A Delphi technology forecasting approach using a semi-Markov concept

This paper suggests a technology forecasting approach based on a semi-Markov model, which appropriately describes the probabilistic nature of a sequential technology development process. This approach focuses primarily on the utilization of the information that has been skipped in conventional Delphi survey data. That is, through a simple statistic, the interrelationships among sequential technology developments can be extracted in a formal structure of a semi-Markov model from the original Delphi panel's estimates. A simulation technique is developed to forecast the development process by utilizing the information on such interrelationships. This technique provides a flexible and useful tool for R&D planners or project managers, especially in postanalysis of Delphi forecasting. To make good use of the approach, a computer-based interactive Delphi data analysis system (IDEAS) is implemented in IBM PC.     

Generation dynamics in computer-integrated manufacturing systems

The application of generation dynamics, a methodology for technological forecasting, is widespread in computer-integrated manufacturing systems (CIMS). Experts' opinions about the past and future generations of technology are summarized for computer hardware, computer software, industrial robots, computer vision, computer-aided process planning, and the entire automated factory. The concept of generation dynamics has also been applied to products to be produced by CIMS and to the industrial environment of CIMS. Although experts in the same area do not completely agree with one another, their acceptance of experts' opinions on generation dynamics in other areas to be integrated by CIMS helps the forecasts to be mutually reinforcing and self-fulfilling. On the other hand, such relatively unquestioned acceptance, if and when experts' opinions in other areas become outdated, leads to assumption drag in forecasting.     

Innovation forecasting: A case study of the management of engineering and technology literature

The following paper contributes to the methodology of innovation forecasting. The paper analyzes the literature of engineering and technology management. A brief history and justification for interest in engineering and technology management is presented. The field has a sixty year history of interdisciplinary, and is therefore a ripe source for closer investigation into time trends of knowledge. The paper reviews the literature of innovation forecasting, examining a range of theoretical and methodological literatures interested in the evolution of knowledge. A new application of a model, suitable for sparse and count-like publication data, is presented. A mathematical presentation of the model is offered. A discussion is offered on how the model may be implemented in an approachable way within spreadsheet software. A time history of engineering management literature is extracted from a database and analyzed using the model. A projection of keyword growth is offered, and key features of the emerging knowledge base within engineering management are discussed. Recommendations for future research, as well as for those monitoring the status of the discipline of engineering management, are made.     

MEASUREMENT OF CAPITAL IN DENMARK

In December 1973, the so-called 2nd Perspective Plan was published by the Danish Ministry of Finance. It included some 5 and 15 year forecasts of investments in the private sector, based on the projected development of production and labour. The forecasts were made by use of a simple Cobb-Douglas production function, taking as capital-input the stock of buildings and machinery, using the perpetual inventory method (assuming sudden death).
Since the publication of these forecasts, an attempt has been made to refine the capital concept, measuring its services as factor input. Thus, it has been necessary to introduce an exogenous rate of interest. Inspired by Danish findings for private cars, depreciation functions for stocks and utility of machinery are developed. These functions may not seem very realistic for the heterogenous class of durables called machinery, but other possibilities appear even less convincing.
Together with an assumption of exponential decay for buildings, it is possible to produce alternative time-series for changes in input of capital in the production process. Some of the resulting estimates of parameters in the Cobb-Douglas function give a better fit than the original version. But no value of the elasticity of production of capital is firmly established, e.g. it is obviously dependant upon the period of estimation, and therefore of no great value in forecasting. No firm connection between labour productivity and capital input (in short as well as the long run) has so far been revealed in Denmark, so no measure of capital is yet of great use in forecasting, except when future growth in production resembles that of the past fairly closely.     

Assessing market penetration combining scenario analysis, Delphi, and the technological substitution model: The case of the OLED TV market

The technological substitution model, proposed in 1971, was designed to analyze the penetration process of new-generation technologies replacing old ones. It was expanded in 1979 to consider more than two generations and the substitution process among various entities in the market. However, this expanded model is often limited by a relative lack of data on the latest-generation technology.On the other hand, scenario analysis, as another breed to forecasting tool, provides rich and complex portraits of possible future development of technologies. However, it fails to provide quantifiable forecasts. Therefore, previous researchers combined these two methods to analyze the development of new technologies.Nevertheless, to make forecasting more holistic, the current opinions of seasoned experts should also be taken into account. However, because of the often-diverged expert opinions, a method for consensus building, such as the Delphi method, is also necessary. Therefore, we combined the scenario analysis with the Delphi method and the technological substitution model to analyze the development of a new technology, namely, the latest-generation television (TV), or the organic light-emitting diode (OLED) TV. We elaborate three possible scenarios, and forecast the market share of OLED TV and four other TV technologies in the global market over the next 10 years: cathode ray tube, rear projection, plasma display panel, and liquid crystal display.     

Technology forecasting for commodity projections: A case study on the effect of substitution by aluminum on the future demand for copper

This paper deals with the possible use of technology forecasting in commodity projection and, to a lesser extent, in resource allocation for research and development. As a specific example, technology forecasting was used to estimate the effect of substitution of copper by aluminum on the future demand of copper in 1980 and 1990. and to identify a future technology for extracting aluminum from clay; a technology which could be especially useful in the developing countries. Basic predictions were made for functional units of the major common end uses of copper and aluminum in 1980 and 1990, including electrical conductor and heat exchanger applications. The amounts of copper required in each application in 1980 and 1990 were estimated for three substitution scenarios. In the first of these, substitution is assumed to take place according to a logistic type function, resulting in a predicted copper demand of 3.70 million tons in 1980 and 4.91 million tons in 1990. In the second scenario, substitution is assumed to progress at past linear rates, resulting in a predicted copper demand of 4.3 million tons in 1980 and 6.41 million tons in 1990. In the third scenario, no further substitution is assumed, resulting in a predicted copper demand of 4.81 million tons in 1980 and 7.77 million tons in 1990. The effective date of the technology monitoring that produced these predictions is July 1974, and the predictions are current as of the date. The then current forecasts for copper demand made by the U.S. Bureau of Mines and other agencies using compound growth and regression models, corresponded to the unlikely scenario of no further substitution, and therefore needed correction in view of these anticipated technological changes.     

Determinants for forecasting technological substitution

This paper investigates the factors determining the rate of technological substitution and evaluates the prospects for forecasting models based on market-share data only. Studies on underlying causes for the substitution of one technology or product by another reveal the frequent presence of a number of factors. These factors are first discussed in general terms so as to establish their causal relationships with the rate of substitution. On the basis of the identified cause-effect relationships, a composite model is formulated that incorporates the impact of individual factors in a combination of additive and multiplicative interactions. The relative importance and sensitivity of different factors in describing the behavior of the substitution process is then studied through a system-dynamics application of the model. Finally, using three specific cases, a comparison is given between the forecasts made by the comprehensive model, incorporating factors that are known to have significant impact on the rate of substitution, with that of a generalized model for forecasting technological substitution.     

Medical technology decisions in The Netherlands: How to solve the dilemma of technology foresight versus market research?

In this article, we discuss a dilemma consisting of the market-oriented perspective of users of medical technology versus the long-term technology foresight perspective. The context of medical technology is interesting, because we have to cope with complex future-oriented multi-level and multi-actor strategic decision making. In order to deal with this dilemma we suggest combining the results of a (group) expert opinion forecasting approach with a more market-oriented scenario-approach. More specifically, we use the results of the Delphi-technique as the main input for the development of various capacity (Market-based) scenarios. We exemplify this approach by a real life example directed at the future of imaging techniques for cancer care in The Netherlands and focus on a set of scenarios that deal with the application of the MRI-technique in the period 2005–2015. The Delphi-panel's expectations with respect to imaging technology representing the technological forecasts, combined with other relevant developments (such as demographic and epidemiological developments) are translated into alternative inputs for assumptions of the scenario-model. This model is basic to the future projections in terms of needed MRI-scanners, manpower and investments. We argue that the results provide motivation to continue to explore the methodological interesting area of innovation, aligning the market-oriented perspective of users of (medical) technology with the long-term technology forecasting perspective.     

Forecasting technology costs via the experience curve — Myth or magic?

To further understand the effectiveness of experience curves to forecast technology costs, a statistical analysis using historical data is carried out. Three hypotheses are tested using available datasets that together shed light on the historical ability of experience curves to forecast technology costs. The results indicate that the Single Factor Experience Curve is a useful forecasting model when errors are viewed in their log format. Practitioners should note that due to the convexity of the log curve a mean overestimation of potential cost reductions can arise as values are converted into monetary units. Time is also tested as an explanatory variable, however forecasts made with endogenous learning based on cumulative capacity as used in traditional experience curves are shown to be vastly superior. Furthermore the effectiveness of increasing weights for more recent data is tested using Weighted Least Squares with exponentially increasing weights. This results in forecasts that are less biased, though have increased spread when compared to Ordinary Least Squares.     

A review of selected recent advances in technological forecasting

During the past decade, there have been some significant developments in technological forecasting methodology. This paper describes developments in environmental scanning, models, scenarios, Delphi, extrapolation, probabilistic forecasts, technology measurement and some chaos-like behavior in technological data. Some of these developments are refinements of earlier methodology, such as using computerized data mining (DM) for environmental scanning, which extends the power of earlier methods. Other methodology developments, such as the use of cellular automata and object-oriented simulation, represent new approaches to basic forecasting methods. Probabilistic forecasts were developed only within the past decade, but now appear ready for practical use. Other developments include the wide use of some methods, such as the massive national Delphi studies carried out in Japan, Korea, Germany and India. Other new developments include empirical tests of various trend extrapolation methods, to assist the forecaster in selecting the appropriate trend model for a specific case. Each of these developments is discussed in detail.     

Combined forecast process: Combining scenario analysis with the technological substitution model

Forecasts can be improved by combining separate forecasts obtained by different methods. The complementary nature of the scenario analysis and technological substitution models means that combining the two can obtain improved forecasts. The former has the strength of dealing with the uncertain future, while the later offers data-based forecasts of quantifiable parameters. This study thus proposes a process for combining the scenario analysis with the technological substitution model for discussing new generation technological developments. The proposed process not only has the strengths of scenario analysis, but also contains features that scenario analysis lacks, including predicting annual developments for future years, considering old technology development, and forecasting substitution of old technologies. This study uses the forecast of the market share of Fiber to the x in Taiwan over the next ten years as an example illustrating the proposed combined forecast process.     

Trend extrapolation in long-term forecasting: An investigation using New Zealand electricity consumption data

The potential of using simple trend extrapolation curves for forecasting electricity consumption is investigated with reference to New Zealand sectoral data. Fitting of logistic- and nonlogistic-based trend curves to the historical consumption data has resulted in the choice of the most viable model, the logistic model. This leads to further investigation using a Fibonacci search technique to establish the optimal asymptotes for the logistic curves. Very stable results are obtained, indicating that current electricity consumptions are approaching saturation. Forecasting precision and accuracy are discussed, and this leads to the calculation of low, medium, and high estimates of the 5-, 10-, and 15-year forecasts.     

Assimilation of aggregated inputs into Delphi forecasts: A regression analysis

To examine the manner in which the individual assessments of a panel of delphi experts are combined into a delphi forecast, the supporting reasons they gave for their forecasts of 40 computer applications were coded into categories of assessed “technical feasibility,” “cost of initiating,” and “benefits or needs provided.” Even though different sets of experts provided these statements in support of their individual forecasts, with some experts emphasizing one aspect and others another, it was found that the median forecast of the entire panel was significantly related to the average proportion of reasons in each category which favored bringing about the development. That is, the delphi forecasts of computer applications suggest that the computer application is forecasted to occur sooner to the extent it was judged to be technically feasible, beneficial to users or society, and not costly to develop. The results indicate further that delphi forecasting among a group of experts has logical validity, and that individual contributions are integrated into a group outcome.     

Forecasting technology diffusion: a new generalisation of the logistic model

The present research proposes a new generalisation of the logistic model aiming at technology diffusion forecasting. Regarding criticisms and failures reported in the literature to apply logistic function for long-term forecasting, in our work we focused on short-term accuracy of forecast. To formulate the model, based on mathematical approximation, at first the differential equation governing the diffusion process is found and then by solving the derived differential equation, the forecast function is obtained. In all steps, mathematical tools from numerical analysis are used. We compared the New Generalized Logistic Model with eight of the most renowned models in the literature. The model led to more accurate fits and forecasts than those obtained from other models we applied for comparison.     

A model for forecasting the substitution of one technology for another

Most technological changes can be described as a substitution of one material, process or product for another. Each such substitution, if successful, normally tends to follow an S-shaped (or “logistic”) curve: that is, it starts slowly as initial problems and resistances have to be overcome; then it proceeds more rapidly as the competition between the new and the old technology grows keener and the new technology gains an advantage; and finally, as the market for the new technology approaches saturation, the pace of substitution slows down. Sometimes, when the process is completed, the old technology continues to retain some specialized portion of the total market (i.e., a sub-market) for which it is particularly well adapted. In forecasting the course and speed of the substitution process especially when it has already begun and partially taken place, the simplest approach is to project a function having the appropriate S-shaped curve, using historical data to determine the free parameters of the function. While useful, especially where data are not available for a more sophisticated study, the simple curve-fitting techniques fail to take into account several important factors that affect economic and management decisions on the part of producers and intermediate users (as well as “final” consumers) and thereby influence the course which the substitution process is likely to take. To overcome this limitation, a simulation model has been developed at IR&T which allows some of these factors to be evaluated and incorporated explicitly and quantitatively. The model is described and its application is illustrated in the case of the substitution of plastic for glass in bottles. It is most applicable where the competing technologies are rather precisely defined, where a good deal of current technical and economic data are available, and where an in-depth analysis is desired. Because this particular forecast was made before the sudden precipitous increase in petroleum prices, which upsets the price relationships assumed in the forecast, there is discussion of the vulnerability of forecasts to political and other contra-economic developments.     

An international comparison of analysis' earnings forecast accuracy

A major task of financial analysts working for stockbrokers and investment firms is to forecast future earnings of listed companies. The usefulness of their work crucially depends on the accuracy of the forecasts. A great many studies have examined the accuracy, bias, and other characteristics of profit forecasts made in the U.S. In contrast, however, there is very little research on forecasting accuracy in other countries despite the increasingly global nature of investing. This paper examines the accuracy of corporate earnings forecasts in 34 different countries. In addition, a model is developed that seeks to explain differences across companies and countries. The findings show that eight countries have better forecast accuracy than the U.S. This cross-sectional model shows that with the inherent difficulty in forecasting for a specific company (proxied by the change in its earnings), risk and the number of analysts following the stock are the major factors in explaining earnings forecast accuracy.     

Modeling as a communication process: Computer conferencing offers new perspectives

Model building is currently one of the most expensive and difficult applications of computer technology. It is expensive both as a conceptual activity, because building a model requires an abstract understanding of a complex process, and as a computational activity, because it demands massive amounts of data and many operations. Furthermore, under conventional approaches one cannot start using the full power of the computer in modeling until all the components have been identified and all the relationships are established. This article introduces an alternative approach in which the computer is used during the earliest stages of model construction. This approach is actually an extension of current technology for computer-based teleconferencing. It incorporates nonhuman “participants” – modules, data bases, or parts of programs-in the modeling process. The advantages of this approach include (1) the opportunity for human participants to test alternative model structures and (2) the capability of the computer to monitor and feed information about the process back to the model director.     

The Performance of Exchange Rate Forecasts

Since the floating of the Australian dollar the forecasting of exchange rate movements has become more difficult and received much more attention. As a result, some participants in the foreign exchange market have, on a number of occasions, come under criticism for their inability to predict exchange rate movements. This article seeks to evaluate these criticisms through an examination of exchange rate forecasts made by market participants (as published in the Australian Financial Review from March 1985 to December 1985). The accuracy of the $A/US$ forecasts is compared with that of forecasts generated from a number of simple forecasting rules as well as forecasts of the US$/Yen exchange rate. In general, the simple forecasting rules provide superior forecasts to those provided by the individual market participants. However, under some criteria, the mean of the individual participants' forecasts may be preferred to these simple forecasting rules. Further, the comparison of the US$/Yen forecasts with the $A/US$ forecasts shows the former to be generally more accurate.