Synopsis

The paper Matching of technological forecasting technique to a technology by Mishra, S., Vrat, P. & Deshmukh, S. G. looks at the methods used in order to pick an appropriate technique for performing technology forecasting within a given area and identifying which forecasting profile most closely matches the technology type is trying to be forecast in order to improve the forecasting accuracy.The article starts with a description of the current environment in which technology forecasting exists and presenting a few of the challenges associated with the current state of technology forecasting. It then proceeds to outline the environment in which this paper was written to set the context and boundaries of the paper.The paper then goes through a brief history of technology forecasting methods, paying particular attention to the different methods under the headings of subjective assessment methods, exploratory methods of technology forecasting and normative approaches to technology forecasting as well as introducing additional papers that have been written to help identify and choose appropriate forecasting methods from those available, it then goes on to identify the weaknesses of those methods in order to better determine what the limitations of them are and where they can be best utilized, it does this with a focus on main battle tank technology as it’s basis to provide structure to the methods they ultimately choose which includes breaking down each component and treating them differently in terms of forecasting the technology.The paper then goes through the utilization of these methods to show how the data can be captured and then interprets it in order to provide some action, it proceeds to match these forecasting methods to particular technologies by introducing experts in technology forecasting to use the characteristics of each method and determine which technology artefact it would be best suited to. Finally, it analyses the output to determine if the methods chosen were the best ones and how they could be determined to be the best in terms of accuracy and reliability of the forecast, it does this by providing a robust series of steps to follow which can be used to determine mathematically the fitness of the methods to a technology profile and then picking those with the least deviation from it to reduce the uncertainty around method selection. It finishes by summing up the original premise, the method they followed and a brief synopsis of the findings and some information on validation of the results and ways to determine confidence in the results obtained from the forecasting methods.

Evaluation

This paper starts with an overview of technology forecasting; it correctly points out the that in modern times an organisation needs to continually update its technology forecasts in order to survive the constant change

organisations face and enable them to have first mover advantage however it doesn’t mention at all that the first mover doesn’t always gain the market share and in many cases a late entrant to the market can gain significant share and profit by doing things differently for example the apple iPod which was a late entrant to the music player market but ended up dominating the marketplace for music players (Abel, 2008).

While looking at research performed previously to determine where technology forecasting was taking place it identified the US, Japan and Europe as the main centres of technological forecasting and while this may still be true it relies on a paper created 9 years prior to this one being written to substantiate the claim, this paper by its own admission when looking for forecasters outside the United States “we wrote to close to 200 fellow members of the World Future Studies Federation, which is the most broadly based futures society in the world. Its membership is carefully self-selected so that everyone is a legitimate futurist. From that total inquiry we got 3 responses” (Coates, et al., 1994, p. 28), the paper takes this at face value without questioning why Coates et al. did not try alternative methods to find out if forecasting was done in these other countries or there genuinely was little to no forecasting happening.

This paper did have a thorough overview of technology forecasting methods and had a large number of references covering 31 different technology forecasting methodologies, it does however concentrate more heavily on the Delphi model (Pankratova & Malafeev, 2012) than the other methodologies, especially as it was used for multiple technologies during the later parts of the paper, this could be because it was the best fit or because of some bias of the authors towards a well understood method.

The authors provide a table (Mishra, et al., 2002, p. 5) listing combinations of the following data headings “extent of data validity”, “degree of data validity”, “number of variables affecting technology development”, “degree of similarity between proposed and existing technologies” and “forecasting methods” which are the methods to be used given the other fields in the table, however it does not indicate at any point what small, medium or high mean which leaves the selection of forecasting methods up to the interpretation of the reader, this is an oversight especially when later on in the paper they spend a large amount of time and space to show how to be specific when analysing the data returned to determine if the best fit method was chosen.

The authors do provide a very detailed diagram showing the “inadequacies of Technology Forecasting and Technology Tactics” (Mishra, et al., 2002, p. 6), this does break out large issues (such as Human related issues with forecasters) into their constituent parts and by the authors own admission they condensed all of the details they’ve found down to what they consider the “salient problem areas” (Mishra, et al., 2002, p. 5) the problem with this method is that the results will be highly dependent on the authors own point of view and what they consider not

noteworthy can in other circumstances become highly salient with another authors requirements involving different forecasting issues.

The questionnaire the authors created which while not exhaustive is very complete and should cover most of the topics required for any technology forecasting method, the only criticism would be in the weightings the assigned to each question, for example they stated “the innovation characteristics would score higher than number of options. Flexibility to change may be more important than ease of exploitation.” (Mishra, et al., 2002, p. 9) while they do go on to use their experts to verify the weightings no explanation is given as to why these weightings were chosen. While analysing the results the outliers from the standard deviation were discarded (Mishra, et al., 2002, p. 22) while this is best practice in terms of statistical analysis they are discarding the opinion of experts which do not agree with the mainstream, the dangers of this can be illustrated by Richard Rumelt in the book Strategy Safari (2009, p121) where anyone answering yes if for Honda entering the car market would have failed the question although this ultimately proved successful.

Overall this paper achieves its goal of showing how to match up a technology forecasting method with a particular technology type and provides a number of options for each type that can be used to reduce the uncertainty around the method chosen and also provides a list of questions to ask when forecasting technology that have been seen and approved by experts in the field, the paper provides a sound mathematical model which can be reused and repurposed to help other forecasting attempts in future, because this paper shows practical aspects on choosing a forecasting method as well as some of the issues with it and providing practical advice on the statistical analysis of the results it is highly relevant in a practical rather than theoretical sense with the results underlying theories and methods able to be used as a generic template for performing technology forecasting going forward.

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