Technology forecasting

Technology forecasting is a technique to forecast the future characteristics of useful technological machines, procedures or techniques

Primarily, a technological forecast deals with the characteristics of technology, such as levels of technical performance, like speed of a military aircraft, the power in watts of a particular future engine, the accuracy or precision of a measuring instrument etc. The forecast does not have to state how these characteristics will be achieved.
Secondly, technological forecasting usually deals with only useful machines, procedures or techniques. It excludes services or techniques intended for luxury or amusement.
Methods of technology forecasting

Commonly adopted methods of technology forecasting include the Delphi method, , growth curves and extrapolation.

The Delphi method relies on a panel of experts. The expert answers questionnaires in two or more rounds and after each round a facilitator provides the summary of the answer along with the reason to the participants. . Thus, participants are encouraged to revise their earlier answers in light of the replies of other members of the group. It is believed that during this process the range of the answers will decrease and the group will converge towards the "correct" answer. Based on a pre defined stop criteria the process is stopped and the mean or median scores of the final rounds determines the result.

A growth curve is a process where values for the measured property like population, body weight etc are plotted on a grapg as a function of time.

Extrapolation is the process of constructing new data points outside a discrete set of known data points. Its results are often less meaningful than interpolation, and are subject to greater uncertainty.

Reasons for combining forecasts
The reason forecasts go wrong because they ignore related fields. A given technical approach may have been superseded by another technical approach which the forecaster might have ignored. .
Inconsistency between forecasts is another important reason for failure. Because of these problems, it is often necessary to combine forecasts of different technologies. Therefore it is better to combine forecasts obtained from different methods rather than concentrating on a single method. If this is done, the strengths of one method may help compensate for the weaknesses of another.
Trend curve and growth curves
The growth curves and a trend curve are used in combination to allow the forecaster to draw some conclusions about the future growth of a technology which might not be possible, were either method used alone.
A single method like growth curve might not be enough to say anything about the time at which a given technical approach is likely to be supplanted by a successor approach.
Trend curve alone cannot say anything about the ability of a specific technical approach to meet the projected trend, or about the need to look for a successor approach. Thus the need for combining forecasts.
Identification of consistent deviations
We assume that the scatter data points about a trend curve are due to random influences which are neither controllable nor measurable. However, consistent deviations may represent something other than just random influences
The opportunity to apply an analogy comes where such consistent deviations are identified. The typical events which bring about deviations from a trend are wars and depressions. Thus the usefulness of combining analogies with a trend forecast is to predict deviations from the trend deviations which are associated with or caused by external events or influences.
As with other uses of analogy, it is important to determine the extent to which the analogy between the event used as the basis for the forecast, and the historical model event, satisfies the criteria for a valid analogy.
Forecasts of different technologies
We can get different forecasts from a single technology but combining these forecasts as less important than combing forecasts derived from different technologies because of the fact that technologies may interact or be interrelated in some fashion. Another reason for this is that of consistency in an overall picture or scenario. “One of the simplest examples of interacting trends is the projection to absurdity, i.e. simply projecting the given data indefinitely without getting any specific result. For instance, if one simply projects recent rates of growth of world population, one arrives at some fantastic conclusions about the density of population in a particular place by various dates in the next millennium.”(wikipedia)

Some other trends which can confidently be expected to not continue indefinitely are:
Annual production of scientific papers.
Number of automobiles per capita.
Kilowatt hours of electricity generated annually.
Another instance of interacting trends was in the case of the number of scientists in the U.S. growing faster than the overall population. Since 1940s through the 1960s, science as an activity in the United States grew exponentially. The number of dollars spent on R&D was growing faster than the GNP (in the 1960s).
If projected indefinitely, these two curves would give the result that eventually every person in the U.S. would be working as a scientist and the entire GNP would be devoted to R&D alone, which are however absurd conclusions. Thus it is clear that the scientific discipline of technology forecasting is not mere trend extrapolation but also involves combining forecasts
Uses
The method has considerable use in the manufacturing industry.


References

http://en.wikipedia.org/wiki/Technology_forecasting

. Klopfenstein, Bruce K. "Forecasting consumer adoption of information technology and services - Lessons from home video forecasting". Journal of the American Society for Information Science 1989 Jan;40(1):17-26.
Martino, Joseph (January 1983). Technological Forecasting for Decision Making, 2nd edition, North-Holland Makridakis, Spyros; Steven C. Wheelwright, Rob J. Hyndman (December 1998). Forecasting: Methods and Applications, 3rd edition, John Wiley

scenario planning and IPL 20-20

Wikipedia describes scenario planning as a strategic planning method that some organizations use to make flexible long-term plans. It is in large part an adaptation and generalization of classic methods used by military intelligence
This article is about the use of scenario planning in reinventing cricket with the help of 20-20 cricket. Although the format has been up and running, this article is attempt to understand the development process for this format of this game in the context of scenario planning. The different steps involved in the scenario planning as given in wikipedia has been discussed below with respect to 20-20 cricket.

Step 1 -Drivers for change/assumptions-
Although cricket had a mass following in India and some experts considered cricket as the religion of India , the challenge was to make it more popular and increase the viewership have which would have seemed a distant pipedream six months ago. The target segment to focus for increasing the popularity was the women segment which could not watch the game because of the amount of time involved in the game. Women in India remain very busy throughout the day. If she is housewife then she has to look after her family and if she is a working then the added pressure of maintaining a balance between work and family. This leaves little time to enjoy the game of cricket which seems to go on till eternity (in a test match) and very long as far as one day international is concerned. The second target segment was the younger generation who was more hooked to short and exciting sports like football and basketball which were made popular because of the different foreign sports channels in India. The games had huge popular stars and a lot of glamour in the form of cheerleaders and music in breaks. The challenge for cricket administrator was to break out of the stereotype and deliver something that is sync with the modern day demand.

Step 2 - bring drivers together into a viable framework
The next step was to bring all these factors or drivers in a relevant framework and rearrange them to find some meaning hidden in them.
I think in this step the use of managerial intuition helps a big role in developing the relationship between the various complex patterns. The other aspect to be seen in this stage is the experiences that one can borrow from other sports in other countries and try to inculcate some of the reasonable attributes in the scenarios.
Like in IPL a lot of features have been borrowed from the football leagues and the National basketball Association of U.S. For eg. Getting overseas players to play for domestic teams like that of latin American and European players playing in English premiership league and the concept of auctioning players that has been seen in most of the sports in the US, the cheerleading from basketball and American football.

Step 3 - produce initial (seven to nine) mini-scenarios
The outcome of the previous stage was to develop seven to nine groupings of drivers. Having formed these groups the next step is to find relationship between these factors.

For eg we can form different scenarios for cricket like
Half day game involving only foreign players.
One day series between Indian players and foreign players (with each side playing for 25 overs)
A tournament featuring player from different countries(in the form of champions trophy)
Etc

Step 4 reduce to two or three scenarios

The main action in this stage is to bring the number of scenarios to 2 or 3. The Challenge is to fit in the variables sensibly in these possible 2 or 3 scenarios. This usually requires a lot of brainstorming. The process can provide very useful insight into the issues affecting the organization i.e. BCCI (in this case).The purpose of developing fewer scenarios is to focusing properly and thinking deeply about a few scenarios.
In the context of 20-20 cricket the 2 or 3 scenarios could have been
A fit between domestic and international cricket where foreign players participate. The duration is minimized to half a day of cricket(25 overs each)
A international tournament in the lines of champions trophy where different countries with their respective teams participate. The game duration can be kept at 15 overs each.

Step 5 writing the scenarios

The next step is to write the scenarios. The idea is to produce the scenarios in the form most suitable for use by the managers who are going to base their strategy on them. Like the operational, marketing and the financial issues.In the 20-20 case I can be issues like the availablity of the players and the stadiums, the kind of revenues that can be generated, the time frame of the entire tournament, the fixtures of the game etc.

Step 6 identify issues arising

The stage is to determine the most critical outcomes, the issues that has the greatest impact on the future of the organization. The subsequent strategy is to address these issues by developing robust strategies rather than aiming for profit maximization.
The 20-20 format has been able to develop these robust strategies like keeping the format of the game as competitive as possible without changing the fundamentals rules of cricket. This strategy has helped it in retaining its ardent supporters while at the same time attracting newer audiences. The fun and glamour has been injected to the system without loosing the seriousness of the game. It has also converted attending the 20-20 game into a family event like going to the movies, whether you watch at home or in the stadium.

References
http://en.wikipedia.org/wiki/Scenario_planning