substantially it may be more
difficult to generate meaningful forecasts from historical data by extrapolation
● In trend extrapolation, data sets are analyzed with an eye to identifying relevant trends that can
be extended in time to predict capability. Tracking changes in the measurements of interest is
particularly useful.
For example, Moore’s law holds that the historical rate of improvement of computer processing
capability is a predictor of future performance (Moore, 1965). Several approaches to trend
extrapolation have been developed over the years

11. Time
Adoptio
n
The growth pattern of a technological capability is
similar to the growth of biological life. Technologies
go through an invention phase, an introduction and
innovation phase, diffusion and growth phase, and
a maturity phase. In doing so, their growth is similar
to the S-shaped growth of biological life.
Technological forecasting helps to estimate the
timing of these phases.
This growth curve forecasting method is particularly
useful in determining the upper limit of
performance for a specific technology.
Forecasting by growth curves involves fitting a
growth curve to a set of data on technological
performance, then extrapolating the growth curve
beyond the range of the data to obtain an estimate
of future performance.
Phases of the growth
curve

12. • The upper limit to the growth curve is known.
• The chosen growth curve to be fitted to the historical data is the correct one.
• The historical data gives the coefficients of the chosen growth curve formula correctly
Two types of s-curve formulations
Which can be adopted based on the requirements of the forecasting.
• Pearl-Reed Curve
• Gompertz curve
Activity involves three assumptions

13. • This is the only approach which can be used when the system is bound by a limit.
• When one has a set of historical data, it has to be decided which of the growth curves will be
appropriate to use.
• Pearl and Gompertz have different applications.
• In case of broadcasting of new technology, initially there are only few suppliers, few after sales
facilities, few users etc.
• As broadcasting progresses further substitution is easier, but easiest applications are normally
completed first and the tougher ones later.
• Under this situation, Pearl curve is more appropriate. But, where success of broadcasting does not
make further substitution easier, Gompertz curve is more appropriate.
Advantages

14. Application
Growth curves could be used for forecasting how and when a given technical approach will reach its
upper limit.
Analysis of most of the technologies shows that when a technical approach is new, growth is slow
because of initial problems.
Once these are overcome, growth in performance is rapid.

15. • TIA was developed in the late 1970s to answer a particularly difficult and important question in
futures research.
• Quantitative methods based on historical data are used to produce forecasts by extrapolating
such data into the future, but such methods ignore the effects of unprecedented future events.
• Quantitative methods assume that forces at work in the past will continue to work in the future
and future events that can change past relationships or deflect the trends will not occur or have
no appreciable effect.
• Methods that ignore future possibilities result in surprise-free projections and, therefore, are
unlikely in most cases.
• The set of future events that could cause surprise-free trends to change in the future
must be specified. When TIA is used, a data base is created of key potential events, their
probabilities, and their impacts.
• TIA forecasts were used by the Federal Aviation Administration, Federal Bureau of Investigation,
Joint Chiefs of Staff, National Science Foundation, Department of Energy, Department of
Transportation, the State of California, and other U.S. agencies.
• TIA is a forecasting method that permits extrapolations of historical trends to be modified in
view of
expectations about future events. This method permits an analyst, interested in tracking
a particular trend, to include and systematically examine the effects of possible future events
that are believed important.
Trend Impact Analysis(TIA)

16. • A curve is fitted to historical data to calculate the future trend, given no unprecedented future
events; and
• Expert judgments are used to identify a set of future events that, if they were to occur, could cause
deviations from the extrapolation of historical data. For each such event, experts judge the
probability of occurrence as a function of time and its expected impact, should the event occur, on
the future trend. An event with high impact is expected to swing the trend relatively far, in a
positive or negative direction, from its un-impacted course.
Two principal steps are necessary:

17. Precursor a person or thing that comes before another of the same kind
The National Academy of Engineering workshop definition of an accident precursor is any event or group of
events that must occur for an accident to occur in a given scenario. “one that precedes and indicates the
approach of another.”
• Precursor analysis is a systematic approach to address catastrophic failures that are usually preceded by
precursory events, although observable, are not recognizable as harbingers of a failure until after the fact.
• It is a predictive tool, a proactive safety measure that doesn’t simply access the trend of pervious failures but it
has a precursor information to predict failures that may or may not occur in the past.
Precursor Analysis

18. • Creating an event database
• Qualitative & Quantitative assessment
Important steps in Precursor
Analysis

19. Creating an event database
Below are the sources for Precursor information to create event database
• Major technology failures
• Less impact incidences from multiple sources
• Precursor information from each actual or potential failure events
• Information from relevant multiple sources

20. Qualitative Assessment : Generally involves panel of experts looking at resulting precursor data to
define trends to specific events of interest.
Quantitative Assessment : It incorporates statistical analysis by:
a) Regression Trees
b) Generalized linear models
c) Principal component analysis
d) Bayesian Network Analysis
Qualitative & Quantitative
assessment

21. • Proactive rather than reactive
• Cost effective option
• Reveals hidden accident causes
Advantages

22. • Long waves are also called as Kondratiev waves, supercycles, great surges are hypothesized cycle-
like phenomena.
• It is stated that the period of a wave ranges from forty to sixty years, the cycles consist of
alternating intervals between high sectorial growth and intervals of relatively slow growth
Long Wave analysis

23. • It helps to understand the causes and effects of common recurring events in technology throughout
history
• The causes documented by Long Wave analysis are inequality, social, political, environmental &
Technological. Effects can be good or bad and include technological advance, revolutions - and
revolution's contributing causes which can include cost, political intolerance, failed-freedoms and
opportunity & time required for implementation
Explanation of Long wave

24. • Monitoring, Environmental Scanning and Technology Watch, are suitable for making one
aware of changes on the horizon that could impact the penetration or acceptance of the
technologies in the marketplace.
• Environmental scanning can be thought of as the central input to future research.” However, the
output is too general to support a specific decision.
• “The objective of a monitoring system is simply to find early indications of possibly important
future developments to gain as much lead-time as possible “.
MONITORING AND INTELLIGENCE METHODS

25. Technology monitoring is one of the techniques, which can be used for monitoring breakthroughs
through forerunner events.
Most large manufacturing organizations have formed systems for continuously scanning the
technological environment, known as technology scanning/monitoring/intelligence.
Monitoring process has following steps:
1. Information Scanning.
2. Screening the scanned information.
3. Evaluation of the screened information & development of ideas.
4. Utilization of the evaluated ideas for R&D planning, project formulations, etc.
Technology Monitoring

26. Major steps involved in technology monitoring are:
• Scanning
• Filtering
• Analysis and Development of forecast
Steps in Technology Monitoring

27. A) Scanning: The idea behind scanning is to collect as much information that is available on the
particular field of technology. The information could cover the following aspects:
• Research plans and developments
• Environment of the technology
• Support of various governments for the technology
• Human skills and capabilities
• Social and ethical issues
• Benefits of the technology
Scanning

28. B) Filtering: In most cases, not all the information captured on the technology would be relevant for
a particular forecast.
• Hence, based on the forecast required, the necessary information is identified through filtering of
relevant data.
Filtering

29. C) Analysis and Development of forecast: This methodology is appropriate in situations such as
developing Research and Development plan,
• and identifying new sources of technology or emerging technologies.
Analysis and Development of forecast

30. • The advantage of this method is that it can be an efficient early warning device on threats to
existing products/services;
• or may provide signals on opportunities for new products or services.
• It is a useful method for decision makers.
Advantages

31. • To enable it to be useful a team is needed for carrying out the monitoring work and at least
two years of basic data collection as well as storage is necessary.
• All these may be possible only in the case of comparatively large corporations or industry
associations or government.
Difficulties

32. Technology monitoring is a useful tool for anticipating changes through continuously
monitoring the signals of change, especially the following:
a) to plan R&D,
b) to obtain new ideas on product/process/technology,
c) to identify possible sources for technology procurement/licensing etc.
Application

33. Bibliometric
Bibliometrics is defined as the measurement of texts and information.
Historically Bibliometric methods have been used to trace back academic journal
citations.
However, today Bibliometrics can be used to understand the past and even
potentially to forecast the future
Bibliometrics helps to explore, organize and analyse large amounts of historical
data helping researchers to identify “hidden patterns” that may help researchers
in the decision making process.
Important Terminologies
Unit of analysis, Impact, Normalization, Self-citation, Citation window,
Fractionalization, Indicator

34. Bibliometric Software :
The Application below can be downloaded, free of charge. They cover all sorts of things, from
management and conversion of data to construction of matrices and visualization, as well as
implementation of statistical functions that can be personally designed.
1. Publish or Perish
Based on data from Google Scholar, it creates bibliometric analyses of researchers. It calculates the
values of several indicators
2. R
A programming language and development environment, basically used for statistical calculations and
construction of graphs. Freely available and very flexible.
3. CiteSpace
A program to analyse, visualize and cluster (mainly) bibliographic data downloaded from Web of Science.

35. Databases for Bibliometric
analysis1.Google Scholar (Google)
Google Scholar indexes a vide variety of scientific literature available on the Web:
journals, books, preprints, reports and material from digital archives. The
coverage from before 1996 is weak.
2. ISI Web of Science(Thomson Reuters), Scopus (Elsevier)
Gives access to three citation indexes:Science Citation Index Expanded
(coverage 1945-). Social Sciences Citation Index (coverage 1956-). Arts &
Humanities Citation Index (coverage 1975-). All in all, they cover approximately
10,000 refereed journals.

36. Patent Analysis
• Patents are useful for competitive analysis and technology trend analysis
• Patents have always been analysed in R and D project management to
assess competitive position and to avoid infringement.
• Patent analysis is also a valuable approach that uses patent data to derive
information about a particular industry or technology used in forecasting.
• Patent analysis has been shown to be valuable in planning technology
development from the analysis of strategy at a national level to modelling
specific emerging technologies
• Patent data is usually freely accessible in most countries and several
guidelines have been introduced to enhance the technique using keywords
and categorization.

37. Text Mining:
The process of distilling actionable insights from text

38. Research Profiling
Research profiling refers to the process of construction and
application of user profiles generated by computerized data
analysis.
This involves the use of algorithms or other mathematical
techniques that allow the discovery of patterns or
correlations in large quantities of data, aggregated in
databases. When these patterns or correlations are used to
identify or represent people, they can be called profiles.

39. The technical process of profiling can be separated in several steps:
1. Preliminary grounding
2. Data collection
3. Data preparation
4. Data mining
5. Interpretation
6. Application
7. Institutional decision
Research Profiling

40. Application domains
Profiling technologies can be applied in a variety of different domains and for a
variety of purposes. These profiling practices will all have different effect and
raise different issues.
e.g. 1. In the financial sector, institutions use profiling technologies for fraud
prevention and credit scoring.
2. In the context of employment, profiles can be of use for tracking
employees by monitoring their online behaviour, for the detection of fraud by
them, and for the deployment of human resources by pooling and ranking their
skills

41. THANK YOU!